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Trauma diagnostic-related target proteins and their detection techniques

Published online by Cambridge University Press:  11 April 2024

YiLiu Wei
Affiliation:
Department of Trauma Center & Emergency Surgery, The First Affiliated Hospital of Fujian Medical University, 350004 Fuzhou, China Department of Trauma Center and Emergency Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350004 Fuzhou, China
Xiaohan Ren
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Zhitao Yuan
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Jie Hong
Affiliation:
Department of Trauma Center & Emergency Surgery, The First Affiliated Hospital of Fujian Medical University, 350004 Fuzhou, China Department of Trauma Center and Emergency Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350004 Fuzhou, China
Tao Wang
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Weizhi Chen
Affiliation:
Department of Trauma Center & Emergency Surgery, The First Affiliated Hospital of Fujian Medical University, 350004 Fuzhou, China Department of Trauma Center and Emergency Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350004 Fuzhou, China
Yuqing Xu
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Jinwang Ding
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Jun Lin
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Wenqian Jiang*
Affiliation:
Institute of Applied Genomics, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China College of Biological Science and Engineering, Fuzhou University, No. 2 Xueyuan Road, 350108 Fuzhou, China
Peng Zhang*
Affiliation:
Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127 Shanghai, China
Qiaoyi Wu*
Affiliation:
Department of Trauma Center & Emergency Surgery, The First Affiliated Hospital of Fujian Medical University, 350004 Fuzhou, China Department of Trauma Center and Emergency Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350004 Fuzhou, China
*
Corresponding authors: Wenqian Jiang; Email: YP2103@fzu.edu.cn; Peng Zhang; Email: pengzhang016@sjtu.edu.cn; Qiaoyi Wu; Email: qywu@mail.fjmu.edu.cn
Corresponding authors: Wenqian Jiang; Email: YP2103@fzu.edu.cn; Peng Zhang; Email: pengzhang016@sjtu.edu.cn; Qiaoyi Wu; Email: qywu@mail.fjmu.edu.cn
Corresponding authors: Wenqian Jiang; Email: YP2103@fzu.edu.cn; Peng Zhang; Email: pengzhang016@sjtu.edu.cn; Qiaoyi Wu; Email: qywu@mail.fjmu.edu.cn
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Abstract

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Trauma is a major health issue. According to estimates from the Global Burden of Disease Study, trauma is responsible for around 10% of global deaths (Ref. Reference Murray and Lopez1). Significant progress has been made in managing trauma patients because of the continuous advancement of medical technology. Rational and effective treatment and care protocols have significantly improved the survival rates of trauma patients (Ref. Reference Malekifar2). Nonetheless, trauma patients still face a significant threat to their survival because of a range of complications. Multiple studies have reported potential complications that trauma patients may encounter, as shown in Figure 1, including psychological trauma-related disorders (Ref. Reference Messman-Moore and Cook3), as well as various physiological complications such as infection-induced sepsis (Refs Reference Heming4, Reference Lakomkin5), thrombosis or haemorrhage (Refs Reference Villarreal, Shibuya and Jupiter6, Reference Wu7), ischaemic tissue necrosis (Ref. Reference Pradhan8), acute respiratory distress syndrome (Ref. Reference Ramin9), traumatic brain injury (TBI) (Ref. Reference Miller10), among others. Trauma patients who are admitted to the intensive care unit (ICU) often face an increased risk of complications, which contributes to a substantial mortality rate. Statistics indicate that among trauma patients who die several days or weeks after the incident, 45% of deaths can be attributed to severe brain injury, adult respiratory distress syndrome (ARDS) or multiple organ dysfunction syndrome (Ref. Reference Baker11).

Figure 1. Classification of complications of trauma and methods for identifying their target proteins.

Timely detection of newly emerging infections and complications in trauma patients is crucial as it enables physicians to initiate timely and appropriate treatment measures, thereby reducing mortality rates and improving patient prognosis after trauma. Severe trauma can worsen systemic inflammation in patients, often leading to sepsis and life-threatening consequences. Traditionally, monitoring severely injured trauma patients has primarily relied on assessing the extent of injury. However, by employing target protein identification of characteristic biomarkers in trauma, precise assessment, monitoring and prediction of trauma outcomes can be achieved (Ref. Reference Hopf12). Therefore, target protein identification techniques can be vital tools in clinical diagnostics, particularly for early identification of biomarkers for various trauma complications. Additionally, they can offer comprehensive protein information to explore the pathogenesis and progression of related diseases, uncover intricate cellular metabolic pathways and signalling networks and identify key factors and associated proteins involved in disease development. This enhances our understanding of diseases and facilitates further research, drug design and the development of treatment strategies (Refs Reference Danazumi13, Reference Zhu14). In accordance with the illustration provided in Figure 1, some of the target protein identification techniques currently being developed include mass spectrometry (MS), polyacrylamide gel electrophoresis (PAGE) and immunological methods. By selectively utilizing these techniques based on specific needs, it is possible to identify and validate target proteins associated with the occurrence or prognosis of trauma-related complications. This enables timely diagnosis and improves the post-trauma survival rate of patients. In order to contribute to the reduction of post-trauma mortality rates and enhance diagnostic efficiency and prognostic outcomes for patients, the aim of this review is to comprehensively gather and evaluate promising biomarkers along with the methods employed for their detection. By consolidating and analysing this information, it can be applied to effectively improve the survival rate post-trauma. In the following sections, we will explore the various consequences of trauma and their associated biomarkers, where we will delve into the biomarkers that play a crucial role in its diagnosis and prognosis. Afterwards, we will also review the detection methods related to complications following trauma.

Post-traumatic sepsis

In 1991, the American College of Chest Physicians and the Society of Critical Care Medicine jointly released the first version of international consensus defining sepsis during the consensus conference. According to the conference, sepsis is described as a progressive continuum of inflammatory deterioration, starting from systemic inflammatory response syndrome triggered by infection and advancing to sepsis, severe sepsis and septic shock. Nevertheless, this definition had notable limitations as it failed to consider specific biomarkers that commonly elevate during sepsis (Ref. Reference Mayr, Yende and Angus15). The third edition of the international consensus definition for sepsis and septic shock introduced a more precise definition and advocated the use of the quick sequential organ failure assessment as an efficient screening tool for sepsis to identify patients with suspected infections and unfavourable prognoses (Ref. Reference Singer16). Sepsis can result in diverse organ damages, encompassing sepsis-associated encephalopathy, septic cardiomyopathy, sepsis-associated acute kidney injury, sepsis-associated acute lung injury and sepsis-induced liver injury (Ref. Reference Huo17). Without a doubt, early identification of the infection source and initiation of antibiotic therapy in a timely manner are vital measures to decrease mortality rates in sepsis, septic shock and trauma patients. Timely identification of the infection source enables healthcare professionals to expeditiously implement suitable treatment measures, including initiating appropriate antibiotic therapy, effectively controlling infection spread and preventing the emergence of severe complications.

Presently, extensive research has demonstrated the significant diagnostic, therapeutic and prognostic roles of sepsis biomarkers, which exhibit a direct correlation with the severity of the injury (Ref. Reference van Engelen18). Biomarkers associated with sepsis include cytokines, tumour necrosis factor-alpha (TNF-α), markers of cellular pyroptosis, high-density lipoprotein (HDL), procalcitonin (PCT) and C-reactive protein (CRP), among others. Furthermore, each of these biomarkers will be sequentially introduced in the subsequent sections. And the summarized information is provided in Table 1.

Table 1. Biomarkers of post-traumatic sepsis and their biological functions

Cytokines

Cytokines, which are small proteins secreted by both immune and non-immune cells, exhibit diverse biological activities and play a pivotal role in immune response (Ref. Reference Calzavacca19). Studies have demonstrated the diagnostic potential of interleukin-6 (IL-6) for early-onset neonatal sepsis (Ref. Reference Eichberger and Resch20). IL-6 is a multifunctional cytokine involved in immune response, tissue regeneration and metabolism. Rapid production of IL-6 contributes to host defence during infection and tissue damage, whereas excessive synthesis of IL-6 and dysregulation of IL-6 receptor signalling are associated with pathological conditions (Ref. Reference Kang21). Thus, IL-6 serves as a reliable biomarker for inflammatory diseases. Elevated serum IL-6 levels not only indicate sepsis in patients, but also reflect the severity of sepsis with significant and rapid changes (Ref. Reference Kobayashi22). Moreover, research conducted by Chen et al. suggests that the elevation of IL-6 levels worsens sepsis and leads to myocardial injury (Ref. Reference Chen23).

Tumour necrosis factor-alpha

TNF-α is a polypeptide polymer that exhibits diverse biological effects. TNF-α can activate T cells and stimulate the production of pro-inflammatory mediators such as IL-1 and IL-6, ultimately leading to the development of a cytokine storm (Ref. Reference Ling24). A study on the influence of cytokine levels on immune status and survival outcomes in elderly patients with sepsis indicated that, it was found that TNF-α levels in the serum of patients in both the mild and severe sepsis groups were significantly higher than those in the control group (P < 0.05). Additionally, TNF-α levels were significantly higher in the severe sepsis group compared with the mild sepsis group (P < 0.05) (Ref. Reference Li and Yan25). This suggests a direct correlation between TNF-α levels and the condition as well as the prognosis of sepsis patients.

Markers of cellular pyroptosis

Cell pyroptosis is a form of programmed cell death that is triggered by pro-inflammatory mediators (Ref. Reference Yu26). According to the study by Wang et al., trauma patients who develop sepsis frequently exhibit a higher proportion of peripheral blood mononuclear cells (PBMCs) undergoing pyroptosis compared with those who do not develop sepsis (Ref. Reference Wang27). During the early stages of trauma, there is a notable rise in the proportion of PBMCs undergoing pyroptosis, which exhibits a significant correlation with the severity and inflammatory status of trauma patients. Cell pyroptosis is primarily mediated by the inflammatory enzyme caspase-1 and triggers a systemic inflammatory response in the host. Therefore, elevated expression of caspase-1 frequently signifies the onset of sepsis, and PBMC pyroptosis emerges as a potential biomarker for predicting sepsis development following trauma.

High-density lipoprotein

HDL is a complex family of lipoprotein particles consisting of diverse biomolecules, such as proteins and lipids. HDL particles demonstrate protective functions against atherosclerosis in various cell types. These functions encompass promoting cholesterol efflux from macrophages within the arterial wall, exerting antioxidant activity, demonstrating anti-inflammatory effects and safeguarding the vascular endothelium (Ref. Reference Darabi and Kontush28). According to the study by Tanaka et al., lipid profiles of sepsis patients in the ICU significantly differ from those of general trauma patients. Sepsis patients exhibit lower levels of HDL, which possesses cardiovascular protective effects, whereas the HDL concentration in trauma patients remains unaltered (Ref. Reference Tanaka29).

Procalcitonin and C-reactive protein

PCT and CRP have been identified in numerous studies as diagnostic and prognostic biomarkers for sepsis (Refs Reference Hoshino30, Reference Magrini31, Reference Yang32). PCT is a precursor protein synthesized by thyroid C cells (parafollicular cells). Under pathological conditions such as inflammation and infection, it undergoes further processing to form calcitonin. Therefore, it has been proposed as a specific biomarker for bacterial infection and has subsequently been applied in clinical practice (Ref. Reference BalcI33). CRP is a well-known acute-phase reactant, initially discovered in the 1980s and named for its capability to bind to pneumococcal C-polysaccharide. CRP plays a role in the apoptotic process by facilitating the binding of damaged and disrupted cells, and contributes to the clearance of debris generated from tissue damage (Ref. Reference Feigin34). Yang et al. suggested that detecting the expression levels of neutrophil CD64 and PCT can provide a more sensitive diagnosis of sepsis compared with CRP (Ref. Reference Yang32).

In addition to diagnosing and predicting sepsis through biomarkers, Daigo et al. employed effective immunopurification methods to identify the proteomic characteristics of the sepsis pentraxin 3 (PTX3) complex and its interactions with neutrophil extracellular traps. PTX3 is a conserved glycoprotein (GP) that is secreted by various cells such as endothelial cells, myeloid cells, fibroblasts and osteogenic cells in response to inflammatory stimuli, and it plays a crucial role in regulating immune responses and inflammatory processes (Ref. Reference Dong35). Their research suggests that PTX3 may help create an antimicrobial microenvironment in sepsis through its interaction with antimicrobial proteins (Ref. Reference Daigo36). Similar studies indicate that target protein identification techniques play a crucial role in enhancing our understanding of sepsis pathogenesis.

Trauma-related acute respiratory distress syndrome

ARDS is a severe inflammatory lung condition characterized by severe respiratory failure. It is characterized by persistent refractory hypoxaemia and the presence of diffuse opacities on chest radiographs (Ref. Reference Rezoagli, Fumagalli and Bellani37). A multinational prospective study conducted in 459 ICUs across 50 countries/regions on five continents revealed regional variations in the mortality rate of ARDS, but the overall mortality rate remains very high at approximately 40% (Ref. Reference Bellani38). Currently, treatment options for ARDS are limited to providing supportive measures aimed at preventing further lung damage. For patients who do not respond well to these measures, the mortality rate is even higher, reaching 60% (Ref. Reference Kasotakis39). ARDS, a severe lung condition, can be caused by various factors such as sepsis, trauma, smoking and blood transfusions. Its severity and complexity pose challenges in treatment and management (Refs Reference Moazed40, Reference Rubenfeld41).

Many biomarkers have been tested for the diagnosis and treatment of ARDS (Ref. Reference Walter, Wilson and Ware42). However, ARDS induced by trauma exhibits clinical and biological differences compared with ARDS resulting from other clinical conditions (Ref. Reference Calfee43). Biomarkers for ARDS can be categorized as endothelial cell injury biomarkers (e.g. plasma angiopoietin-2 (Ang-2) (Ref. Reference Agrawal44), von Willebrand factor (vWF) (Ref. Reference Barnett and Ware45), vascular endothelial growth factor (VEGF) (Ref. Reference Medford46)), epithelial injury biomarkers (e.g. surfactant protein-D (SP-D)) (Ref. Reference Ware47), soluble receptor for advanced glycation end products (sRAGE) (Ref. Reference Uchida48), Clara cell protein 16 (CC-16) (Ref. Reference Koczula and Gallotta49)), inflammatory cytokines (e.g. various interleukins (ILs) (Ref. Reference Fremont50), mitochondrial DNA (Ref. Reference Faust51)) and other biomarkers (Ref. Reference Walter, Wilson and Ware42). Similarly, they are consolidated and summarized in Table 2.

Table 2. Biomarkers of trauma-related ARDS and their biological functions

Endothelial cell injury biomarkers

Ang-2, as an endothelial cell growth factor, plays a crucial role in regulating vascular permeability and has been identified as a key mediator in acute lung injury (ALI) in animal models (Ref. Reference Agrawal44). As a biomarker, Ang-2 can distinguish trauma-related ARDS from general ALI (Ref. Reference Fremont50). Furthermore, studies have demonstrated that a higher ratio of Ang-2 to angiopoietin-1 (Ang-1), with Ang-1 acting as an antagonist of Ang-2, is an independent predictor of mortality in patients with lung injury (Ref. Reference Ong52). vWF is a GP involved in haemostasis and is found in endothelial cells (Ref. Reference Barnett and Ware45). Research by Moss et al. suggests that vWF levels are associated with the aetiology of ARDS in patients, allowing for the identification of trauma-related ARDS based on vWF levels (Ref. Reference Moss53). VEGF is a significant cytokine that promotes angiogenesis in the body (Ref. Reference Leung54). Medford et al. demonstrated that genetic polymorphisms in the VEGF pathway are associated with an increased risk of developing ARDS in ventilated patients (Ref. Reference Medford46).

Epithelial injury biomarkers

SP-D is a pulmonary surfactant protein primarily located in alveoli and respiratory epithelial cells. It plays a central role in regulating lung host defence and modulating allergic responses (Ref. Reference Hartl and Griese55). The receptor for advanced glycation end products (RAGE) and its soluble form (collectively known as sRAGE) are believed to be involved in host defence against infection, inflammation, cardiac metabolic disorders and age-related diseases (Ref. Reference Erusalimsky56). Research by Uchida et al. demonstrated that RAGE is an effective biomarker for ARDS, and the levels of RAGE in the serum vary depending on the severity of lung injury (Ref. Reference Uchida48). CC-16 is a secretory protein primarily located in the terminal bronchiolar Clara cells along the bronchial tree. Increasing evidence suggests that CC-16 plays a crucial protective role against oxidative stress and inflammatory in the respiratory tract (Ref. Reference Broeckaert57). Research by Ware et al. confirmed that three biomarkers produced by lung epithelium (SP-D, RAGE and CC-16) can have good discriminatory power for the potential diagnosis of ARDS in septic patients (Ref. Reference Ware47).

Inflammatory cytokines

ILs are a class of protein signalling molecules produced by immune system cells, and they exhibit diverse biological activities and regulatory functions in immune responses. Notably, plasma biomarkers such as IL-6 and IL-8 have been confirmed to be valuable in diagnosing traumatic lung injury (Ref. Reference Fremont50). Furthermore, Faust et al. demonstrated a significant association between plasma mitochondrial DNA levels and ARDS in trauma patients (Ref. Reference Faust51).

Other biomarkers

Ang-2, CC-16 and mitochondrial DNA have been demonstrated to possess diagnostic or prognostic value in trauma-related ARDS (Refs Reference Fremont50, Reference Xu and Song58). However, there are still many biomarkers that have not been reported yet, which can aid in the diagnosis of ARDS. The development of genomics, metabolomics and proteomics has led to the identification of an increasing number of differentially expressed genes, metabolites and proteins specific to ARDS. Dong et al. utilized proteomic analysis to identify insulin-like growth factor-binding protein 7 (IGFBP7) as a novel pathogenic protein in ARDS. IGFBP7 is a protein that binds to insulin-like growth factors (IGFs), thereby modulating their activity and biological effects within cells. The team found that plasma IGFBP7 is associated with the 28-day mortality rate in ARDS, providing a foundation for further experimental and clinical research on the mechanisms and utility of IGFBP7 as a biomarker for this disease (Ref. Reference Dong59). Additionally, Rademaker et al. proposed a targeted proteomics analysis method to investigate the thrombotic pathways in ARDS associated with COVID-19 and influenza. This study demonstrated the significance of the technique in elucidating the pathological mechanisms of ARDS (Ref. Reference Rademaker60).

Traumatic brain injury

TBI is a brain injury caused by external mechanical forces, affecting millions of people worldwide. The causes of TBI are diverse and include sports-related injuries, road traffic accidents, falls, assaults and other factors (Ref. Reference Agoston, Shutes-David and Peskind61). Recently, falls have surpassed road traffic accidents as the primary cause of TBI (Ref. Reference Roozenbeek, Maas and Menon62). The threat posed by TBI should not be underestimated. On the one hand, there is currently no cure for TBI, and drug development for this condition has encountered frequent failures in clinical trials. On the other, accurately determining injury phenotypes in cases of mild damage is a challenging task (Ref. Reference Azar63). A study by Thornhill et al. showed that the majority (78%) of patients with severe TBI experienced disabilities. Similarly, the disability rate was 54% in moderate TBI patients and 51% in mild TBI patients (Ref. Reference Thornhill64). Early diagnosis of TBI commonly involves a combination of imaging techniques and clinical studies, whereas biomarkers play a crucial role in identifying biological processes that are not directly observable through imaging techniques. Biomarkers can provide information about ongoing tissue damage and the risk of neurological deterioration, making them valuable supplementary diagnostic tools. Therefore, diagnosing different severity levels of TBI and predicting and improving outcomes through biomarker identification hold significant value. The detection of novel biomarkers can offer insights into the underlying mechanisms of TBI, aid in early diagnosis, predict disease progression and contribute to the development of highly clinically significant molecular-targeted therapies.

Previous literature has reported several biomarkers associated with TBI. Typically, biomarkers for TBI are detected in blood or cerebrospinal fluid (CSF). Inflammatory cytokines such as TNF-α and interleukin-1 beta (Ref. Reference Gourin and Shackford65), similar to sepsis and ARDS, may suggest the presence of TBI. However, they lack specificity as reliable biomarkers for TBI.

The most extensively studied biomarkers in TBI research are those related to astrocyte injury, primarily S100 calcium-binding protein B (S100B) (Refs Reference Rainey66, Reference Vogelbaum67) and glial fibrillary acidic protein (GFAP) (Refs Reference Metting68, Reference Vos69). S100B is a calcium-binding protein secreted by astrocytes, Schwann cells, melanocytes, adipocytes and chondrocytes (Ref. Reference Heizmann, Fritz and Schäfer70). Haselmann et al. demonstrated that S100B is a reliable biomarker for TBI. They also found that plasma-based S100B detection is superior to serum-based detection, significantly reducing processing time (Ref. Reference Haselmann71). GFAP is a specific intermediate filament protein found in astrocytes and is upregulated during astrocyte proliferation. Research by Vos et al. has shown that both GFAP and S100B can be used to assess the severity of brain injury following TBI. When combined with clinical variables and subjected to comprehensive analysis, these biomarkers may help in more accurately predicting patient prognosis (Ref. Reference Vos69).

Besides the abovementioned biomarkers, there are other protein biomarkers associated with neuronal cell body injury, such as ubiquitin C-terminal hydrolase-L1 (UCH-L1) (Ref. Reference Papa72), neuronal-specific enolase (NSE) (Ref. Reference Meric73) and neuronal cell death, including alpha-II-spectrin (αII-spectrin) breakdown products (Ref. Reference Pineda74). UCH-L1 is a neuron-specific enzyme that is highly abundant in neurons. Papa et al. compared the levels of UCH-L1 in CSF between patients with severe TBI and an uninjured control group. They examined the relationship between UCH-L1 levels and the severity of brain injury, complications and functional outcomes, confirming that this biomarker can determine the severity of injury in TBI patients (Ref. Reference Papa72). The study by Brophy et al. confirmed the role of UCH-L1 in TBI diagnosis and investigated its kinetic profile in the biofluids of patients with severe TBI (Ref. Reference Brophy75). NSE has previously been confirmed as a biomarker for acute neuronal injury. Consequently, some studies have utilized its levels to assess the impact of organic solvent exposure on neurotoxicity (Ref. Reference Züngün76). Under normal circumstances, NSE levels in body fluids, such as serum and CSF, are minimal. However, during neuronal damage in the brain, NSE levels rapidly rise. Hence, NSE can be utilized for the diagnosis and prognosis of TBI (Ref. Reference Meric73). αII-spectrin is a key constituent of the cortical membrane cytoskeleton and acts as a primary substrate for calpain and caspase-3, both calcium-dependent proteases. Substantial evidence suggests that αII-spectrin undergoes proteolytic processing by calpain and caspase-3, leading to the generation of specific breakdown products. The study by Cardali and Maugeri further demonstrated that the degradation products of αII-spectrin serve as reliable biomarkers for severe TBI in humans (Ref. Reference Cardali and Maugeri77). Microtubule-associated proteins (MAPs) are essential constituents of the cellular cytoskeleton associated with microtubules. They play a crucial role in promoting microtubule assembly and maintaining stability. Specifically, MAP-2 is a neuronal protein predominantly found in dendrites and axons (Ref. Reference Drewes, Ebneth and Mandelkow78). Posmantur et al. conducted a study using protein blotting and qualitative immunohistochemistry to examine the acute alterations in cortical MAP-2 immunoreactivity in rats after TBI. The results confirmed a reduction in MAP-2 levels at the site of injury following TBI (Ref. Reference Posmantur79). Kitagawa et al. examined the distribution of MAP-2 in gerbil brains and confirmed its immunohistochemical reaction as a biomarker for monitoring cerebral ischaemic injury progression (Ref. Reference Kitagawa80). Alpha-synuclein (α-syn) is a 140-amino acid small protein, and there is abundant evidence supporting its involvement in neurodegenerative diseases, including Parkinson's disease (Ref. Reference Chen81). The clinical symptoms of long-term survivors of TBI often resemble the signs of brain dysfunction observed in neurodegenerative diseases, including Alzheimer's disease. This suggests that chronic TBI and related neurodegenerative diseases might share common neural circuits. Research by Ikonomovic et al. has demonstrated the presence of α-syn aggregates in the cortical tissue of severe TBI patients, providing additional support for the hypothesis that head trauma significantly elevates the risk of developing neurodegenerative diseases (Ref. Reference Ikonomovic82).

MS is an effective method for exploring biomarkers of TBI in injured tissues because of its high sensitivity and specificity (Ref. Reference Guingab-Cagmat83). Jenkins et al. conducted a study in mice, employing two-dimensional gel electrophoresis to investigate biomarkers of TBI (Ref. Reference Jenkins84). Recently, several studies have utilized target protein identification techniques to screen for novel biomarkers of TBI. Wang et al. employed tandem mass tagging (TMT) and ultra-high-performance liquid chromatography-tandem mass spectrometry in a study to identify and quantify differentially expressed proteins in a rat model of TBI. The study findings revealed that human kininogen-1, Protein Kinase A Regulatory Subunit 2A and myelin basic protein were potential biomarkers associated with the time of injury, suggesting their potential as effective therapeutic targets (Ref. Reference Wang85). Hinson et al.'s study demonstrated the feasibility of antibody-free proteomic detection of protein biomarkers in the plasma of TBI patients, in addition to the common antibody-mediated protein biomarker detection methods (Ref. Reference Hinson86). Label-free MS can reflect blood–brain barrier disruption in severely injured patients. Furthermore, using techniques such as western blot and immunocytochemistry, Zhang et al. confirmed that TBI patients exhibited elevated levels of caspase-8 mRNA and protein compared with the control group. Additionally, they observed the specific phenomenon of caspase-8 degradation into a 20-kDa fragment protein in TBI patients. TBI patients exhibited increased Fas levels compared with the control group, and this increase was correlated with the relative levels of caspase-8. Fas is a protein located on the cell membrane that belongs to the death receptor family and plays a crucial role in programmed cell death. These findings demonstrate the involvement of Fas and caspase-8 in the cascade of programmed cell death following human TBI, thereby revealing partial mechanisms underlying the progression of severe head and brain injury (Ref. Reference Zhang87). All the biomarkers mentioned in this section are consolidated in Table 3.

Table 3. Biomarkers of TBI and their biological functions

Post-traumatic thrombus

Blood circulation normally remains in a fluid state, and when a blood vessel is injured, the clotting system is triggered, leading to the formation of a haemostatic clot that adheres to the blood vessel wall without significantly impairing blood flow within the vessel (Ref. Reference Owens and Mackman88). Thrombus can be categorized based on location into arterial, venous and microvascular thrombosis, as well as disseminated intravascular coagulation (DIC). Arterial thrombosis occurs when platelets aggregate within an atherosclerotic plaque. Rupture of the plaque can result in local thrombosis and blockage of blood flow, potentially leading to myocardial infarction or stroke (Ref. Reference Mackman89). The process of arterial thrombosis is illustrated in Figure 2. Venous thrombosis typically develops in the major leg veins, with the clot predominantly composed of red blood cells and fibrin. Unlike arterial thrombosis, venous thrombosis primarily arises from alterations in blood composition, changes in blood flow and/or activation of endothelial cells (Refs Reference Eppihimer and Schaub90, Reference Mackman91). Capillaries are tiny blood vessels that are not visible to the naked eye without magnification or assistance (Ref. Reference Bray92). Microvascular thrombosis can contribute to small vessel disease, which is often associated with chronic hypertension and other risk factors for cardiovascular disease, and may result in microinfarctions in the brain and heart (Ref. Reference Senchenkova93). DIC is a condition characterized by widespread microvascular thrombosis. Sepsis and endotoxaemia are the primary pathological states associated with DIC. Additionally, DIC can occur as a consequence of severe trauma, such as surgery, which releases ‘tissue fragments’ into the bloodstream and triggers clotting activation (Ref. Reference Owens and Mackman88).

Figure 2. Process of arterial thrombosis – the thrombin cascade reaction. TF triggers coagulation by forming the FVIIa complex, which activates FX and FIX. FXI can activate FIXa. The prothrombin (PT) complex (FVa: FXa) catalyses the conversion of PT to thrombin. Thrombin then stimulates the activity of various proteases and cofactors. Moreover, thrombin cleaves fibrinogen into soluble monomers, which are subsequently cross-linked by FXIIIa. Activation of a protease-activated receptor on platelets leads to the formation of a blood clot.

Protein C, a vitamin K derivative, plays a crucial role in anticoagulation. Protein S, an important anticoagulant, acts as a non-enzymatic cofactor for activating protein C (Ref. Reference Juhl94). Protein C inactivates clotting factors Va and VIIIa, thus a deficiency in protein C results in the overexpression and activation of these factors, leading to thrombosis (Ref. Reference Mansour and Isaeva95). When platelets adhere to damaged blood vessel walls, they become activated, triggering the activation of platelet integrin GP IIb-IIIa (αIIbβ3), which binds fibrinogen and vWF to form platelet aggregates or mural thrombus. Simultaneously, damaged vessel walls and activated platelet surfaces accelerate local clotting, stabilizing the thrombus through fibrin formation (Ref. Reference Andrews96). The formation of atherosclerotic plaque in blood vessels leads to narrowing of the vascular lumen, increased blood flow rate, and the generation of a pathological high wall shear rate. Rupture of the narrow plaque cap disrupts the endothelial cell layer and exposes pro-coagulant collagen in the extracellular matrix. These events result in vWF binding and elongation, platelet adhesion and shear-induced platelet aggregation, leading to the formation of occlusive clots and blood flow cessation (Ref. Reference Kim97). Zhang et al. previously described a unique anti-platelet autoantibody in patients with HIV or hepatitis C-associated thrombocytopenia that recognizes the GPIIIa49-66 sequence. After activation by platelet-reducing nicotinamide adenine dinucleotide phosphate oxidase, this autoantibody induces complement-independent thrombocytolysis through the production of reactive oxygen species and peroxides (Refs Reference Li98, Reference Nardi99). Subsequently, they discovered a human single chain variable fragment antibody, named A11, which recognizes GPIIIa49-66. This antibody exhibits functional properties similar to patient autoantibodies, preferentially binds to activated platelets and is capable of lysing arterial thrombi in vitro.

Inflammation plays a crucial role in initiating deep vein thrombosis (DVT). Various clinical conditions related to inflammation, including sepsis, systemic infections, cancer, trauma and surgery, are linked to a higher risk of venous thromboembolism (encompassing DVT and pulmonary embolism). Inflammation may be implicated in the pathophysiology of post-thrombotic syndrome, characterized by fibrotic vein damage, incomplete recirculation and valve insufficiency, resulting in elevated venous pressure after DVT (Ref. Reference ten Cate-Hoek, Henke and Wakefield100). Because of the involvement of inflammation in thrombosis, organization and degradation processes, numerous inflammation-related factors, including conventional biomarkers (e.g. CRP, IL-6 and other cytokines) and specific markers such as matrix metalloproteinases (MMPs), have been assessed as indicators of thrombotic resolution (Ref. Reference Borgel101). Matrix metalloproteinase 9 (MMP9) inactivation did not impact thrombus formation but had a notable influence on thrombus resolution and the restoration of vascular wall elasticity, resulting in enhanced wall compliance in MMP9 knockout mice. MMP9 inactivation also results in increased macrophage infiltration into the thrombus and a relative reduction in the stiffness of collagen and elastin fibres during thrombus resolution, highlighting the potential of MMP9 as a valuable therapeutic target (Ref. Reference Deatrick102). Thromboregulatory protein (THBD) enhances endothelial cell survival and angiogenesis. By means of its epidermal growth factor-like repeat sequence and serine/threonine-rich domain, THBD facilitates thrombin activation of protein C and its cofactor carboxypeptidase-B (Ref. Reference Loghmani and Conway103). Activated protein C (APC) functions as an anticoagulant in the bloodstream through the proteolytic cleavage of coagulation factors V and VIII. Furthermore, APC exhibits anti-inflammatory properties by binding to endothelial protein C receptor expressed on neutrophils and monocytes (Ref. Reference Loghmani and Conway103). Therefore, THBD is regarded as a crucial protective factor against thrombosis and inflammatory conditions (Ref. Reference Ito, Kakihana and Maruyama104).

Post-traumatic ischaemic necrosis

Tissue ischaemic necrosis occurs when tissues and organs do not receive timely blood supply following trauma, leading to necrosis. Ischaemic necrosis can manifest in different body parts, including the heart, brain, digestive tract and bones (Refs Reference Mok, Lau and Wong105, Reference Parks106, Reference Rice, Vannucci and Brierley107, Reference Simon108). Ischaemic necrosis exhibits noticeable variations in occurrence time among different tissues and organs. Tissue ischaemic necrosis primarily occurs in regions with abundant tissue blood perfusion or regions with impaired blood supply. Consequently, severe tissue damage in these areas leads to the destruction of protective mechanisms that maintain blood supply, causing inadequate perfusion and resulting in tissue ischaemic necrosis (Ref. Reference Pearce109). Human tissues rely on adequate blood supply for proper function. Ischaemia directly impairs blood perfusion and normal physiological activities of diverse tissues and organs. Delayed treatment can result in cellular apoptosis, exacerbating inflammation and predisposing to other lesions (Ref. Reference Pijnenburg, Felten and Javier110).

The target proteins associated with post-traumatic ischaemic necrosis primarily include the following: GFAP, B-cell lymphoma-2 (Bcl-2), receptor-interacting protein kinase (RIPK3), receptor-interacting protein 3 (RIP3), connexin 43 (CX43) and adenine nucleotide translocase 1 (ANTI). GFAP, in addition to serving as a biomarker for TBI, has also been implicated in studies associated with ischaemic necrosis. The peak value of GFAP is correlated with the duration of TBI as well as the degree and maturity of neuronal necrosis. Petito CK and Halaby IA employed immunohistochemistry and optical density analysis to assess the alterations of GFAP in astrocytes, aiming to examine the level of neuronal necrosis in mice following transient ischaemia (Ref. Reference Petito and Halaby111). The results demonstrated an initial decline and delayed rise of GFAP in the constructed brain injury model. Additionally, it observed astrocyte swelling and increased transcriptional activity. Moreover, the duration of transient ischaemia had a significant impact on the immunoreactivity of GFAP. According to Kim et al.'s research, the Bcl-2 protein, involved in apoptosis, plays a crucial role in protecting neuronal cells with acquired ischaemic tolerance (Ref. Reference Kim112). The team conducted immunohistochemistry to investigate the cerebral cortex hippocampus region CA1 after gerbils experienced transient global cerebral ischaemia. Necroptosis is a distinct form of programmed necrosis observed in neurological disorders, including cerebral ischaemia. RIPK3 serves as a crucial target in the necroptosis pathway. Fayaz et al. employed small molecule compounds to target and suppress the activity of RIPK3, effectively safeguarding neurons during ischaemic stroke (Ref. Reference Fayaz113). Ischaemic diseases have been confirmed to be associated with abnormal cell death induced by ischaemic stress. The pathogenesis of ischaemic diseases has been shown to be associated with abnormal cell death induced by ischaemic stress. Lee et al. utilized computer simulation interaction analysis, and combined experimental approaches to provide initial evidence that phosphorylated death-associated protein (Daxx) plays a crucial role in mediating ischaemic cell death via RIP3 (Ref. Reference Lee114). Fas-associated factor 1 (FAF1) is a protein known for its involvement in apoptosis, but its role in the pathogenesis of ischaemic diseases remains unclear. In their study, Yu et al. established a mouse model of retinal ischaemia to investigate the potential association of FAF1 with the pathogenesis of ischaemic diseases (Ref. Reference Yu115). The study revealed that FAF1 plays a crucial role in the development of ischaemic retinal damage and may be implicated in the pathogenesis of retinal ischaemic diseases. Xi et al. investigated microRNA-206 (miR-206) regulates the CX43 extracellular signal-regulated protein kinase (ERK)1/2 pathway whether involved in the occurrence of glucocorticoid-induced avascular necrosis of the femoral head (Ref. Reference Xi116). The results demonstrated that miR-206 down-regulates its target protein CX43, leading to the inhibition of the ERK1/2 signalling pathway and osteogenic differentiation. Moreover, miR-206 is involved in the development and repair processes of steroid-induced necrosis of the femoral head. Wang et al. investigated the involvement of miR-2861 and ANTI in the regulation of cardiac cell necrosis during myocardial ischaemic necrosis (Ref. Reference Wang117). They found that ANTI not only inhibited h202-induced cardiomyocyte necrosis but also antagonized myocardial necrosis in a mouse model of ischaemia/reperfusion. Biomarkers of trauma and their biological functions are shown in Table 4.

Table 4. Biomarkers of post-traumatic ischaemic necrosis and their biological functions

Target protein identification techniques

Proteins are vital constituents of human tissues and cells, participating in nearly all biological processes and functions. Proteins exert regulatory functions in the human body, serving as hormones, enzymes and signalling molecules that play a critical role in governing human metabolism, immune response and cellular signal transmission (Ref. Reference Healy118). They contribute to maintaining a stable physiological state in the body and actively participate in the body's adaptation and response to internal and external stimuli. Moreover, abnormal levels or structural alterations of proteins can indicate the presence of diseases or pathological conditions (Ref. Reference Li119). For instance, abnormal levels of certain proteins, either excessive or deficient, may be associated with diseases including tumours, inflammation, cardiovascular disorders and cerebrovascular diseases. Through the detection and analysis of protein alterations, it facilitates disease diagnosis, assessment of disease severity and monitoring of treatment efficacy.

Proteomics is now recognized as a more suitable approach than genomics for comprehending gene function since it examines the final output of the genome. Target protein identification technology relies on sophisticated experimental methods and analytical techniques, including MS, electrophoresis and small interfering RNA blocking, among others. This technology aids in the identification of target proteins linked to specific diseases or biological processes, allowing for the determination of their features, such as expression level, localization, structure and function.

Mass spectrometry

MS is a method used to study protein complexes, providing insights into subunit stoichiometry, composition and protein–ligand and protein–protein interactions. MS can be employed to determine the amino acid sequence of peptides and characterize various post-translational modifications, including phosphorylation and glycosylation. Additionally, it enables absolute and relative quantification of proteins, allowing the identification and quantification of thousands of proteins from complex samples (Ref. Reference Zhang120). Its speed, high sensitivity and versatility have made it an indispensable tool for peptide and protein analysis. MS consists of three basic components: an ion source, a mass analyzer and a detector. An ion source is utilized to ionize the species under analysis, and the resulting ions are subsequently transported to the mass analyzer by a magnetic or electric field. Two commonly employed ionization methods are electrospray ionization (ESI) (Ref. Reference Fenn121) and matrix-assisted laser desorption ionization (MALDI) (Ref. Reference Karas122). A mass analyzer differentiates ions based on their mass/charge (m/z) ratios. Detectors record the charge induced or current generated as ions pass by or strike a surface, and a computer graphically displays the resulting signal as a mass spectrum.

MS is classified into ESI-MS and MALDI-MS based on distinct ionization methods. Prior to performing MS analysis, ESI employs electrical energy to facilitate the migration of ions from the solution to the gas phase. ESI converts the test sample into ions, then disperses finely charged droplets, generating an aerosol by subjecting the liquid to high pressure (Ref. Reference Ho123). MALDI employs matrices that absorb laser energy to generate ions from macromolecules, thus preventing thermal decomposition of heat-sensitive compounds caused by rapid heating (Ref. Reference Hillenkamp124). MALDI method comprises three steps. In the first step, a sample is mixed with an appropriate matrix material and coated on a metal plate. Subsequently, the sample is irradiated by a pulsed laser, resulting in ablation and desorption of both the sample and matrix material. Finally, the analyte molecules traverse a thermal plume of ablative gas, where they become protonated or deprotonated and ionized, before entering a mass spectrometer for analysis (Ref. Reference Karas and Kruger125).

In protein analysis, MS is commonly coupled with liquid chromatography. The protein mixture is subjected to trypsinization, resulting in the generation of short peptide fragments. These fragments are then separated by chromatography and introduced into a mass spectrometer. The ion source of the mass spectrometer ionizes the sample, charging the peptides, and determines their relative molecular mass by recording the m/z ratio and charge of the ionized peptides. The peptides are then further fragmented to obtain fragment ion information. By combining these two sets of information, m/z ratio spectra can be generated, which can be compared with protein databases to identify proteins corresponding to the peptides in the sample (Ref. Reference Wilfried, Niessen, Desiderio and Loo126).

Figure 3 illustrates the protocol for identifying target proteins through MS. Tissue or cell samples were collected and subjected to sample lysis and protein extraction using SDT buffer (4% sodium dodecyl sulphate (SDS), 100 mM Tris-HCl, pH 7.6). Protein quantification was then conducted using a bicinchoninic acid protein assay kit. The proteins were digested into small peptide fragments using trypsin. High-performance liquid chromatography was employed to separate the protein samples. Subsequently, the separated protein samples were analysed using liquid chromatography tandem mass spectrometry (LC-MS/MS). The acquired MS data were analysed and interpreted. Protein identification and functional annotation were conducted utilizing suitable bioinformatics tools and databases, including UniProt, NCBI, etc.

Figure 3. Process of quantifying proteins through MS.

Cross-linking MS captures the 3D structure of proteins by covalently linking residues that are in close spatial proximity. The proteins were subsequently identified using MS and database searches based on these structures. In a typical cross-linking MS experiment, proteins are cross-linked in solution, digested into peptides using a protease (typically trypsin) and subsequently analysed by LC-MS/MS in data-dependent acquisition mode (Ref. Reference Chen and Rappsilber127).

Currently, MS is a pivotal technology for screening protein markers and drug therapeutic targets. Hinson et al. employed antibody-free proteomics to identify protein biomarkers in the plasma of patients who experienced severe trauma. MS detected the non-specific upregulation of vascular adhesion molecule 1 and MMP9, which serve as protein markers reflecting blood–brain barrier disruption, in severely injured patients (Ref. Reference Hinson86). Peng Zhang et al. employed isobaric tag for relative and absolute quantification in conjunction with LC-MS/MS to measure changes in brain protein expression in rats with diffuse axonal injury (DAI). They successfully identified and quantified a total of 1858 proteins, including four novel biomarkers (citrate synthase, synaptosomal-associated protein 25, microtubule-associated protein 1B and Rho-associated protein kinase 2), which offer potential insights into the pathophysiological mechanisms underlying DAI (Ref. Reference Zhang128). Junjie Zhang et al. employed surface-enhanced laser desorption/ionization-time of flight mass spectrometry for the screening of differentially expressed proteins in serum samples from Wilms tumour patients. They observed an increased level of thioredoxin 1, an inflammatory factor associated with post-traumatic stress, suggesting its potential role in post-traumatic stress in Wilms' tumours (Ref. Reference Zhang129).

Electrophoresis

Gel electrophoresis

Gel electrophoresis encompasses several techniques: one-dimensional PAGE, two-dimensional PAGE (2D-PAGE), isoelectric focusing (IEF) electrophoresis and non-denaturing gel electrophoresis. In one-dimensional gel electrophoresis, the migration rate of proteins is determined by the pore size, protein charge, size and shape of the gel, leading to their separation (Ref. Reference Gallagher130). 2D-PAGE is a commonly used technique in proteomics research (Ref. Reference O'Farrell131). IEF is a widely used technique for separating proteins based on their isoelectric point pH (referred to as pI). It is employed for both analytical and preparative protein separations (Ref. Reference Bjellqvist132). IEF is an electrophoretic technique in which amphiphilic molecules separate as they migrate through a pH gradient (Ref. Reference Garfin133). One major limitation of sodium dodecyl sulphate PAGE (SDS-PAGE) is that it intentionally denatures proteins prior to electrophoresis, resulting in the inability to detect enzyme activity, protein cofactors and protein-binding interactions (Ref. Reference Nowakowski, Wobig and Petering134). In contrast to denaturing methods, native gel electrophoresis does not employ charged denaturants such as SDS, allowing the preservation of the native protein structure. Consequently, the migration of proteins is determined by their inherent charge (Ref. Reference Nigg and Pavlovic135).

The general procedure for detecting trauma-related target proteins through gel electrophoresis is as follows: utilize the identical methods for protein extraction and quantification as employed in MS. Then, prepare polyacrylamide gel and the corresponding electrophoresis buffer. Conduct necessary pre-processing steps on the proteins, such as reduction, alkylation, etc. Subsequently, the prepared protein samples were loaded into the gel slots, connect the slots to the electrophoresis system and apply a suitable voltage to separate and mobilize the proteins within the gel. If desired, stain the gel using a dye, such as Coomassie brilliant blue, to visualize the protein bands. Analyse the gel electrophoresis image using suitable image analysis software, and determine the migration position of the target proteins by referencing known molecular weight markers. Finally, perform identification and annotation of the target proteins.

Capillary electrophoresis

Capillary electrophoresis (CE) is a sensitive and versatile technique that encompasses a series of electrophoretic separation methods performed in capillaries with submillimetre diameters, as well as microfluidic and nanofluidic channels. These methods include capillary zone electrophoresis, capillary isoelectric focusing, capillary gel electrophoresis (CGE), affinity capillary electrophoresis (ACE), capillary isotachophoresis and micellar electrokinetic chromatography (Ref. Reference Kemp136). CGE is a widely employed technique for protein separation. CGE offers several advantages over traditional SDS-PAGE, such as on-column detection, automated operation, superior separation capability and precise quantification of proteins and determination of molecular weights (Ref. Reference Zhu, Lu and Liu137). ACE is a specialized form of CE that utilizes intermolecular binding interactions to investigate protein–ligand interactions (Ref. Reference Chu138).

Immunological methods

Enzyme-linked immunosorbent assay

Enzyme-linked immunosorbent assay (ELISA) is a highly sensitive and specific biochemical analytical method used for detecting, quantifying or qualitatively analysing analytes.

Here are the basic steps for ELISA: (1) coating: the first step involves coating a microplate with the substance to be detected. This is typically done by immobilizing the antigen on the surface of the plate. (2) Blocking: after coating, the plate is blocked to prevent non-specific binding. This involves adding a blocking agent (e.g. bovine serum albumin or milk) to cover any remaining uncoated surfaces on the plate. (3) Primary antibody incubation: the sample, which may contain the target molecule (e.g. a specific protein), is added to the wells of the microplate. If the target molecule is present, it will bind to the immobilized antigen. (4) Washing: the plate is then washed to remove any unbound or non-specifically bound substances. (5) Secondary antibody incubation: a secondary antibody, which is specific to the primary antibody and is conjugated with an enzyme (e.g. horseradish peroxidase or alkaline phosphatase), is added. This secondary antibody binds to the primary antibody that is already bound to the target molecule. (6) Washing: again, the plate is washed to remove any unbound secondary antibody. (7) Enzyme reaction: a substrate specific to the enzyme is added. If the enzyme is present (because the secondary antibody is bound), it will catalyse a reaction that produces a detectable signal, often a colour change. (8) Measurement: the intensity of the colour change is proportional to the amount of the target molecule present in the sample. This can be measured using a spectrophotometer.

ELISA has various variations depending on the specific application, including direct ELISA, indirect ELISA, sandwich ELISA and competition ELISA (Fig. 4). It is a highly sensitive and specific technique extensively employed in clinical laboratories, research and diagnostics to detect and quantify various biological substances.

Figure 4. Popular ELISA formats: (A) direct ELISA; (B) indirect ELISA; (C) sandwich ELISA; (D) competitive ELISA with labelled antibody and (E) competitive ELISA with labelled antigen.

In recent years, there has been significant progress in the development of enzyme-linked immunosorbent technology, making it a valuable research tool in immune studies, immune diagnosis and immune-related quality control detection across different industries.

One of the advantages of ELISA is that it can be performed without the requirement of complex or expensive equipment. The analyte in ELISA can refer to a specific substance, specific protein or even a complex mixture comprising multiple proteins, such as biomolecular complexes (Ref. Reference Konstantinou139).

Cathepsin X, a lysosomal cysteine-type carboxypeptidase, is mainly found in monocytes and macrophages. It can be released into circulation either because of the composition of immune cells or regulated secretion. Plasma levels of cathepsin X show a significant increase within the initial 72 h after trauma. ELISA can detect cathepsin X levels in both white blood cells and plasma samples from both healthy volunteers and patients with multiple injuries. The high sensitivity and specificity of this method are valuable for further assessing cathepsin X as a marker for diagnosing and recovering from inflammatory diseases (Ref. Reference Nägler140).

Western blot

Western blotting (WB) is a widely employed technique for protein analysis, involving the separation of native or denatured proteins through gel electrophoresis, their subsequent transfer onto protein-binding membranes and the detection of target proteins using specific antibodies (Ref. Reference Meftahi141). This procedure involves multiple steps, including: (a) sample preparation, which entails protein extraction and measurement of protein concentration from cell or tissue lysates; (b) electrophoresis on an SDS polyacrylamide gel to separate proteins based on size; (c) immobilization of the separated proteins onto nitrocellulose or polyvinylidene fluoride membranes; (d) blocking of non-specific proteins on the membrane; (e) detection of target proteins using specific primary antibodies; (f) incubation with labelled chemiluminescent or fluorescent molecule-conjugated secondary antibodies; (g) detection of signals indicating antigen/antibody binding and (h) densitometry analysis of protein bands of interest using software (Ref. Reference Mishra, Tiwari and Gomes142).

Following these basic steps, researchers can identify and quantify specific proteins in a complex mixture, providing valuable information about gene expression and protein levels in biological samples. Consequently, WB can be used to isolate and identify proteins in scientific and biomedical research, thus aiding in the diagnosis of various diseases.

WB technique is a well-established molecular biology method that has been utilized for over 30 years. It offers high selectivity and sensitivity in protein detection, making it a valuable tool for identifying target proteins in diverse samples. Recent studies indicate that the complement and apoptosis systems in cells are activated as part of a danger-sensing and signalling cascade following severe tissue trauma reactions, particularly in patients with multiple injuries. The early activation of the complement cascade and excessive production of the potent allergic toxin C5a are demonstrated to modulate the immune response following trauma. Huber-Lang et al. (Ref. Reference Huber-Lang143) employed western blotting to elucidate the interaction between the central complement component C5 and the pro-apoptotic aspartic protease cathepsin D. In vitro co-incubation of C5a and cathepsin D led to a progressive increase in the concentration of C5a over time. However, when inhibited by the aspartic protease inhibitor pepstatin A, these findings offer in vitro evidence that the aspartic protease cleaves C5, leading to the production of functional C5a. This represents a novel pathway for complement activation.

Lateral flow immunoassays

Lateral flow assay (LFA) is a strip-based platform used for rapid detection and quantification of analytes in complex mixtures. It involves applying the sample to the detection device and obtaining the test result within 5–30 min. The basic principle involves the liquid sample (or extract) containing the target analyte moving across different regions of the polymer strip through capillary action. Specific regions of the strip can bind to molecules that interact with the analyte, and a labelled analyte-specific molecule can also be present. The test result is visually distinguishable (Ref. Reference Koczula and Gallotta49).

Lateral flow immunoassays (LFIAs) operate on a simple and rapid principle. Test strips typically consist of four components: a sample pad, a binding pad, a detection pad and absorbent paper. The process of LFIAs is shown in Figure 5. It begins when a liquid sample (e.g. blood, urine, saliva) is applied to the sample pad. Then, the sample moves to the conjugate pad, which contains particles (commonly gold or coloured latex beads) coated with molecules that can bind to the target analyte in the sample. The sample continues to flow through the reactive membrane, which is a strip or membrane with immobilized capture reagents. These capture reagents can be antibodies or other molecules that specifically bind to the target analyte. Finally, the mixture reaches the absorbent pad, which helps wick the liquid through the membrane.

Figure 5. Schematic diagram of LFIAs.

The cost-effective development and simple production of LFA enable its broad application in various rapid detection fields. It can be utilized for qualitative and quantitative detection of specific antigens, antibodies, gene amplification products as well as diverse biological samples (Ref. Reference Koczula and Gallotta49).

Raman spectroscopy

In 1928, the Indian scientist C. V. Raman was inspired by the Compton effect and discovered the phenomenon of inelastic light scattering, now known as the Raman effect. This effect is universal in all types of molecules, whether they are in the form of gas, liquid or solid. When a sample is illuminated by visible monochromatic light, most of the light is absorbed, while a portion of it is scattered. During this process, the incident photons interact with the molecules in the sample, resulting in elastic collisions. The absorbed energy causes the molecules to transition to an unstable virtual energy state, followed by the release of energy and a photon during the relaxation process. When the released energy matches the absorbed energy, the molecule returns to its original energy level, resulting in the scattered light having the same frequency as the incident light. However, only the direction of the scattered light changes, and this scattering phenomenon is referred to as Rayleigh scattering. In the case of inelastic collisions between photons and molecules, the direction of the photon changes, and energy exchange occurs between them. After the energy release, the molecule does not return to its initial energy level, resulting in an energy difference between the final and initial states. This difference leads to the phenomenon known as Raman scattering, where the energy lost by the electron is not equal to the absorbed energy, causing the incident frequency to differ from the scattering frequency (Ref. Reference Krafft144). Raman scattering can be further classified into Stokes Raman scattering, which occurs when the scattering frequency is lower than the incident frequency, and anti-Stokes Raman scattering, which occurs when the scattering frequency is higher than the incident frequency. When the two frequencies are equal, the scattering is referred to as Rayleigh scattering (Ref. Reference Raman and Krishnan145).

Raman spectroscopy, named after C. V. Raman, is a non-destructive method based on inelastic vibration scattering that is used to detect molecular vibrations. Each molecule possesses unique chemical bonds, resulting in distinct vibrational modes and corresponding energy changes, known as Raman shifts. This characteristic Raman shift provides a specific fingerprint for the biochemical composition and structure of tissues. Consequently, each molecule has its own characteristic Raman spectrum, which allows for qualitative analysis by determining rotational energy levels. Additionally, the intensity of the Raman line can be used for quantitative analysis to determine molecular concentration (Ref. Reference Kirsch146). Raman spectroscopy is highly sensitive, non-invasive and effective in identifying target proteins. It can be used for in vivo molecular imaging and in vitro detection of metabolites and protein biomarkers (Ref. Reference Cutshaw147). In Raman spectroscopy, a common method involves collecting Raman spectra by irradiating the sample with a laser beam through a microscope objective. The scattered Raman light is then reflected back to the optical path through the microscope objective, and finally, the light signal is converted into an electrical signal to generate Raman spectra.

Raman spectroscopy has been employed to analyse differences in target proteins between healthy and sick samples. For instance, a comparative analysis using Raman spectroscopy was conducted on mice induced with sepsis and uninfected mice. The results revealed a significant increase in haemoglobin content in mice with sepsis compared with normal mice (Ref. Reference Verma148). The process is illustrated in Figure 6. Additionally, a large amount of haemoglobin was released from red blood cells, and the haemoglobin content was positively correlated with the mortality rate of sepsis patients (Ref. Reference Effenberger-Neidnicht and Hartmann149). Raman spectrum analysis has also identified platelet-derived growth factor-BB as a key biomarker for distinguishing patients undergoing coronary intervention (Ref. Reference Yang150). Raman spectroscopy offers several advantages, such as eliminating the need for sample preparation, applicability to a wide range of objects and non-intrusiveness. However, the weak Raman signal resulting from the small Raman scattering cross section of molecules remains a challenge in Raman spectroscopy. The Raman scattering cross section can be understood as the probability of collision between photons and molecules. Currently, plasmon-enhanced Raman scattering (PERS) has emerged as an effective method to enhance Raman signals. One common mode of PERS is surface-enhanced Raman scattering (SERS), which enables the identification of biomolecules such as target proteins and nucleic acids. The interaction of biomolecules bound to metal surfaces alters the spectral characteristics, thus enhancing the Raman signal (Ref. Reference Zong151). In this case, the vibrational energy levels of molecules are primarily excited by the collective oscillation of conduction electrons in metals. When appropriately excited, nanostructures generate a super-strong electromagnetic near-field near their surfaces. Tip-enhanced Raman scattering (TERS) is another mode of PERS that allows for the identification of specific proteins. This technique involves scanning a laser beam on the tip, fixing the laser at the point of highest enhancement and using this arrangement for further measurements. The measured parameter value depends on the spectral quality associated with the electromagnetic field enhancement of a specific tip. TERS requires special sample preparation and substrate (Ref. Reference Kradolfer152).

Figure 6. Raman spectroscopy revealed cell-free haemoglobin as a marker for systemic inflammation.

SERS and TERS are two different PERS techniques with differences in sample preparation. SERS usually uses metal substrates with good surface enhancement effects to enhance the Raman signal of the sample. The sample to be tested is typically added directly as a solution onto the cleaned and treated metal substrate, where it can be adsorbed through interaction with the metal surface. The prepared SERS samples can be characterized using techniques such as scanning electron microscopy and atomic force microscopy. In a study, SERS technology combines aptamer sensors for sensitive detection of protein biomarkers. A nanocomposite with a special structure was designed, consisting of flower-like silver nanoparticles and a magnetic material. Specific DNA aptamers were used to selectively and effectively modify the surface of the nanocomplex for capturing cTnI target proteins. Binding of the target protein to the nanocomplex triggers the SERS effect, generating a specific Raman signal. The design of this SERS adaptor construct provides a novel approach to detect target proteins relevant for trauma diagnosis (Ref. Reference Alves, Sigoli and Mazali153).

TERS requires special nanoprobes, typically with metal nanoparticle modifications. These nanoprobes have a fine-pointed geometry and produce a local electromagnetic field enhancement effect. The sample to be tested is typically positioned near the tip of the nanoprobe to establish contact with the probe surface. Local electromagnetic field enhancement can be achieved by adjusting parameters such as the distance and position of the probe and sample, scanning the sample surface with movement of the probe and laser beam while recording Raman signals. TERS technology has the potential to provide information on protein structure, interactions and denaturation states in protein studies.

For instance, TERS technology can be utilized for studying the molecular structure and structural changes of proteins. Collecting TERS spectral data from protein samples allows the extraction of vibrational information about the protein, including vibrational frequency and mode of specific amino acid residues. This information enables researchers to deduce the protein's secondary structure, folding state and potential conformational changes.

TERS technology can be utilized for studying protein–ligand interactions, including those involving drug molecules. By bringing the needle tip into contact with a sample containing the protein and ligand, TERS can provide Raman signals related to the interaction interface. This capability aids researchers in comprehending the interaction mechanism, binding site and binding strength between the protein and ligand. Furthermore, TERS technology enables the investigation of protein denaturation processes and folding states. By monitoring Raman spectral changes in protein samples under varying environmental conditions (e.g. temperature, solvent, pH), TERS facilitates researchers' comprehension of protein stability, structural alterations and changes in folding states (Ref. Reference Verma154). These applications enhance the potential of Raman spectroscopy for detecting target proteins in wounds.

Summary and outlook

Target protein identification technology holds immense potential for development and allows for in-depth investigation into alterations in body proteins following trauma. By employing techniques such as MS, gel electrophoresis and others, we can detect specific protein markers generated following trauma. These markers can serve as indicators for diagnosing and predicting post-traumatic complications. Moreover, the application of target protein identification technology aids in unravelling the mechanisms involved in wound development and identifying potential targets for therapeutic intervention.

Target protein technology can be integrated with multiomics approaches. For instance, integrating it with genomics research can enhance our understanding of the molecular mechanisms underlying post-traumatic complications. Studies have demonstrated that the progression of sepsis following trauma can vary based on differences in the gene expression profiles of patients. Integrating it with pharmacogenomics research enables us to comprehend an individual's drug response and tolerance. Through analysis of individual genetic variations, it becomes feasible to predict a patient's response to a specific drug and the likelihood of adverse effects, facilitating the development of personalized treatment plans. Additionally, bioinformatics plays a crucial role in trauma management by offering efficient methods for large-scale data processing and analysis. This enhances the identification efficiency of target proteins and novel biomarkers and accelerates their discovery. Moreover, it aids in constructing predictive models to facilitate the development of optimized treatment strategies.

As technology continues to advance and various fields integrate their technologies, we anticipate the emergence of precise and personalized methods for diagnosing and treating trauma. These advancements will significantly diminish the impact of trauma on patient survival.

Abbreviations

2D-PAGE, two-dimensional PAGE; ACE, affinity capillary electrophoresis; ALI, acute lung injury; Ang-1, angiopoietin-1; Ang-2, angiopoietin-2; ANTI, adenine nucleotide translocase 1; APC, activated protein C; ARDS, adult respiratory distress syndrome; Bcl-2, B-cell lymphoma-2; CC-16, Clara cell protein 16; CE, capillary electrophoresis; CGE, capillary gel electrophoresis; CRP, C-reactive protein; CSF, cerebrospinal fluid; CX43, connexin 43; DAI, diffuse axonal injury; Daxx, death-associated protein; DIC, disseminated intravascular coagulation; DVT, deep vein thrombosis; IEF, isoelectric focusing; ELISA, enzyme-linked immunosorbent assay; ERK, extracellular signal-regulated protein kinase; ESI, electrospray ionization; FAF1, Fas-associated factor 1; GFAP, glial fibrillary acidic protein; GP, glycoprotein; HDL, high-density lipoprotein; ICU, intensive care unit; IGFs, insulin-like growth factors; IGFBP7, insulin-like growth factor-binding protein 7; ILs, interleukins; IL-1, interleukin-1; IL-6, interleukin-6; IL-8, interleukin-8; LC-MS/MS, liquid chromatography tandem mass spectrometry; LFA, lateral flow assay; LFIAs, lateral flow immunoassays; MALDI, matrix-assisted laser desorption ionization; MAPs, microtubule-associated proteins; miR-206, microRNA-206; MMPs, matrix metalloproteinases; MMP9, matrix metalloproteinase 9; MS, mass spectrometry; m/z, mass/charge; NSE, neuronal-specific enolase; PAGE, polyacrylamide gel electrophoresis; PBMCs, peripheral blood mononuclear cells; PCT, procalcitonin; PERS, plasmon-enhanced Raman scattering; PT, prothrombin; PTX3, pentraxin 3; RAGE, receptor for advanced glycation end products; RIP3, receptor-interacting protein 3; RIPK3, receptor-interacting protein kinase; S100B, S100 calcium-binding protein B; SDS, sodium dodecyl sulphate; SDS-PAGE, sodium dodecyl sulphate PAGE; SERS, surface-enhanced Raman scattering; SP-D, surfactant protein-D; sRAGE, soluble receptor for advanced glycation end products; TBI, traumatic brain injury; TERS, tip-enhanced Raman scattering; TF, tissue factor; THBD, thromboregulatory protein; TNF-α, tumour necrosis factor-alpha; UCH-L1, ubiquitin C-terminal hydrolase-L1; VEGF, vascular endothelial growth factor; vWF, von Willebrand factor; WB, Western blotting; αIIbβ3, IIb-IIIa; αII-spectrin, alpha-II-spectrin; α-syn, alpha-synuclein

Funding statement

This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (grant numbers 2021Y9107 and 2021Y9196); the Fuzhou Science and Technology Project (grant number 2021S263); the Major Research Projects for Young and Middle-aged Researchers of Fujian Provincial Health Commission (grant numbers 2021ZQNZD010 and 2021ZQNZD001) and the Natural Science Foundation of Fujian Province (grant numbers 2022J01521, 2021J01607 and 2022J011067); Fujian Provincial Science and Technology Plan Project (grant number 2022Y4003); the Fuzhou Science and Technology Project (grant number 2022S005); Fujian Provincial Major Health Research Project (grant number 2022ZD01001); Fujian provincial health technology project (grant number 2021GGA053); Fuzhou University Testing Fund of precious apparatus (grant number 2024T021); Fujian Provincial Health Technology Project (grant number 2023CXA018); Fuzhou Health science and technology plan soft science research project (grant number 2022-S-wr4); Young and middle-aged talent research project of Fuzhou city (grant number 2022-S-rc).

Competing interests

None.

Footnotes

*

These authors contributed equally to this work and should be considered as co-first authors.

References

Murray, CJL and Lopez, AD (1997) Mortality by cause for eight regions of the world: global burden of disease study. Lancet (London, England) 349, 12691276. https://doi.org/10.1016/S0140-6736(96)07493-4CrossRefGoogle ScholarPubMed
Malekifar, E et al. (2022) Auditing the status of prioritization of nursing actions in the trauma emergency department. International Emergency Nursing 64, 101215. https://doi.org/10.1016/j.ienj.2022.101215CrossRefGoogle ScholarPubMed
Messman-Moore, TL and Cook, NK (2016) Posttraumatic stress disorder, pp. 308312. https://doi.org/10.1016/B978-0-12-397045-9.00012-4Google Scholar
Heming, N et al. (2021) Sepsis in the critically ill patient: current and emerging management strategies. Expert Review of Anti-Infective Therapy 19, 635647. https://doi.org/10.1080/14787210.2021.1846522CrossRefGoogle ScholarPubMed
Lakomkin, N et al. (2017) Incidence and predictive risk factors of postoperative sepsis in orthopedic trauma patients. Journal of Orthopaedics and Traumatology 18, 151158. https://doi.org/10.1007/s10195-016-0437-4CrossRefGoogle ScholarPubMed
Villarreal, JV, Shibuya, N and Jupiter, DC (2021) Thromboprophylaxis and bleeding complications in orthopedic and trauma patients: a systematic review. Journal of Foot & Ankle Surgery 60, 10141022. https://doi.org/10.1053/j.jfas.2021.03.010CrossRefGoogle ScholarPubMed
Wu, YT et al. (2023) REBOA in trauma and the risk of venous thromboembolic complications: a matched-cohort study. American Journal of Surgery 225, 10911095. https://doi.org/10.1016/j.amjsurg.2022.11.035CrossRefGoogle ScholarPubMed
Pradhan, A et al. (2023) Complications after talus fractures: a trauma centre experience. Injury 54, 772777. https://doi.org/10.1016/j.injury.2022.12.013CrossRefGoogle ScholarPubMed
Ramin, S et al. (2019) Acute respiratory distress syndrome after chest trauma: epidemiology, specific physiopathology and ventilation strategies. Anaesthesia Critical Care & Pain Medicine 38, 265276. https://doi.org/10.1016/j.accpm.2018.09.009CrossRefGoogle ScholarPubMed
Miller, GF et al. (2021) Predictors of traumatic brain injury morbidity and mortality: examination of data from the national trauma data bank: predictors of TBI morbidity & mortality. Injury 52, 11381144. https://doi.org/10.1016/j.injury.2021.01.042CrossRefGoogle ScholarPubMed
Baker, CC et al. (1980) Epidemiology of trauma deaths. The American Journal of Surgery 140, 144150. https://doi.org/10.1016/0002-9610(80)90431-6CrossRefGoogle ScholarPubMed
Hopf, HW (2003) Molecular diagnostics of injury and repair responses in critical illness: what is the future of ‘monitoring’ in the intensive care unit? Critical Care Medicine 31, S518S523. https://doi.org/10.1097/01.CCM.0000081433.98328.4BCrossRefGoogle ScholarPubMed
Danazumi, AU et al. (2023) Targeted protein degradation might present a novel therapeutic approach in the fight against African trypanosomiasis. European Journal of Pharmaceutical Sciences 186, 106451. https://doi.org/10.1016/j.ejps.2023.106451CrossRefGoogle Scholar
Zhu, HJ et al. (2023) Novel strategies and promising opportunities for targeted protein degradation: an innovative therapeutic approach to overcome cancer resistance. Pharmacology & Therapeutics 244, 108371. https://doi.org/10.1016/j.pharmthera.2023.108371CrossRefGoogle ScholarPubMed
Mayr, FB, Yende, S and Angus, DC (2014) Epidemiology of severe sepsis. Virulence 5, 411. https://doi.org/10.4161/viru.27372CrossRefGoogle ScholarPubMed
Singer, M et al. (2016) The third international consensus definitions for sepsis and septic shock (Sepsis-3). The Journal of the American Medical Association 315, 801810. https://doi.org/10.1001/jama.2016.0287CrossRefGoogle ScholarPubMed
Huo, L et al. (2023) Pharmacological inhibition of ferroptosis as a therapeutic target for sepsis-associated organ damage. European Journal of Medicinal Chemistry 257, 115438. https://doi.org/10.1016/j.ejmech.2023.115438CrossRefGoogle ScholarPubMed
van Engelen, TSR et al. (2018) Biomarkers in sepsis. Critical Care Clinics 34, 139152. https://doi.org/10.1016/j.ccc.2017.08.010CrossRefGoogle ScholarPubMed
Calzavacca, P et al. (2014) Effects of selective beta1-adrenoceptor blockade on cardiovascular and renal function and circulating cytokines in ovine hyperdynamic sepsis. Critical Care 18, 610. https://doi.org/10.1186/s13054-014-0610-1CrossRefGoogle ScholarPubMed
Eichberger, J and Resch, B (2022) Reliability of interleukin-6 alone and in combination for diagnosis of early onset neonatal sepsis: systematic review. Frontiers in Pediatrics 10, 840778. https://doi.org/10.3389/fped.2022.840778CrossRefGoogle ScholarPubMed
Kang, SJ et al. (2019) Targeting interleukin-6 signaling in clinic. Immunity 50, 10071023. https://doi.org/10.1016/j.immuni.2019.03.026CrossRefGoogle ScholarPubMed
Kobayashi, T et al. (2021) Rapid changes in serum IL-6 levels in preterm newborns with Gram-negative early-onset sepsis. Cytokine 138, 155371. https://doi.org/10.1016/j.cyto.2020.155371CrossRefGoogle ScholarPubMed
Chen, LR et al. (2023) CD1d-dependent natural killer T-cells inactivation aggravates sepsis-induced myocardial injury via T lymphocytes infiltration and IL-6 production in mice. International Immunopharmacology 120, 110256. https://doi.org/10.1016/j.intimp.2023.110256CrossRefGoogle ScholarPubMed
Ling, HX et al. (2023) Evaluation of qSOFA combined with inflammatory mediators for diagnosing sepsis and predicting mortality among emergency department. Clinica Chimica Acta 544, 117352. https://doi.org/10.1016/j.cca.2023.117352CrossRefGoogle ScholarPubMed
Li, XG and Yan, BQ (2018) Research on the effect of cytokine concentration on the immune level and survival conditions of elderly patients with sepsis. Experimental and Therapeutic Medicine 16, 842846. https://doi.org/10.3892/etm.2018.6221Google ScholarPubMed
Yu, P et al. (2021) Pyroptosis: mechanisms and diseases. Signal Transduction and Targeted Therapy 6, 128. https://doi.org/10.1038/s41392-021-00507-5CrossRefGoogle ScholarPubMed
Wang, YC et al. (2018) Caspase-1-dependent pyroptosis of peripheral blood mononuclear cells predicts the development of sepsis in severe trauma patients. Medicine 97, e9859. https://doi.org/10.1097/md.0000000000009859CrossRefGoogle ScholarPubMed
Darabi, M and Kontush, A (2022) High-density lipoproteins (HDL): novel function and therapeutic applications. Biochimica et Biophysica Acta (BBA) – Molecular and Cell Biology of Lipids 1867, 159058. https://doi.org/10.1016/j.bbalip.2021.159058CrossRefGoogle ScholarPubMed
Tanaka, S et al. (2017) Low HDL levels in sepsis versus trauma patients in intensive care unit. Annals of Intensive Care 7, 60. https://doi.org/10.1186/s13613-017-0284-3CrossRefGoogle ScholarPubMed
Hoshino, K et al. (2017) Incidence of elevated procalcitonin and presepsin levels after severe trauma: a pilot cohort study. Anaesthesia and Intensive Care 45, 600604. https://doi.org/10.1177/0310057X1704500510CrossRefGoogle ScholarPubMed
Magrini, L et al. (2014) Comparison between white blood cell count, procalcitonin and C reactive protein as diagnostic and prognostic biomarkers of infection or sepsis in patients presenting to emergency department. Clinical Chemistry and Laboratory Medicine 52, 14651472. https://doi.org/10.1515/cclm-2014-0210CrossRefGoogle ScholarPubMed
Yang, AP et al. (2016) Neutrophil CD64 combined with PCT, CRP and WBC improves the sensitivity for the early diagnosis of neonatal sepsis. Clinical Chemistry and Laboratory Medicine 54, 345351. https://doi.org/10.1515/cclm-2015-0277CrossRefGoogle ScholarPubMed
BalcI, C et al. (2003) Usefulness of procalcitonin for diagnosis of sepsis in the intensive care unit. Critical Care 7, 8590. https://doi.org/10.1186/cc1843CrossRefGoogle ScholarPubMed
Feigin, E et al. (2023) Early signaling of bacteremia in patients who present to the department of emergency medicine with relatively low C-reactive protein (CRP) concentrations. Clinica Chimica Acta 547, 117451. https://doi.org/10.1016/j.cca.2023.117451CrossRefGoogle Scholar
Dong, W et al. (2022) PTX3 promotes osteogenic differentiation by triggering HA/CD44/FAK/AKT positive feedback loop in an inflammatory environment. Bone 154, 116231. https://doi.org/10.1016/j.bone.2021.116231CrossRefGoogle Scholar
Daigo, K et al. (2012) The proteomic profile of circulating pentraxin 3 (PTX3) complex in sepsis demonstrates the interaction with azurocidin 1 and other components of neutrophil extracellular traps. Molecular & Cellular Proteomics 11, M111 015073. https://doi.org/10.1074/mcp.M111.015073CrossRefGoogle ScholarPubMed
Rezoagli, E, Fumagalli, R and Bellani, G (2017) Definition and epidemiology of acute respiratory distress syndrome. Annals of Translational Medicine 5, 282. https://doi.org/10.21037/atm.2017.06.62CrossRefGoogle ScholarPubMed
Bellani, G et al. (2016) Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. The Journal of the American Medical Association 315, 788800. https://doi.org/10.1001/jama.2016.0291CrossRefGoogle ScholarPubMed
Kasotakis, G et al. (2021) Acute respiratory distress syndrome (ARDS) after trauma: improving incidence, but increasing mortality. Journal of Critical Care 64, 213218. https://doi.org/10.1016/j.jcrc.2021.05.003CrossRefGoogle ScholarPubMed
Moazed, F et al. (2020) Cigarette smoking and ARDS after blunt trauma. Chest 158, 14901498. https://doi.org/10.1016/j.chest.2020.05.603CrossRefGoogle ScholarPubMed
Rubenfeld, GD et al. (2005) Incidence and outcomes of acute lung injury. New England Journal of Medicine 353, 16851693. https://doi.org/10.1056/NEJMoa050333CrossRefGoogle ScholarPubMed
Walter, JM, Wilson, J and Ware, LB (2014) Biomarkers in acute respiratory distress syndrome: from pathobiology to improving patient care. Expert Review of Respiratory Medicine 8, 573586. https://doi.org/10.1586/17476348.2014.924073CrossRefGoogle ScholarPubMed
Calfee, CS et al. (2007) Trauma-associated lung injury differs clinically and biologically from acute lung injury due to other clinical disorders. Critical Care Medicine 35, 22432250. https://doi.org/10.1097/01.ccm.0000280434.33451.87CrossRefGoogle ScholarPubMed
Agrawal, A et al. (2013) Plasma angiopoietin-2 predicts the onset of acute lung injury in critically ill patients. American Journal of Respiratory and Critical Care Medicine 187, 736742. https://doi.org/10.1164/rccm.201208-1460OCCrossRefGoogle ScholarPubMed
Barnett, N and Ware, LB (2011) Biomarkers in acute lung injury – marking forward progress. Critical Care Clinics 27, 661683. https://doi.org/10.1016/j.ccc.2011.04.001CrossRefGoogle ScholarPubMed
Medford, ARL et al. (2009) Relationship between vascular endothelial growth factor + 936 genotype and plasma/epithelial lining fluid vascular endothelial growth factor protein levels in patients with and at risk for ARDS. Chest 136, 457464. https://doi.org/10.1378/chest.09-0383CrossRefGoogle ScholarPubMed
Ware, LB et al. (2013) Biomarkers of lung epithelial injury and inflammation distinguish severe sepsis patients with acute respiratory distress syndrome. Critical Care 17, R253. https://doi.org/10.1186/cc13080CrossRefGoogle ScholarPubMed
Uchida, T et al. (2006) Receptor for advanced glycation end-products is a marker of type I cell injury in acute lung injury. American Journal of Respiratory and Critical Care Medicine 173, 10081015. https://doi.org/10.1164/rccm.200509-1477OCCrossRefGoogle ScholarPubMed
Koczula, KM and Gallotta, A (2016) Lateral flow assays. Essays in Biochemistry 60, 111120. https://doi.org/10.1042/EBC20150012Google ScholarPubMed
Fremont, RD et al. (2010) Acute lung injury in patients with traumatic injuries: utility of a panel of biomarkers for diagnosis and pathogenesis. Journal of Trauma-Injury Infection and Critical Care 68, 11211127. https://doi.org/10.1097/TA.0b013e3181c40728CrossRefGoogle ScholarPubMed
Faust, HE et al. (2020) Plasma mitochondrial DNA levels are associated with ARDS in trauma and sepsis patients. Chest 157, 6776. https://doi.org/10.1016/j.chest.2019.09.028CrossRefGoogle ScholarPubMed
Ong, T et al. (2010) Ratio of angiopoietin-2 to angiopoietin-1 as a predictor of mortality in acute lung injury patients. Critical Care Medicine 38, 18451851. https://doi.org/10.1097/CCM.0b013e3181eaa5bfCrossRefGoogle ScholarPubMed
Moss, M et al. (1996) Endothelial cell activity varies in patients at risk for the adult respiratory distress syndrome. Critical Care Medicine 24, 17821786. https://doi.org/10.1097/00003246-199611000-00004CrossRefGoogle ScholarPubMed
Leung, DW et al. (1989) Vascular endothelial growth factor is a secreted angiogenic mitogen. Science (New York, N.Y.) 246, 13061309. https://doi.org/doi:10.1126/science.2479986CrossRefGoogle ScholarPubMed
Hartl, D and Griese, M (2006) Surfactant protein D in human lung diseases. European Journal of Clinical Investigation 36, 423435. https://doi.org/10.1111/j.1365-2362.2006.01648.xCrossRefGoogle ScholarPubMed
Erusalimsky, JD (2021) The use of the soluble receptor for advanced glycation-end products (sRAGE) as a potential biomarker of disease risk and adverse outcomes. Redox Biology 42, 101958. https://doi.org/10.1016/j.redox.2021.101958CrossRefGoogle ScholarPubMed
Broeckaert, F et al. (2000) Clara cell secretory protein (CC16): features as a peripheral lung biomarker. Annals of the New York Academy of Sciences 923, 6877. https://doi.org/10.1111/j.1749-6632.2000.tb05520.xCrossRefGoogle ScholarPubMed
Xu, WJ and Song, Y (2017) Biomarkers for patients with trauma associated acute respiratory distress syndrome. Military Medical Research 4, 25. https://doi.org/10.1186/s40779-017-0134-5CrossRefGoogle ScholarPubMed
Dong, X et al. (2021) Plasma insulin-like growth factor binding protein 7 contributes causally to ARDS 28-day mortality: evidence from multistage Mendelian randomization. Chest 159, 10071018. https://doi.org/10.1016/j.chest.2020.10.074CrossRefGoogle ScholarPubMed
Rademaker, E et al. (2022) Thrombosis pathways in COVID-19 vs. influenza-associated ARDS: a targeted proteomics approach. Journal of Thrombosis and Haemostasis 20, 12061212. https://doi.org/10.1111/jth.15671CrossRefGoogle ScholarPubMed
Agoston, DV, Shutes-David, A and Peskind, ER (2017) Biofluid biomarkers of traumatic brain injury. Brain Injury 31, 11951203. https://doi.org/10.1080/02699052.2017.1357836CrossRefGoogle ScholarPubMed
Roozenbeek, B, Maas, AI and Menon, DK (2013) Changing patterns in the epidemiology of traumatic brain injury. Nature Reviews Neurology 9, 231236. https://doi.org/10.1038/nrneurol.2013.22CrossRefGoogle ScholarPubMed
Azar, S et al. (2017) Biofluid proteomics and biomarkers in traumatic brain injury. Methods in Molecular Biology 1598, 4563. https://doi.org/10.1007/978-1-4939-6952-4_3CrossRefGoogle ScholarPubMed
Thornhill, S et al. (2000) Disability in young people and adults one year after head injury: prospective cohort study. British Medical Journal 320, 16311635. https://doi.org/10.1136/bmj.320.7250.1631CrossRefGoogle ScholarPubMed
Gourin, CG and Shackford, SR (1997) Production of tumor necrosis factor-alpha and interleukin-1beta by human cerebral microvascular endothelium after percussive trauma. The Journal of Trauma: Injury, Infection, and Critical Care 42, 11011107. https://doi.org/10.1097/00005373-199706000-00020CrossRefGoogle ScholarPubMed
Rainey, T et al. (2009) Predicting outcome after severe traumatic brain injury using the serum S100B biomarker: results using a single (24 h) time-point. Resuscitation 80, 341345. https://doi.org/10.1016/j.resuscitation.2008.11.021CrossRefGoogle Scholar
Vogelbaum, MA et al. (2005) S100beta as a predictor of brain metastases: brain versus cerebrovascular damage. Cancer 104, 817824. https://doi.org/10.1002/cncr.21220CrossRefGoogle ScholarPubMed
Metting, Z et al. (2012) GFAP and S100B in the acute phase of mild traumatic brain injury. Neurology 78, 14281433. https://doi.org/10.1212/WNL.0b013e318253d5c7CrossRefGoogle ScholarPubMed
Vos, PE et al. (2010) GFAP and S100B are biomarkers of traumatic brain injury: an observational cohort study. Neurology 75, 17861793. https://doi.org/10.1212/WNL.0b013e3181fd62d2CrossRefGoogle ScholarPubMed
Heizmann, CW, Fritz, G and Schäfer, BW (2002) S100 proteins: structure, functions and pathology. Frontiers in Bioscience-Landmark 7, 13561368. https://doi.org/10.2741/heizmannGoogle ScholarPubMed
Haselmann, V et al. (2021) Plasma-based S100B testing for management of traumatic brain injury in emergency setting. Practical Laboratory Medicine 26, e00236. https://doi.org/10.1016/j.plabm.2021.e00236CrossRefGoogle ScholarPubMed
Papa, L et al. (2010) Ubiquitin C-terminal hydrolase is a novel biomarker in humans for severe traumatic brain injury. Critical Care Medicine 38, 138144. https://doi.org/10.1097/CCM.0b013e3181b788abCrossRefGoogle ScholarPubMed
Meric, E et al. (2010) The prognostic value of neuron-specific enolase in head trauma patients. Journal of Emergency Medicine 38, 297301. https://doi.org/10.1016/j.jemermed.2007.11.032CrossRefGoogle ScholarPubMed
Pineda, JA et al. (2007) Clinical significance of alphaII-spectrin breakdown products in cerebrospinal fluid after severe traumatic brain injury. Journal of Neurotrauma 24, 354366. https://doi.org/10.1089/neu.2006.003789CrossRefGoogle ScholarPubMed
Brophy, GM et al. (2011) Biokinetic analysis of ubiquitin C-terminal hydrolase-L1 (UCH-L1) in severe traumatic brain injury patient biofluids. Journal of Neurotrauma 28, 861-870. https://doi.org/10.1089/neu.2010.1564CrossRefGoogle ScholarPubMed
Züngün, C et al. (2013) Assessment of serum S100B and neuron specific enolase levels to evaluate the neurotoxic effects of organic solvent exposure. Clinical Toxicology 51, 748751. https://doi.org/10.3109/15563650.2013.820831CrossRefGoogle ScholarPubMed
Cardali, S and Maugeri, R (2006) Detection of alphaII-spectrin and breakdown products in humans after severe traumatic brain injury. Journal of Neurosurgical Sciences 50, 2531.Google ScholarPubMed
Drewes, G, Ebneth, A and Mandelkow, EM (1998) MAPs, MARKs and microtubule dynamics. Trends in Biochemical Sciences 23, 307311. https://doi.org/10.1016/s0968-0004(98)01245-6CrossRefGoogle ScholarPubMed
Posmantur, RM et al. (1996) Diminished microtubule-associated protein 2 (MAP2) immunoreactivity following cortical impact brain injury. Journal of Neurotrauma 13, 125137. https://doi.org/10.1089/neu.1996.13.125CrossRefGoogle ScholarPubMed
Kitagawa, K et al. (1989) Microtubule-associated protein 2 as a sensitive marker for cerebral ischemic damage – immunohistochemical investigation of dendritic damage. Neuroscience 31, 401411. https://doi.org/10.1016/0306-4522(89)90383-7CrossRefGoogle ScholarPubMed
Chen, XH et al. (1995) The human NACP/alpha-synuclein gene: chromosome assignment to 4q21.3-q22 and TaqI RFLP analysis. Genomics 26, 425427. https://doi.org/10.1016/0888-7543(95)80237-gCrossRefGoogle ScholarPubMed
Ikonomovic, MD et al. (2004) Alzheimer's pathology in human temporal cortex surgically excised after severe brain injury. Experimental Neurology 190, 192203. https://doi.org/10.1016/j.expneurol.2004.06.011CrossRefGoogle ScholarPubMed
Guingab-Cagmat, JD et al. (2012) In vitro MS-based proteomic analysis and absolute quantification of neuronal-glial injury biomarkers in cell culture system. Electrophoresis 33, 37863797. https://doi.org/10.1002/elps.201200326CrossRefGoogle ScholarPubMed
Jenkins, LW et al. (2002) Conventional and functional proteomics using large format two-dimensional gel electrophoresis 24 hours after controlled cortical impact in postnatal day 17 rats. Journal of Neurotrauma 19, 715740. https://doi.org/10.1089/08977150260139101CrossRefGoogle ScholarPubMed
Wang, H et al. (2021) TMT-based proteomics analysis to screen potential biomarkers of acute-phase TBI in rats. Life Sciences 264, 118631. https://doi.org/10.1016/j.lfs.2020.118631CrossRefGoogle ScholarPubMed
Hinson, HE et al. (2021) Antibody-free mass spectrometry identification of vascular integrity markers in major trauma. Neurotrauma Reports 2, 322329. https://doi.org/10.1089/neur.2021.0007CrossRefGoogle ScholarPubMed
Zhang, XP et al. (2003) Caspase-8 expression and proteolysis in human brain after severe head injury. FASEB Journal 17, 13671369. https://doi.org/10.1096/fj.02-1067fjeCrossRefGoogle ScholarPubMed
Owens, AP, 3rd and Mackman, N (2010) Tissue factor and thrombosis: the clot starts here. Thrombosis and Haemostasis 104, 432439. https://doi.org/10.1160/TH09-11-0771CrossRefGoogle Scholar
Mackman, N (2004) Role of tissue factor in hemostasis, thrombosis, and vascular development. Arteriosclerosis Thrombosis and Vascular Biology 24, 10151022. https://doi.org/10.1161/01.ATV.0000130465.23430.74CrossRefGoogle ScholarPubMed
Eppihimer, MJ and Schaub, RG (2000) P-Selectin-dependent inhibition of thrombosis during venous stasis. Arteriosclerosis Thrombosis and Vascular Biology 20, 24832488. https://doi.org/10.1161/01.atv.20.11.2483CrossRefGoogle ScholarPubMed
Mackman, N (2008) Triggers, targets and treatments for thrombosis. Nature 451, 914918. https://doi.org/10.1038/nature06797CrossRefGoogle ScholarPubMed
Bray, MA et al. (2020) Microvascular thrombosis: experimental and clinical implications. Translational Research 225, 105130. https://doi.org/10.1016/j.trsl.2020.05.006CrossRefGoogle ScholarPubMed
Senchenkova, EY et al. (2010) Angiotensin II-mediated microvascular thrombosis. Hypertension 56, 10891095. https://doi.org/10.1161/HYPERTENSIONAHA.110.158220CrossRefGoogle ScholarPubMed
Juhl, D et al. (2021) Two novel variants in the protein S gene PROS1 are associated with protein S deficiency and thrombophilia. Acta Haematologica 144, 222226. https://doi.org/10.1159/000508525CrossRefGoogle ScholarPubMed
Mansour, E and Isaeva, A (2019) Arterial thrombus in a protein C deficient patient. Cureus Journal of Medical Science 11, e6130. https://doi.org/10.7759/cureus.6130Google Scholar
Andrews, RK et al. (2004) Platelet interactions in thrombosis. IUBMB Life 56, 1318. https://doi.org/10.1080/15216540310001649831CrossRefGoogle ScholarPubMed
Kim, D et al. (2019) Occlusive thrombosis in arteries. APL Bioengineering 3, 041502. https://doi.org/10.1063/1.5115554CrossRefGoogle ScholarPubMed
Li, Z et al. (2009) C-terminal ADAMTS-18 fragment induces oxidative platelet fragmentation, dissolves platelet aggregates, and protects against carotid artery occlusion and cerebral stroke. Blood 113, 60516060. https://doi.org/10.1182/blood-2008-07-170571CrossRefGoogle ScholarPubMed
Nardi, M et al. (2001) Complement-independent, peroxide-induced antibody lysis of platelets in HIV-1-related immune thrombocytopenia. Cell 106, 551561. https://doi.org/10.1016/s0092-8674(01)00477-9CrossRefGoogle ScholarPubMed
ten Cate-Hoek, AJ, Henke, PK and Wakefield, TW (2016) The post thrombotic syndrome: ignore it and it will come back to bite you. Blood Reviews 30, 131137. https://doi.org/10.1016/j.blre.2015.09.002CrossRefGoogle ScholarPubMed
Borgel, D et al. (2019) Inflammation in deep vein thrombosis: a therapeutic target? Hematology (Amsterdam, Netherlands) 24, 742750. https://doi.org/10.1080/16078454.2019.1687144Google ScholarPubMed
Deatrick, KB et al. (2013) Matrix metalloproteinase-9 deletion is associated with decreased mid-term vein wall fibrosis in experimental stasis DVT. Thrombosis Research 132, 360366. https://doi.org/10.1016/j.thromres.2013.06.027CrossRefGoogle ScholarPubMed
Loghmani, H and Conway, EM (2018) Exploring traditional and nontraditional roles for thrombomodulin. Blood 132, 148158. https://doi.org/10.1182/blood-2017-12-768994CrossRefGoogle ScholarPubMed
Ito, T, Kakihana, Y and Maruyama, I (2016) Thrombomodulin as an intravascular safeguard against inflammatory and thrombotic diseases. Expert Opinion on Therapeutic Targets 20, 151158. https://doi.org/10.1517/14728222.2016.1086750CrossRefGoogle ScholarPubMed
Mok, CC, Lau, CS and Wong, RWS (1998) Risk factors for avascular bone necrosis in systemic lupus erythematosus. British Journal of Rheumatology 37, 895900. https://doi.org/10.1093/rheumatology/37.8.895CrossRefGoogle ScholarPubMed
Parks, DA et al. (1982) Ischemic injury in the cat small intestine: role of superoxide radicals. Gastroenterology 82, 915.CrossRefGoogle ScholarPubMed
Rice, JE, 3rd, Vannucci, RC and Brierley, JB (1981) The influence of immaturity on hypoxic-ischemic brain damage in the rat. Annals of Neurology 9, 131141. https://doi.org/10.1002/ana.410090206CrossRefGoogle ScholarPubMed
Simon, RP et al. (1984) Blockade of N-methyl-d-aspartate receptors may protect against ischemic damage in the brain. Science (New York, N.Y.) 226, 850852. https://doi.org/10.1126/science.6093256CrossRefGoogle ScholarPubMed
Pearce, DH et al. (2005) Avascular necrosis of the talus: a pictorial essay. Radiographics 25, 399410. https://doi.org/10.1148/rg.252045709CrossRefGoogle ScholarPubMed
Pijnenburg, L, Felten, R and Javier, RM (2020) A review of avascular necrosis, of the hip and beyond. La Revue de Médecine Interne 41, 2736. https://doi.org/10.1016/j.revmed.2019.10.332CrossRefGoogle ScholarPubMed
Petito, CK and Halaby, IA (1993) Relationship between ischemia and ischemic neuronal necrosis to astrocyte expression of glial fibrillary acidic protein. International Journal of Developmental Neuroscience 11, 239247. https://doi.org/10.1016/0736-5748(93)90082-oCrossRefGoogle ScholarPubMed
Kim, YJ et al. (2000) Immunohistochemical detection of Bax, caspase-3 and Bcl-2 proteins following ischemia in a gerbil model of ischemic tolerance. Neurology, Psychiatry and Brain Research 8, 1722.Google Scholar
Fayaz, SM et al. (2016) Novel RIPK3 inhibitors discovered through a structure-based approach exert post-ischemic neuroprotection. Molecular Diversity 20, 719728. https://doi.org/10.1007/s11030-016-9663-1CrossRefGoogle ScholarPubMed
Lee, YS et al. (2013) Daxx is a key downstream component of receptor interacting protein kinase 3 mediating retinal ischemic cell death. FEBS Letters 587, 266271. https://doi.org/10.1016/j.febslet.2012.12.004CrossRefGoogle ScholarPubMed
Yu, C et al. (2018) FAF1 mediates necrosis through JNK1-mediated mitochondrial dysfunction leading to retinal degeneration in the ganglion cell layer upon ischemic insult. Cell Communication and Signaling 16. https://doi.org/10.1186/s12964-018-0265-7CrossRefGoogle ScholarPubMed
Xi, Y et al. (2021) The role of miR- 206 on regulation of ERK1/2 signaling pathway by targeting Cx43 in rabbit steroid-induced avascular necrosis of femoral head model. Chinese Journal of Clinical Anatomy 39, 154160.Google Scholar
Wang, K et al. (2016) MicroRNA-2861 regulates programmed necrosis in cardiomyocyte by impairing adenine nucleotide translocase 1 expression. Free Radical Biology and Medicine 91, 5867. https://doi.org/10.1016/j.freeradbiomed.2015.11.031CrossRefGoogle ScholarPubMed
Healy, LD et al. (2018) Regulation of immune cell signaling by activated protein C. Journal of Leukocyte Biology 103, 11971203. https://doi.org/10.1002/jlb.3mir0817-338rCrossRefGoogle Scholar
Li, M et al. (2021) Integrative proteome analysis implicates aberrant RNA splicing in impaired developmental potential of aged mouse oocytes. Aging Cell 20, e13482. https://doi.org/10.1111/acel.13482CrossRefGoogle ScholarPubMed
Zhang, G et al. (2010) Overview of peptide and protein analysis by mass spectrometry. Current Protocols in Protein Science 108, 10.21.110.21.30. https://doi.org/10.1002/0471140864.ps1601s62Google Scholar
Fenn, JB et al. (1989) Electrospray ionization for mass spectrometry of large biomolecules. Science (New York, N.Y.) 246, 6471. https://doi.org/10.1126/science.2675315CrossRefGoogle ScholarPubMed
Karas, M et al. (1987) Matrix-assisted ultraviolet laser desorption of non-volatile compounds. International Journal of Mass Spectrometry and Ion Processes 78, 5368. https://doi.org/10.1016/0168-1176(87)87041-6CrossRefGoogle Scholar
Ho, CS et al. (2003) Electrospray ionisation mass spectrometry: principles and clinical applications. The Clinical Biochemist Reviews 24, 312.Google ScholarPubMed
Hillenkamp, F et al. (2012) Matrix-assisted laser desorption/ionization mass spectrometry of biopolymers. Analytical Chemistry 63, 1193A1203A. https://doi.org/10.1021/ac00024a716CrossRefGoogle Scholar
Karas, M and Kruger, R (2003) Ion formation in MALDI: the cluster ionization mechanism. Chemical Reviews 103, 427440. https://doi.org/10.1021/cr010376aCrossRefGoogle ScholarPubMed
Wilfried, MA and Niessen, RACC (2017) Interpretation of mass spectra. In Desiderio, DM and Loo, JA (eds), Interpretation of MS-MS Mass Spectra of Drugs and Pesticides. Hoboken, NJ, USA: Wiley, pp. 5570.Google Scholar
Chen, ZA and Rappsilber, J (2023) Protein structure dynamics by crosslinking mass spectrometry. Current Opinion in Structural Biology 80, 102599. https://doi.org/10.1016/j.sbi.2023.102599CrossRefGoogle ScholarPubMed
Zhang, P et al. (2016) Quantitative proteomics analysis to identify diffuse axonal injury biomarkers in rats using iTRAQ coupled LC-MS/MS. Journal of Proteomics 133, 9399. https://doi.org/10.1016/j.jprot.2015.12.014CrossRefGoogle ScholarPubMed
Zhang, J et al. (2016) Screening and identification of post-traumatic stress-related serum factors in children with Wilms’ tumors. Oncology Letters 11, 12991304. https://doi.org/10.3892/ol.2016.4099CrossRefGoogle ScholarPubMed
Gallagher, SR (2012) One-dimensional SDS gel electrophoresis of proteins. Current Protocols in Molecular Biology 97, 10.2A.110.2A.44. https://doi.org/10.1002/0471142727.mb1002as97CrossRefGoogle Scholar
O'Farrell, PH (1975) High resolution two-dimensional electrophoresis of proteins. Journal of Biological Chemistry 250, 40074021. https://doi.org/10.1016/s0021-9258(19)41496-8CrossRefGoogle ScholarPubMed
Bjellqvist, B et al. (1982) Isoelectric focusing in immobilized pH gradients: principle, methodology and some applications. Journal of Biochemical and Biophysical Methods 6, 317339. https://doi.org/10.1016/0165-022x(82)90013-6CrossRefGoogle ScholarPubMed
Garfin, DE (1990) Isoelectric focusing. Methods in Enzymology 182, 459477. https://doi.org/10.1016/0076-6879(90)82037-3CrossRefGoogle ScholarPubMed
Nowakowski, AB, Wobig, WJ and Petering, DH (2014) Native SDS-PAGE: high resolution electrophoretic separation of proteins with retention of native properties including bound metal ions. Metallomics: Integrated Biometal Science 6, 10681078. https://doi.org/10.1039/c4mt00033aCrossRefGoogle ScholarPubMed
Nigg, PE and Pavlovic, J (2016) Characterization of multi-subunit protein complexes of human MxA using non-denaturing polyacrylamide gel-electrophoresis. JoVE-Journal of Visualized Experiments (116), 54683. https://doi.org/10.3791/54683Google ScholarPubMed
Kemp, G (1998) Capillary electrophoresis: a versatile family of analytical techniques. Biotechnology and Applied Biochemistry 27, 917. https://doi.org/10.1111/j.1470-8744.1998.tb01369.xCrossRefGoogle ScholarPubMed
Zhu, ZF, Lu, JJ and Liu, SR (2012) Protein separation by capillary gel electrophoresis: a review. Analytica Chimica Acta 709, 2131. https://doi.org/10.1016/j.aca.2011.10.022CrossRefGoogle ScholarPubMed
Chu, Y-H et al. (2002) Affinity capillary electrophoresis. Accounts of Chemical Research 28, 461468. https://doi.org/10.1021/ar00059a004CrossRefGoogle Scholar
Konstantinou, GN (2017) Enzyme-linked immunosorbent assay (ELISA). Methods in Molecular Biology 1592, 7994. https://doi.org/10.1007/978-1-4939-6925-8_7CrossRefGoogle ScholarPubMed
Nägler, DK et al. (2006) An enzyme-linked immunosorbent assay for human cathepsin X, a potential new inflammatory marker. Journal of Immunological Methods 308, 241250. https://doi.org/10.1016/j.jim.2005.11.002CrossRefGoogle ScholarPubMed
Meftahi, GH et al. (2021) Applications of western blot technique: from bench to bedside. Biochemistry and Molecular Biology Education 49, 509517. https://doi.org/10.1002/bmb.21516CrossRefGoogle ScholarPubMed
Mishra, M, Tiwari, S and Gomes, AV (2017) Protein purification and analysis: next generation western blotting techniques. Expert Review of Proteomics 14, 10371053. https://doi.org/10.1080/14789450.2017.1388167CrossRefGoogle ScholarPubMed
Huber-Lang, M et al. (2012) Cathepsin D is released after severe tissue trauma in vivo and is capable of generating C5a in vitro. Molecular Immunology 50, 6065. https://doi.org/10.1016/j.molimm.2011.12.005CrossRefGoogle ScholarPubMed
Krafft, C et al. (2012) Raman and coherent anti-Stokes Raman scattering microspectroscopy for biomedical applications. Journal of Biomedical Optics 17, 040801. https://doi.org/10.1117/1.JBO.17.4.040801CrossRefGoogle ScholarPubMed
Raman, CV and Krishnan, KS (1928) A new type of secondary radiation. Nature 121, 501502. https://doi.org/10.1038/121501c0CrossRefGoogle Scholar
Kirsch, M et al. (2010) Raman spectroscopic imaging for in vivo detection of cerebral brain metastases. Analytical and Bioanalytical Chemistry 398, 17071713. https://doi.org/10.1007/s00216-010-4116-7CrossRefGoogle Scholar
Cutshaw, G et al. (2023) The emerging role of Raman spectroscopy as an omics approach for metabolic profiling and biomarker detection toward precision medicine. Chemical Reviews 123, 82978346. https://doi.org/10.1021/acs.chemrev.2c00897CrossRefGoogle ScholarPubMed
Verma, T et al. (2021) Cell-free hemoglobin is a marker of systemic inflammation in mouse models of sepsis: a Raman spectroscopic study. The Analyst 146, 40224032. https://doi.org/10.1039/d1an00066gCrossRefGoogle ScholarPubMed
Effenberger-Neidnicht, K and Hartmann, M (2018) Mechanisms of hemolysis during sepsis. Inflammation 41, 15691581. https://doi.org/10.1007/s10753-018-0810-yCrossRefGoogle ScholarPubMed
Yang, H et al. (2018) Noninvasive and prospective diagnosis of coronary heart disease with urine using surface-enhanced Raman spectroscopy. The Analyst 143, 22352242. https://doi.org/10.1039/c7an02022hCrossRefGoogle ScholarPubMed
Zong, C et al. (2018) Surface-enhanced Raman spectroscopy for bioanalysis: reliability and challenges. Chemical Reviews 118, 49464980. https://doi.org/10.1021/acs.chemrev.7b00668CrossRefGoogle ScholarPubMed
Kradolfer, S et al. (2017) Vibrational changes induced by electron transfer in surface bound azurin metalloprotein studied by tip-enhanced Raman spectroscopy and scanning tunneling microscopy. ACS Nano 11, 1282412831. https://doi.org/10.1021/acsnano.7b07768CrossRefGoogle ScholarPubMed
Alves, RS, Sigoli, FA and Mazali, IO (2020) Aptasensor based on a flower-shaped silver magnetic nanocomposite enables the sensitive and label-free detection of troponin I (cTnI) by SERS. Nanotechnology 31, 505505. https://doi.org/10.1088/1361-6528/abb84fCrossRefGoogle ScholarPubMed
Verma, P (2017) Tip-enhanced Raman spectroscopy: technique and recent advances. Chemical Reviews 117, 64476466. https://doi.org/10.1021/acs.chemrev.6b00821CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Classification of complications of trauma and methods for identifying their target proteins.

Figure 1

Table 1. Biomarkers of post-traumatic sepsis and their biological functions

Figure 2

Table 2. Biomarkers of trauma-related ARDS and their biological functions

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Table 3. Biomarkers of TBI and their biological functions

Figure 4

Figure 2. Process of arterial thrombosis – the thrombin cascade reaction. TF triggers coagulation by forming the FVIIa complex, which activates FX and FIX. FXI can activate FIXa. The prothrombin (PT) complex (FVa: FXa) catalyses the conversion of PT to thrombin. Thrombin then stimulates the activity of various proteases and cofactors. Moreover, thrombin cleaves fibrinogen into soluble monomers, which are subsequently cross-linked by FXIIIa. Activation of a protease-activated receptor on platelets leads to the formation of a blood clot.

Figure 5

Table 4. Biomarkers of post-traumatic ischaemic necrosis and their biological functions

Figure 6

Figure 3. Process of quantifying proteins through MS.

Figure 7

Figure 4. Popular ELISA formats: (A) direct ELISA; (B) indirect ELISA; (C) sandwich ELISA; (D) competitive ELISA with labelled antibody and (E) competitive ELISA with labelled antigen.

Figure 8

Figure 5. Schematic diagram of LFIAs.

Figure 9

Figure 6. Raman spectroscopy revealed cell-free haemoglobin as a marker for systemic inflammation.