Early flow cytometry studies revealed T cell activation in major depressive disorder (MDD). MDD is characterised by activation of the immune-inflammatory response system (IRS) and the compensatory immunoregulatory system (CIRS), including deficits in T regulatory (Treg) cells. This study examines the number of cannabinoid type 1 (CB1) and type 2 (CB2) receptor-bearing T/B lymphocytes in MDD, and the effects of in vitro cannabidiol (CBD) administration on CB1/CB2-bearing immunocytes. Using flow cytometry, we determined the percentage of CD20+CB2+, CD3+CB2+, CD4+CB2+, CD8+CB2+ and FoxP3+CB1+ cells in 19 healthy controls and 29 MDD patients in 5 conditions: baseline, stimulation with anti-CD3/CD28 with or without 0.1 µg/mL, 1.0 µg/mL, or 10.0 µg/mL CBD. CB2+ was significantly higher in CD20+ than CD3+ and CD4+ and CD 8+ cells. Stimulation with anti-CD3/CD8 increases the number of CB2-bearing CD3+, CD4+ and CD8+ cells, as well as CB1-bearing FoxP3+ cells. There was an inverse association between the number of reduced CD4+ CB2+ and IRS profiles, including M1 macrophage, T helper-(Th)-1 and Th-17 phenotypes. MDD is characterised by lowered basal FoxP3+ CB1+% and higher CD20+ CB2+%. 33.2% of the variance in the depression phenome (including severity of depression, anxiety and current suicidal behaviours) is explained by CD20+ CB2+ % (positively) and CD3+ CB2+% (inversely). All five immune cell populations were significantly increased by 10 µg/mL of CBD administration. Reductions in FoxP3+ CB1+% and CD3+ /CD4+ CB2+% contribute to deficits in immune homoeostasis in MDD, while increased CD20+CB2+% may contribute to the pathophysiology of MDD by activating T-independent humoral immunity.

As COVID-19 continues, a safe, cost-effective treatment strategy demands continued inquiry. Chronic neuroinflammatory disorders may appear to be of little relevance in this regard; often indolent and progressive disorders characterized by neuroinflammation (such as Alzheimer’s disease (AD)) are fundamentally dissimilar in etiology and symptomology to COVID-19’s rapid infectivity and pathology. However, the two disorders share extensive pathognomonic features, including at membrane, cytoplasmic, and extracellular levels, culminating in analogous immunogenic destruction of their respective organ parenchyma. We hypothesize that these mechanistic similarities may extent to therapeutic targets, namely that it is conceivable an agent against AD’s immunopathy may have efficacy against COVID-19 and vice versa. It is notable that while extensively investigated, no agent has yet demonstrated significant therapeutic efficacy against AD’s cognitive and memory declines. Yet this very failure has driven the development of numerous agents with strong mechanistic potential and clinical characteristics. Having already approved for clinical trials, these agents may be an expedient starting point in the urgent search for an effective COVID-19 therapy. Herein, we review the overlapping Alzheimer’s/ COVID-19 targets and theorize several initial platforms.

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.

The purpose of this study is to describe how to use the precision nomothetic psychiatry approach to (a) delineate the associations between schizophrenia symptom domains, including negative symptoms, psychosis, hostility, excitation, mannerism, formal thought disorders, psychomotor retardation (PHEMFP), and cognitive dysfunctions and neuroimmunotoxic and neuro-oxidative pathways and (b) create a new endophenotype class based on these features. We show that all symptom domains (negative and PHEMFP) may be used to derive a single latent trait called overall severity of schizophrenia (OSOS). In addition, neurocognitive test results may be used to extract a general cognitive decline (G-CoDe) index, based on executive function, attention, semantic and episodic memory, and delayed recall scores. According to partial least squares analysis, the impacts of adverse outcome pathways (AOPs) on OSOS are partially mediated by increasing G-CoDe severity. The AOPs include neurotoxic cytokines and chemokines, oxidative damage to proteins and lipids, IgA responses to neurotoxic tryptophan catabolites, breakdown of the vascular and paracellular pathways with translocation of Gram-negative bacteria, and insufficient protection through lowered antioxidant levels and impairments in the innate immune system. Unsupervised machine learning identified a new schizophrenia endophenotype class, named major neurocognitive psychosis (MNP), which is characterised by increased negative symptoms and PHEMFP, G-CoDe and the above-mentioned AOPs. Based on these pathways and phenome features, MNP is a distinct endophenotype class which is qualitatively different from simple psychosis (SP). It is impossible to draw any valid conclusions from research on schizophrenia that ignores the MNP and SP distinctions.