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Interstitial pneumonia was the first manifestation to be recognized as caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, in just a few weeks, it became clear that the coronavirus disease-2019 (COVID-19) overrun tissues and more body organs than just the lungs, so much so that it could be considered a systemic pathology. Several studies reported the involvement of the conjunctiva, the gut, the heart and its pace, and vascular injuries such as thromboembolic complications and Kawasaki disease in children and toddlers were also described. More recently, it was reported that in a sample of 214 SARS-CoV-2 positive patients, 36.4% complained of neurological symptoms ranging from non-specific manifestations (dizziness, headache, and seizures), to more specific symptoms such hyposmia or hypogeusia, and stroke. Older individuals, especially males with comorbidities, appear to be at the highest risk of developing such severe complications related to the Central Nervous System (CNS) involvement. Neuropsychiatric manifestations in COVID-19 appear to develop in patients with and without pre-existing neurological disorders. Growing evidence suggests that SARS-CoV-2 binds to the human Angiotensin-Converting Enzyme 2 (ACE2) for the attachment and entrance inside host cells. By describing ACE2 and the whole Renin Angiotensin Aldosterone System (RAAS) we may better understand whether specific cell types may be affected by SARS-CoV-2 and whether their functioning can be disrupted in case of an infection. Since clear evidences of neurological interest have already been shown, by clarifying the topographical distribution and density of ACE2, we will be able to speculate how SARS-CoV-2 may affect the CNS and what is the pathogenetic mechanism by which it contributes to the specific clinical manifestations of the disease. Based on such evidences, we finally hypothesize the process of SARS-CoV-2 invasion of the CNS and provide a possible explanation for the onset or the exacerbation of some common neuropsychiatric disorders in the elderly including cognitive impairment and Alzheimer disease. [ABSTRACT FROM AUTHOR]

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy. [ABSTRACT FROM AUTHOR]

Schizophrenia is a psychotic disorder characterized by delusions, hallucinations, negative symptoms, as well as behavioral and cognitive dysfunction. It is a pathoetiologically heterogeneous disorder involving complex interrelated mechanisms that include oxidative stress and neuroinflammation. Neurovascular endothelial dysfunction and blood-brain barrier (BBB) hyperpermeability are established mechanisms in neurological disorders with comorbid psychiatric symptoms such as epilepsy, traumatic brain injury, and Alzheimer's disease. Schizophrenia is frequently comorbid with medical conditions associated with peripheral vascular endothelial dysfunction, such as metabolic syndrome, cardiovascular disease, and diabetes mellitus. However, the existence and etiological relevance of neurovascular endothelial dysfunction and BBB hyperpermeability in schizophrenia are still not well recognized. Here, we review the growing clinical and experimental evidence, indicating that neurovascular endotheliopathy and BBB hyperpermeability occur in schizophrenia patients. We present a theoretical integration of human and animal data linking oxidative stress and neuroinflammation to neurovascular endotheliopathy and BBB breakdown in schizophrenia. These abnormalities may contribute to the cognitive and behavioral symptoms of schizophrenia via several mechanisms involving reduced cerebral perfusion and impaired homeostatic processes of cerebral microenvironment. Furthermore, BBB disruption can facilitate interactions between brain innate and peripheral adaptive immunity, thereby perpetuating harmful neuroimmune signals and toxic neuroinflammatory responses, which can also contribute to the symptoms of schizophrenia. Taken together, these findings support the "mild encephalitis" hypothesis of schizophrenia. If neurovascular abnormalities prove to be etiologically relevant to the neurobiology of schizophrenia, then targeting these abnormalities may represent a promising therapeutic strategy. [ABSTRACT FROM AUTHOR]