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Motivation: Bipolar disorder (BD) and schizophrenia (SZ) has a difficult diagnosis, so the main objective of this article is to propose the use of Artificial Neural Networks (ANNs) to classify (diagnose) groups of patients with BD or SZ from a control group using sociodemographic and biochemical variables. Methods: Artificial neural networks are used as classifying tool. The data from this study were obtained from the array collection from Stanley Neuropathology Consortium databank. Inflammatory markers and characteristics of the sampled population were the inputs variables. Results: Our findings suggest that an artificial neural network could be trained with more than 90% accuracy, aiming the classification and diagnosis of bipolar, schizophrenia and control healthy group. Conclusion: Trained ANNs could be used to improve diagnosis in Schizophrenia and Bipolar disorders. upon the activation function [1]. The ANN inputs are multiplied by different weights to generate a predictive response. So, these responses in ANN are widely used for several applications such as classification and pattern recognition [2].This tool is effective modeling non-linear relationships that may be a promising candidate for differentiation for several biological processes [3]. ANN are used in medical field to analysis of sleep disorders, cytopathology and histopathology

Our findings showed a higher prevalence of suicide risk in individuals with PTSD and support the hypothesis that the investigation of childhood traumatic experiences, especially emotional neglect and abuse, may help in the early detection of suicide risk in individuals with PTSD.

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AimWe aimed to identify whether lifetime cocaine use is a risk factor for conversion from major depressive disorder (MDD) to bipolar disorder (BD) in an outpatient sample of adults.MethodsThis prospective cohort study included 585 subjects aged 18 to 60 years who had been diagnosed with MDD as assessed by the Mini International Neuropsychiatric Interview (MINI‐Plus) at baseline (2012–2015). Subjects were reassessed a mean of 3 years later (2017–2018) for potential conversion to BD as assessed by the MINI‐Plus. Lifetime cocaine use was assessed using the Alcohol, Smoking, and Substance Involvement Screening Test.ResultsIn the second wave, we had 117 (20%) losses, and 468 patients were reassessed. The rate of conversion from MDD to BD in 3 years was 12.4% (n = 58). A logistic regression analysis showed that the risk for conversion from MDD to BD was 3.41‐fold higher (95% confidence interval, 1.11–10.43) in subjects who reported lifetime cocaine use at baseline as compared to individuals who did not report lifetime cocaine use at baseline, after adjusting for demographic and clinical confounders.ConclusionThese findings showed that lifetime cocaine use is a potential predictor of conversion to BD in an MDD cohort. Further studies are needed to assess the possible underlying mechanisms linking exposure to cocaine with BD conversion.

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