Current Psychiatry Research and Reviews - Volume 19, Issue 3, 2023

Depression, or major depressive disorder, is a mental illness that significantly affects psychosocial functioning and reduces the quality of ones life. The annual incidence of depression throughout the globe is around 6%. The disorder should be diagnosed at a particular stage for the treatment to be designed. Biomarkers can help to do so with objective pieces of evidence. Various biomarkers like Imaging biomarkers, Molecular biomarkers, Transcriptomic biomarkers, Genetic biomarkers, Neuroendocrine, and Inflammatory biomarkers can be used to diagnose depression. The use of digital sensors has also been reported recently for the determination of depression. This review summarizes various biomarkers to diagnose depression. Further recent updates and related clinical trials are included.

The coronavirus pandemic of 2019 has resulted in extensive social regulations and affected many aspects of life. It has led to significant stress and adversely impacted mental health across the globe. The virus has been found to directly increase neuropsychiatric sequelae in those affected. Various psychosocial factors have also increased the incidence and prevalence of mental health problems worldwide. There was a need for a ramp-up of psychiatric services to support individuals in such a situation. Even after the pandemic, there is a need for improving access to mental health services for the mentally ill as well as those affected by the regulations brought about to tackle the pandemic. Telepsychiatric services are in place throughout the world in different forms and are the answer to bridging the mental health gap during and in the aftermath of the pandemic. Hence, it is important to continue developing and enhancing tele psychiatric services in different countries for supporting and treating individuals affected by the pandemic.

Background:The outbreak of the COVID-19 pandemic, the constant transformation of the SARS-COV-2 virus form, exposure to substantial psychosocial stress, environmental change, and isolation have led to the inference that the overall population's mental health could be affected, resulting in an increase in cases of psychosis. Objective: We initiated a systematic review to determine the impact of the SARS-COV-2 virus and its long-term effects - in both symptomatic and asymptomatic cases - on people with or without psychosis. We envisioned that this would give us an insight into effective clinical intervention methods for patients with psychosis during and after the pandemic. Methods: We selected fifteen papers that met our inclusion criteria, i.e., those that considered participants with or without psychiatric illness and exposed to SARS-COV-2 infection, for this review and were retrieved via Google, Google Scholar, MEDLINE, PubMed, and PsychINFO Database. Key Gap: There is a dearth of research in understanding how COVID-19 affects people with or without a prior personal history of psychosis. Results: The systematic review summary provides insight into the state of knowledge. Insights from the systematic review have also been reviewed from the salutogenesis model's perspective. There is moderate evidence of new-onset psychosis during the COVID-19 pandemic in which some antipsychotics treated the psychotic symptoms of patients while treating for COVID-19. Suggestions and recommendations are made for preventive and promotive public health strategies. Conclusion: The Salutogenesis model and Positive Psychology Interventions (PPI) provide another preventive and promotive public health management approach.

Background: Borderline personality disorder (BPD) is a clinical condition associated with numerous individual and collective negative consequences. According to several etiological theories and retrospective research, child maltreatment (CM) may be considered a central factor explaining BPD development. Objective: In order to verify this hypothesis, a systematic review of prospective longitudinal studies was conducted. Methods: Following searches in five electronic databases, 19 articles that examined the relationship between CM (i.e., physical, sexual and emotional abuse; physical and emotional neglect) and BPD (i.e., diagnosis or severity score) were selected. Results: Overall, the results only partly confirm the hypothesis that CM is a risk factor for BPD. Evidence for a prospective relationship between CM and later BPD is stronger in studies using a symptom count compared to a categorical diagnosis. However, the small number of studies precludes assessing the differential impacts between CM types and BPD. Conclusion: Available prospective longitudinal studies do not unequivocally support the idea that CM is a robust risk factor for BPD. Future research needs are discussed.

Background: Substance use disorders are highly prevalent among psychotic patients and are associated with poorer clinical outcomes. Objective: This study aimed to investigate the prevalence of psychotic disorders in substance users and the epidemiological features of this population. Materials and Methods: In a cross-sectional study, we investigated 455 outpatient and inpatient participants with SUD referred to the Iran psychiatric hospital from April, 2020, to March, 2021. All participants were substance users, referred to Iran psychiatric center to follow up on their comorbidities and psychiatric disorders. The Structured Clinical Interview for DSM-5 (SCID) was used for psychotic disorders and substance use disorder diagnoses. Finally, gathered data were analyzed by SPSS-25. Results: A total of 455 patients were involved in the study. The mean age was 34.66 years, of which 89.7% were men. The most common main substances include methamphetamine (39.1%) and heroin (27%). The prevalence of psychotic disorder was 36.7%, and the most common type was substance-induced psychotic disorder (26.4%) and schizophrenia (8.4%). There was a significant association between methamphetamine and opium use and the presence of substance-induced psychotic disorder and schizophrenia, respectively (p #130; 0.001). Conclusion: The most common type of substance use was methamphetamine, and the most common type of psychotic disorder was substance-induced psychotic disorder. There was a significant association between methamphetamine and opium use and the presence of substance- induced psychotic disorder and schizophrenia, respectively. It should be noted that this was a sample of individuals hospitalized for substance abuse. It was not a general population sample and was very biased toward substance use.

Aim: This study aims to examine mechanisms that may account for the comorbidity between social anxiety and depression. We hypothesized that maladaptive metacognitions and anhedonia would serially mediate the link between social anxiety and depression. Methods: We tested this notion by collecting data from 208 international, English-speaking participants ranging from the ages of 18 to 65 years old using the Social Interaction Anxiety Scale (SIAS), Self-Assessment Anhedonia Scale (SAAS), Patient’s Health Questionnaire-9 (PHQ-9), and Metacognition Questionnaire (MCQ-30). Results: The results show a significant serial mediation effect from social anxiety via beliefs about uncontrollability and danger and anhedonia to depression, with the pathway from social anxiety via reduced beliefs in cognitive confidence and anhedonia to depression not being significant. Furthermore, a direct path from social anxiety to depression was also found to be significant. Conclusion: Findings support both the tripartite model and the self-regulatory executive function model of anxiety and depression. While the findings imply the contributions of other mediating variables, they also indicate that metacognitive therapy with an additional focus on anhedonia could be effective in preventing the development of depression.

Background: Stressors have a huge impact on one’s well-being. They affect individual’s mental and physical health, if untreated. The response to these stresses is termed as a stress response. Based on the type and severity of the stimulus, stress can affect the various actions and functioning of the body. This explains how important it becomes to detect the level of stress and treat it well. The best treatment for stress is to identify the factors causing stress and eliminate them in the initial stage. Various methods have been proposed to detect the level of stress. One of the common methods is by using wearable devices to capture EEG signals and use various proposed algorithms to detect the level of stress. However, there are cases where stress cannot be captured by non-invasive technologies. Besides, these technologies cannot determine the stress-causing factors. This paper proposes a methodology to cater to such cases and identify the factors causing stress in the patient. It can also act as a front line methodology to detect if the candidate is suffering from anxiety or stress. The use of fuzzy logic in various healthcare areas has become very evident. This is because it deals with a range of values. While, Petri nets is a network where the arc runs from place to transition and not between places and transitions. It is the best model to use in dynamic and concurrent activities of the system. Thus, a combination of these two logics can provide an extremely competent basis for the implementation of computing reasoning processes and the modeling of systems with uncertainty. Thus, Fuzzy Petri Nets (FPN) have been proposed. This paper proposes the use of FPN in designing a methodology for factors responsible for causing stress and advancing the level of stress in the patient. The methodology is developed by observing the process of food transfer in plants. The authors have also discussed afferent and efferent stress paths. Methods: The methodology proposed in this paper uses Fuzzy Petri Net. The algorithm designed in this paper has been named as the Sprouting tree algorithm by the authors. Designing the fault tree is the first and very important step for the correct determination of the level of the stress. The score generated using the Hamilton scale is fed as input to the AND/OR gate system to receive the value of stressor, and thus, drawing a fault tree. The transformation rules are applied to convert the fault tree into the FPN. Then, we derive production rules and reachability matrix. These rules help in normalizing the value obtained via fault tree so that they lie in the range of fuzzy logic. FPN calculates the certainty factor (CF), which represents the state of stress in an individual. Therefore, the values obtained from FPN will finally build a tree, which is named as Sprouting tree. Conclusion: The methodology proposed in this paper is absolutely new to detecting the stress. The future of this work is to observe the accuracy of the proposed algorithm by implementing it with real data, which is under process.