Discover mental health最新文献

Introduction: Volunteering has reported health benefits. However, little is known in Portugal about the views of mental health professionals and volunteers on volunteering in mental health care.

Methods: A qualitative secondary analysis of data from six focus groups with 28 participants was conducted in order to explore and compare the perspectives on volunteering in mental health of two stakeholders: mental health professionals and volunteers in Portugal.

Results: Four main themes arose: the nature of the volunteering relationship; volunteering has multiple aims; technology has potential for volunteering; and volunteering has its challenges. Although there were mostly commonalities between their views, some variability suggested that different stakeholders may consider different aspects of volunteering differently. Overall, stakeholders called for structured recruitment and support, training, defining boundaries and fighting the stigma of mental illness.

Conclusion: Despite the lack of volunteering tradition in mental health care in Portugal, volunteering programmes were perceived as an important resource for patients with mental illness.

Popular culture and medical lore have long postulated a connection between full moon and exacerbations of psychiatric disorders. We wanted to empirically analyze the hypothesis that suicides are increased during the period around full moons. We analyzed pre-COVID suicides from the Marion County Coroner's Office (n = 776), and show that deaths by suicide are significantly increased during the week of the full moon (p = 0.037), with older individuals (age ≥ 55) showing a stronger effect (p = 0.019). We also examined in our dataset which hour of the day (3-4 pm, p = 0.035), and which month of the year (September, p = 0.09) show the most deaths by suicide. We had blood samples on a subset of the subjects (n = 45), which enabled us to look at possible molecular mechanisms. We tested a list of top blood biomarkers for suicidality (n = 154) from previous studies of ours ⁷, to assess which of them are predictive. The biomarkers for suicidality that are predictive of death by suicide during full moon, peak hour of day, and peak month of year, respectively, compared to outside of those periods, appear to be enriched in circadian clock genes. For full moon it is AHCYL2, ACSM3, AK2, and RBM3. For peak hour it is GSK3B, AK2, and PRKCB. For peak month it is TBL1XR1 and PRKCI. Half of these genes are modulated in expression by lithium and by valproate in opposite direction to suicidality, and all of them are modulated by depression and alcohol in the same direction as suicidality. These data suggest that there are temporal effects on suicidality, possibly mediated by biological clocks, pointing to changes in ambient light (timing and intensity) as a therapeutically addressable target to decrease suicidality, that can be coupled with psychiatric pharmacological and addiction treatment preventive interventions.

Background: Adult hippocampal neurogenesis (AHN) is a clinically significant neural phenomenon. Understanding its molecular regulation would be important. In this regard, most studies have focused on transcriptional regulators (TRs), epigenetic modifiers, or non-coding RNAs. RNA-binding proteins (RBPs) have emerged as dominant molecular regulators. It would be significant to understand the potential cross-talk between RBPs and TRs, which could influence AHN.

Methods: The present study employed computational analyses to identify RBPs and TRs regulating AHN, followed by the analysis of their interaction networks and detection of hub proteins. Next, the potential mutual regulation of hub TRs and RBPs was analyzed. Additionally, hippocampal genes differentially expressed upon exercise were analyzed for potential regulation by the identified TRs and RBPs.

Results: 105 TRs and 26 RBPs were found to influence AHN, which could also form interactive networks. Polycomb complex proteins were among the TR network hubs, while HNRNP and SRSF family members were among the hub RBPs. Further, the polycomb complex proteins and SRSF1 could have a mutual regulatory relationship, suggesting a cross-talk between epigenetic/transcriptional and post-transcriptional regulatory pathways. A number of exercise-induced hippocampal genes were also found to be potential targets of the identified TRs and RBPs.

Conclusion: SRSF1 may influence post-transcriptional stability, localization, and alternative splicing patterns of polycomb complex transcripts, and the polycomb proteins may in turn epigenetically influence the SRSF1. Further experimental validation of these regulatory loops/networks could provide novel insights into the molecular regulation of AHN, and unravel new targets for disease-treatment.

Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.

There is a crisis in mental health care, with more people suffering from psychiatric disorders than resources that are available for treatment, even though spending is substantial. Millions who suffer from addiction, psychosis, depression and suicidality are either untreated or inadequately treated and organized psychiatry is unable to reach them. Possibly as reflection of under-treatment of psychiatric disorders, the rates of suicide have risen: from 1999 through 2014, the age-adjusted suicide rate in the US increased 24%, from 10.5 to 13.0 per 100,000. Assessment of psychiatric symptoms in ongoing outpatient settings is costly, inadequate and unable to detect clinical changes over time. One's digital phenotype is assessed through footprints left over as result of our interface with technology, including automated assessments of quantity and quality of social media activity, patterns and speed of device usage, and physiological data that is automatically collected, such as location, quantity and type of movement, heart rate, and sleep patterns. The use of digital footprints has been advocated for large-scale data collection that can facilitate psychiatric research in naturalistic settings. We highlight recent papers in Discover Mental Health addressing digital approaches to mental health and we also advance here the concept that digital footprints are ready for clinical use. However, before that happens there needs to be discussion on the appropriate boundaries between care that is driven by signals from digital footprints and the rights to privacy and self-determination.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent psychiatric condition characterized by developmentally inappropriate symptoms of inattention and/or hyperactivity/impulsivity, which leads to impairments in the social, academic, and professional contexts. ADHD diagnosis relies solely on clinical assessment based on symptom evaluation and is sometimes challenging due to the substantial heterogeneity of the disorder in terms of clinical and pathophysiological aspects. Despite the difficulties imposed by the high complexity of ADHD etiology, the growing body of research and technological advances provide good perspectives for understanding the neurobiology of the disorder. Such knowledge is essential to refining diagnosis and identifying new therapeutic options to optimize treatment outcomes and associated impairments, leading to improvements in all domains of patient care. This review is intended to be an updated outline that addresses the etiological and neurobiological aspects of ADHD and its treatment, considering the impact of the "omics" era on disentangling the multifactorial architecture of ADHD.

Background: Pharmacotherapy for most psychiatric conditions was developed from serendipitous observations of benefit from drugs prescribed for different reasons. An algorithmic approach to formulating pharmacotherapy is proposed, based upon which combination of changed activities by brain cell-types is dominant for any particular condition, because those cell-types contain and surrogate for genetic, metabolic and environmental information, that has affected their function. The algorithm performs because functions of some or all the affected cell-types benefit from several available drugs: clemastine, dantrolene, erythropoietin, fingolimod, fluoxetine, lithium, memantine, minocycline, pioglitazone, piracetam, and riluzole PROCEDURES/FINDINGS: Bipolar disorder, major depressive disorder, schizophrenia, Alzheimer's disease, and post-traumatic stress disorder, illustrate the algorithm; for them, literature reviews show that no single combination of altered cell-types accounts for all cases; but they identify, for each condition, which combination occurs most frequently, i.e., dominates, as compared with other possible combinations. Knowing the dominant combination of altered cell-types in a particular condition, permits formulation of therapy with combinations of drugs taken from the above list. The percentage of patients who might benefit from that therapy, depends upon the frequency with which the dominant combination occurs in patients with that particular condition.

Conclusions: Knowing the dominant combination of changed cell types in psychiatric conditions, permits an algorithmically formulated, rationally-based treatment. Different studies of the same condition often produce discrepant results; all might be correct, because identical clinical phenotypes result from different combinations of impaired cell-types, thus producing different results. Clinical trials would validate both the proposed concept and choice of drugs.

Objective: Children and young people (CYP) from low-and-middle-income and developing countries are at risk of poor mental health and wellbeing. Yet these regions are generally under-resourced in terms of mental health services. As a first step to inform service planning and delivery in the English-speaking Caribbean we pooled the available evidence to estimate the prevalence of common mental health problems.

Methods: A comprehensive search of CINAHL, Cochrane Library, EMBASE, MEDLINE, PsycINFO, LILACS, and Web of Science databases, supplemented by grey literature searches was performed until January 2022. Studies conducted in the English-speaking Caribbean that reported prevalence estimates of mental health symptomology or diagnoses in CYP were included. The Freeman-Tukey transformation was applied to calculate the weighted summary prevalence under a random-effects model. Subgroup analyses were also performed to observe emerging patterns in the data. Studies were quality assessed using the Joanna Briggs Institute Prevalence Critical Appraisal Checklist and the GRADE approach. The study protocol was registered with PROSPERO, CRD42021283161.

Results: 33 publications from 28 studies representing 65,034 adolescents from 14 countries met the eligibility criteria. Prevalence estimates ranged from 0.8 to 71.9% with most subgroup estimates between 20 and 30%. The overall pooled prevalence of mental health problems was 23.5% (95% CI 0.175-0.302; I² = 99.7%). There was limited evidence of significant variation in prevalence estimates among subgroups. The quality of the body of evidence was judged as moderate.

Conclusion: It is estimated that between 1 in 4 and 1 in 5 adolescents in the English-speaking Caribbean experience symptoms of mental health problems. These findings highlight the importance of sensitisation, screening, and provision of appropriate services. Ongoing research identifying risk factors and validating outcome measures is also needed to inform evidence-based practice.

Supplementary information: The online version contains supplementary material available at 10.1007/s44192-023-00037-2.

This paper examines the impact of COVID-19 on the mental health of people from different socioeconomic classes. This has been done by creating an anxiety score, based on responses to a set of questions that were asked as part of a two-round telephonic survey done by Young Lives Data for India. Using this index, anxiety levels have been classified as high, medium, and low. As the dependent variable has an ordered nature, an ordered logit model has been used for regression. According to the results, job loss, death of the family's earning member or mishap in the family, and price increases all contributed to increased anxiety. The analysis indicates that anxiety levels among women were higher than among men because of the increased burden of household chores and childcare responsibilities. Also, anxiety levels were higher among those who perceived themselves as rich or poor as opposed to those who were comfortable in their income group. There was a higher anxiety level among the rich due to income loss and increased household responsibilities. A high percentage of children and elders reported feeling anxious; school closures and a lack of social interaction caused stress. Isolation also plagued the elderly. Furthermore, many students couldn't avail online learning opportunities due to a lack of resources. The government implemented many policies to mitigate these issues, which included those to mitigate the immediate hunger problem.

Purposes: (1) To summarize the mental conditions that may accompany persistent symptoms following acute infection by SARS-CoV-2, often termed Long Covid; (2) to formulate treatment based upon the brain cells that are dominantly affected.

Methods: (1) Review the reports relating to the mental symptoms occurring in Long Covid. (2) Review the drugs that address the brain cells affected in Long Covid, and suggest pharmacotherapy for those patients whose response to psychotherapy is suboptimal.

Results: Long Covid affects ~ 10% of patients infected by SARS-CoV-2, and mental symptoms affect ~ 20% of persons with Long Covid. The brain cell-types that have been demonstrated as dominantly affected in Long Covid are astrocytes, oligodendrocytes, neurons, endothelial cells/pericytes, and microglia. Lithium and fluoxetine each address all of those four cell-types. Low dosage of each is likely to be well-tolerated and to cause neither clinically important adverse events (AE) nor serious adverse events (SAE).

Conclusion: For those patients whose response to psychotherapy is suboptimal, lithium and fluoxetine should be administered in combination for both depth of benefit and reduction of dosages.