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Background: Many studies have investigated the efficacy of acupuncture in treating depression, but the mechanism of acupuncture for depression is still controversial and there is a lack of meta-analysis of mechanisms. Consequently, we investigated acupuncture's efficacy and mechanism of depression. Methods: We searched the Cochrane Library, PubMed, EMBASE, Web of Science. The SYRCLE Risk of Bias Tool was used to assess bias risk. Meta-analysis was performed using Stata 15.0 for indicators of depression mechanisms, body weight and behavioral tests. Results: A total of 22 studies with 497 animals with depressive-like behaviors were included. Meta-analysis showed that acupuncture significantly increased BDNF [SMD = 2.40, 95% CI (1.33, 3.46); I² = 86.6%], 5-HT [SMD = 2.28, 95% CI (1.08, 3.47); I² = 87.7%] compared to the control group (p < 0.05), and significantly reduced IL-1ß [SMD = -2.33, 95% CI (-3.43, -1.23); I² = 69.6%], CORT [SMD = -2.81, 95% CI (-4.74, -0.87); I² = 86.8%] (p < 0.05). Acupuncture improved body weight [SMD = 1.35, 95% CI (0.58, 2.11); I² = 84.5%], forced swimming test [SMD = -1.89, 95% CI (-2.55, -1.24); I² = 76.3%], open field test (crossing number [SMD = 3.08, 95% CI (1.98, 4.17); I² = 86.7%], rearing number [SMD = 2.53, 95% CI (1.49, 3.57); I² = 87.0%]) (p < 0.05) compared to the control group. Conclusion: Acupuncture may treat animals of depressive-like behaviors by regulating neurotrophic factors, neurotransmitters, inflammatory cytokines, neuroendocrine system. [ABSTRACT FROM AUTHOR]

Bipolar disorder ( BD ) is a class of common psychiatric disorders, and its high morbidity, disability, and mortality have attracted widespread attention. However, in clinical practice, the initial accurate diagnosis rate of BD is low and easily misdiagnosed as monophasic depression. Many neuroimaging studies have shown that cortical thickness, gray matter, white matter, and functional activities are altered in some brain regions of BD patients. However, their specific neuroimaging indexes have not been clarified, and the specific pathophysiological mechanisms for the onset of BD have not been fully elucidated. Therefore, in this paper, we combed through the recent years of BD patients to study the cortical structure and perfusion of the brain to review the methods in anticipation of more in-depth research at a later stage. [ABSTRACT FROM AUTHOR]

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD. [ABSTRACT FROM AUTHOR]

This document is a compilation of various scientific articles and studies that explore the potential link between neuroinflammation and schizophrenia. It discusses the impact of prenatal maternal immune activation on the increased risk of developing schizophrenia later in life. The vulnerability-stress-inflammation model and the two-hit model of schizophrenia are presented, highlighting the role of inflammation and immune dysregulation in the development of the disorder. The document also discusses the presence of elevated levels of proinflammatory markers in individuals with schizophrenia and the potential use of biomarkers for diagnosis and treatment. It emphasizes the importance of ongoing research and collaboration in understanding the role of immune dysregulation in schizophrenia and the potential for biomarkers to improve diagnosis and treatment. [Extracted from the article]

To enhance the bioavailability and antihypertensive effect of the anti-depressant drug citalopram hydrobromide (CTH) we developed a sustained-release transdermal delivery system containing CTH. A transdermal diffusion meter was first used to determine the optimal formulation of the CTH transdermal drug delivery system (TDDS). Then, based on the determined formulation, a sustained-release patch was prepared; its physical characteristics, including quality, stickiness, and appearance, were evaluated, and its pharmacokinetics and irritation to the skin were evaluated by applying it to rabbits and rats. The optimal formulation of the CTH TDDS was 49.2% hydroxypropyl methyl cellulose K100M, 32.8% polyvinylpyrrolidone K30, 16% oleic acid-azone, and 2% polyacrylic acid resin II. The system continuously released an effective dose of CTH for 24 h and significantly enhanced its bioavailability, with a higher area under the curve, good stability, and no skin irritation. The developed CTH TDDS possessed a sustained-release effect and good characteristics and pharmacokinetics; therefore, it has the potential for clinical application as an antidepressant. [ABSTRACT FROM AUTHOR]

Introduction: In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI). Methods: A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score. Results and discussion: The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is a severe psychiatric disorder characterized by selective impairments in mood regulation, cognition and behavior. Although it is well-known that antidepressants can effectively treat moderate to severe depression, the biochemical effects of these medications on white matter (WM) integrity are still unclear. Therefore, the aim of the study is to review the main scientific evidence on the differences in WM integrity in responders and nonresponders to antidepressant medications. A record search was performed on three datasets (PubMed, Scopus and Web of Science) and ten records matched our inclusion criteria. Overall, the reviewed studies highlighted a good efficacy of antidepressants in MDD treatment. Furthermore, there were differences in WM integrity between responders and non-responders, mainly localized in cingulate cortices, hippocampus and corpus callosum, where the former group showed higher fractional anisotropy and lower axial diffusivity values. Modifications in WM integrity might be partially explained by branching and proliferation as well as neurogenesis of axonal fibers mediated by antidepressants, which in turn may have positively affected brain metabolism and increase the quantity of the serotonergic neurotransmitter within synaptic clefts. However, the reviewed studies suffer from some limitations, including the heterogeneity in treatment duration, antidepressant administration, medical posology, and psychiatric comorbidities. Therefore, future studies are needed to reduce confounding effects of antidepressant medications and to adopt longitudinal and multimodal approaches in order to better characterize the differences in WM integrity between responders and non-responders. [ABSTRACT FROM AUTHOR]

In the twenty years since the publication of the most widely used treatment manuals describing evidence-based therapies for eating disorders, there have been some substantial advances in the field. New methods of delivering treatments have been trialled and our perception of mental health has advanced; significant cultural changes have led to shifts in our societal landscape; and new technologies have allowed for more in-depth research to be conducted. As a result, our understanding of eating disorders and their treatment has broadened considerably. However, these new insights have not necessarily been translated into improved clinical practice. This paper highlights the changes we consider to have had the greatest impact on our work as experienced clinical psychologists in the field and suggests a list of new learnings that might be incorporated into clinical practice and research design. Plain English summary: In the field of eating disorders the most commonly used manualised treatments are nearly twenty years old. There has been much progress in the field since then in terms of technologies, understandings and social changes. In this paper, two experienced clinical psychologists describe some of the more recent developments in the field and highlight ways to incorporate the new learnings into clinical practice and research design. [ABSTRACT FROM AUTHOR]

Background: In recent years, accelerated transcranial magnetic stimulation (aTMS) has been developed, which has a shortened treatment period. The aim of this study was to evaluate the efficacy and long-term maintenance effects of aTMS in patients with major depressive disorder (MDD). Methods: We systematically searched online databases for aTMS studies in patients with MDD published before February 2023 and performed a meta-analysis on the extracted data. Results: Four randomized controlled trials (RCTs) and 10 before-and-after controlled studies were included. The findings showed that depression scores significantly decreased following the intervention (SMD = 1.80, 95% CI (1.31, 2.30), p < 0.00001). There was no significant difference in antidepressant effectiveness between aTMS and standard TMS (SMD = -0.67, 95% CI (-1.62, 0.27), p = 0.16). Depression scores at follow-up were lower than those directly after the intervention based on the depression rating scale (SMD = 0.22, 95% CI (0.06, 0.37), p = 0.006), suggesting a potential long-term maintenance effect of aTMS. Subgroup meta-analysis results indicated that different modes of aTMS may have diverse long-term effects. At the end of treatment with the accelerated repetitive transcranial magnetic stimulation (arTMS) mode, depressive symptoms may continue to improve (SMD = 0.29, 95% CI (0.10, 0.49), I2 = 22%, p = 0.003), while the accelerated intermittent theta burst stimulation (aiTBS) mode only maintains posttreatment effects (SMD = 0.01, 95% CI (-0.45, 0.47), I2 = 66%, p = 0.98). Conclusions: Compared with standard TMS, aTMS can rapidly improve depressive symptoms, but there is no significant difference in efficacy. aTMS may also have long-term maintenance effects, but longer follow-up periods are needed to assess this possibility. Trial registration: This article is original and not under simultaneous consideration for publication. The study was registered on PROSPERO (https://www.crd.york.ac.uk/prospero/) (number: CRD42023406590). [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities are lesions of presumed vascular origin associated with Cerebral small vessel disease. WMH are common findings that and are associated with increased risk of cognitive impairment and dementia. A higher prevalence of WMH has been also reported in patients with bipolar disorder (BD), although the evidence is conflicting. Objective: To compare the prevalence of WMH in adults with BD, with the prevalence found in healthy controls. Methods: We searched the Embase, Medline/PubMed, and references cited in articles retrieved on May 20, 2023. We included case-control studies that compared the prevalence of WMH in adult BD patients with the prevalence of WMH in healthy controls, using T2-weighted magnetic resonance imaging. We performed a meta-analysis using a random-effects method based on the inverse-variance approach. Findings: We included 22 case-control studies reporting data of 1313 people. The overall rate of WMH was 46.5% in BD patients and 28% in controls (pooled Odds Ratio 2.89, 95% CI 1.76; 4.75). We found a moderate heterogeneity across studies (I2 = 0.49). Publication bias was not significant. Interpretation: We found evidence that BD patients have a higher burden of WMH than healthy controls. Main limitations were impossibility of analyzing gender differences and bipolar type, moderate heterogeneity between studies, non-representative samples, lack of control for major confounders and search in two electronic databases. [ABSTRACT FROM AUTHOR]

Depression, a global health problem with growing prevalence, brings serious impacts on the daily life of patients. However, the antidepressants currently used in clinical are not perfectly effective, which greatly reduces the compliance of patients. Berberine is a natural quaternary alkaloid which has been shown to have a variety of pharmacological effects, such as hypoglycemic, lipid-regulation, anticancer, antibacterial, anti-oxidation, anti-inflammatory, and antidepressant. This review summarizes the evidence of pharmacological applications of berberine in treating depression and elucidates the mechanisms of berberine regulating neurotransmitter levels, promoting the regeneration of hippocampal neurons, improving hypothalamic-pituitary-adrenal axis dysfunction, anti-oxidative stress, and suppressing inflammatory status in order to provide a reference for further research and clinical application of berberine. [ABSTRACT FROM AUTHOR]

Economic development and increased stress have considerably increased the prevalence of psychiatric disorders in recent years, which rank as some of the most prevalent diseases globally. Several factors, including chronic social stress, genetic inheritance, and autogenous diseases, lead to the development and progression of psychiatric disorders. Clinical treatments for psychiatric disorders include psychotherapy, chemotherapy, and electric shock therapy. Although various achievements have been made researching psychiatric disorders, the pathogenesis of these diseases has not been fully understood yet, and serious adverse effects and resistance to antipsychotics are major obstacles to treating patients with psychiatric disorders. Recent studies have shown that the mammalian target of rapamycin (mTOR) is a central signaling hub that functions in nerve growth, synapse formation, and plasticity. The PI3K-AKT/mTOR pathway is a critical target for mediating the rapid antidepressant effects of these pharmacological agents in clinical and preclinical research. Abnormal PI3K-AKT/mTOR signaling is closely associated with the pathogenesis of several neurodevelopmental disorders. In this review, we focused on the role of mTOR signaling and the related aberrant neurogenesis in psychiatric disorders. Elucidating the neurobiology of the PI3K-AKT/mTOR signaling pathway in psychiatric disorders and its actions in response to antidepressants will help us better understand brain development and quickly identify new therapeutic targets for the treatment of these mental illnesses. [ABSTRACT FROM AUTHOR]

Many factors have contributed to rendering frailty an emerging, relevant, and very popular concept. First, many pandemics that have affected humanity in history, including COVID-19, most recently, have had more severe effects on frail people compared to non-frail ones. Second, the increase in human life expectancy observed in many developed countries, including Italy has led to a rise in the percentage of the older population that is more likely to be frail, which is why frailty is much a more common concern among geriatricians compared to other the various health-care professionals. Third, the stratification of people according to the occurrence and the degree of frailty allows healthcare decision makers to adequately plan for the allocation of available human professional and economic resources. Since frailty is considered to be fully preventable, there are relevant consequences in terms of potential benefits both in terms of the clinical outcome and healthcare costs. Frailty is becoming a popular, pervasive, and almost omnipresent concept in many different contexts, including clinical medicine, physical health, lifestyle behavior, mental health, health policy, and socio-economic planning sciences. The emergence of the new "science of frailty" has been recently acknowledged. However, there is still debate on the exact definition of frailty, the pathogenic mechanisms involved, the most appropriate method to assess frailty, and consequently, who should be considered frail. This narrative review aims to analyze frailty from many different aspects and points of view, with a special focus on the proposed pathogenic mechanisms, the various factors that have been considered in the assessment of frailty, and the emerging role of biomarkers in the early recognition of frailty, particularly on the role of mitochondria. According to the extensive literature on this topic, it is clear that frailty is a very complex syndrome, involving many different domains and affecting multiple physiological systems. Therefore, its management should be directed towards a comprehensive and multifaceted holistic approach and a personalized intervention strategy to slow down its progression or even to completely reverse the course of this condition. [ABSTRACT FROM AUTHOR]

Migraine is a highly prevalent neurological disorder. Among the risk factors identified, psychiatric comorbidities, such as depression, seem to play an important role in its onset and clinical course. Patients with migraine are 2.5 times more likely to develop a depressive disorder; this risk becomes even higher in patients suffering from chronic migraine or migraine with aura. This relationship is bidirectional, since depression also predicts an earlier/worse onset of migraine, increasing the risk of migraine chronicity and, consequently, requiring a higher healthcare expenditure compared to migraine alone. All these data suggest that migraine and depression may share overlapping biological mechanisms. Herein, this review explores this topic in further detail: firstly, by introducing the common epidemiological and risk factors for this comorbidity; secondly, by focusing on providing the cumulative evidence of common biological aspects, with a particular emphasis on the serotoninergic system, neuropeptides such as calcitonin-gene-related peptide (CGRP), pituitary adenylate cyclase-activating polypeptide (PACAP), substance P, neuropeptide Y and orexins, sexual hormones, and the immune system; lastly, by remarking on the future challenges required to elucidate the etiopathological mechanisms of migraine and depression and providing updated information regarding new key targets for the pharmacological treatment of these clinical entities. [ABSTRACT FROM AUTHOR]

Social Anxiety Disorder (SAD) has its usual onset during adolescence when it is a highly prevalent and debilitating condition. Evidence regarding the processes that underline social anxiety and SAD is not compelling, especially in adolescents. Within an Acceptance and Commitment Therapy (ACT) framework, the causal role of ACT processes on adolescents' social anxiety and how these processes contribute to sustain social anxiety over time is still unknown. Hence, this study explored the role of psychological inflexibility (PI) and acceptance and committed action (as psychological flexibility processes) on social anxiety over time, in a clinical sample of adolescents. Twenty-one adolescents (Mage = 16.19, SD = 0.750) with a primary diagnosis of SAD completed a set of self-report measures assessing PI, acceptance (i.e., willingness to experience social anxiety symptoms), action (i.e., moving towards valued life directions despite social anxiety symptoms) and social anxiety. Path analysis was used to investigate a mediation model linking acceptance, committed action, and PI to social anxiety, directly and indirectly. Findings revealed that acceptance and action were negatively and directly associated with PI after 10-weeks. In turn, PI yielded a positive and direct effect on social anxiety after another 12-weeks. PI totally mediated the relation between acceptance and action and social anxiety, with significant indirect effects. Overall, findings offer evidence for the applicability of the ACT model to adolescent SAD and support the use of clinical interventions targeting PI to understand and alleviate adolescents' social anxiety. [ABSTRACT FROM AUTHOR]

Background: Although social cognitive dysfunction is a major feature of the behavioral variant of frontotemporal dementia (bvFTD), quantitative measurement of social behavior changes is poorly available in clinical settings. Objective: The aim of the study is to evaluate diagnostic accuracy of social-emotional questionnaires in distinguishing bvFTD from healthy control (HC) subjects and Alzheimer's disease (AD) patients. Methods: We enrolled 29 bvFTD, 24 AD, and 18 HC subjects matched for age, sex, and education. Two informant-based measures of socio-emotional sensitivity and empathy (i.e., revised Self-Monitoring Scale (rSMS) and Interpersonal Reactivity Index (IRI)) were administered. One-way ANOVA was performed to compare groups, whereas Receiver Operating Characteristics (ROC) curve analysis tested questionnaire ability in distinguishing groups. A short version of IRI (sIRI) was obtained by excluding the non-contributing subscale (i.e., personal distress). Results: Compared to HC and AD, bvFTD showed significantly lower scores in rSMS and IRI scores, except for IRI personal distress subscale. The sIRI showed an excellent performance in early diagnosis (bvFTD versus HC = AUC 0.95). Both sIRI and rSMS showed good performance in distinguishing bvFTD from AD (AUC 0.83). Conclusions: ROC analyses support the usefulness of informant social questionnaires in memory clinics and their potential value in screening procedures for research eligibility in forthcoming trials. In the timely diagnosis of bvFTD patients, IRI and rSMS may supply crucial information for the early detection of signs and symptoms affecting social-emotional skills, which might otherwise be underrecognized. [ABSTRACT FROM AUTHOR]

This study quantified the prevalence of postpartum mood and anxiety disorder (PMAD) diagnoses among symptomatic Michigan Medicaid enrollees and explored factors associated with receiving a diagnosis. Data sources comprised Michigan Medicaid administrative claims and Phase 7 Michigan Pregnancy Risk Assessment Monitoring System (MI-PRAMS) survey responses, linked at the individual level. Participants were continuously enrolled in Michigan Medicaid, delivered a live birth (2012–2015), responded to the survey, and screened positive for PMAD symptoms on the adapted two-item Patient Health Questionnaire. Unadjusted and adjusted weighted logistic regression analyses were used to predict the likelihood of having a PMAD diagnosis (for the overall sample and stratified by race). The weighted analytic cohort represented 24,353 deliveries across the 4-year study. Only 19.8% of respondents with symptoms of PMAD had a PMAD diagnosis between delivery and 3 months afterward. Black respondents were less likely to have PMAD diagnoses (adjusted odds ratio [AOR]=0.23, 95% CI=0.11–0.49) compared with White respondents. Among White respondents, no covariates were significantly associated with having a diagnosis. However, among Black respondents, more comorbid conditions and more life stressors were statistically significantly associated with having a diagnosis (AOR=3.18, 95% CI=1.27–7.96 and AOR=3.12, 95% CI=1.10–8.88, respectively). Rate of PMAD diagnosis receipt differed by race and was low overall. Black respondents were less likely than White respondents to receive a diagnosis. Patient characteristics influencing diagnosis receipt also differed by race, indicating that strategies to improve detection of these disorders require a tailored approach. [ABSTRACT FROM AUTHOR]

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) therapy has few side effects and comparable therapeutic effects to antidepressant treatment, but few studies have introduced TMS therapy as an initial treatment for MDD. The objective of this study was to retrospectively compare the clinical outcomes between 50 MDD patients without antidepressants (i.e., TMS monotherapy) and 50 MDD patients with antidepressants plus TMS therapy, matched for age, sex, and depression severity. The presence or absence of antidepressant therapy in first-line treatment was determined via a detailed interview by psychiatrists. The study design was a retrospective observational case–control study using the TMS registry data. The key inclusion criteria were adult patients who met the diagnosis of MDD and received 20–30 sessions of intermittent theta-burst stimulation (iTBS) therapy to the left dorsolateral prefrontal cortex (DLPFC). In this study, the Montgomery–Åsberg Depression Rating Scale (MADRS) was used as the primary outcome measure. No significant group differences existed in the baseline MADRS total score between the unmedicated and medicated patient groups. Following TMS therapy, no significant group differences in response rate, remission rate, or relative total score change in the MADRS were observed. The main limitations were the retrospective design and the use of registry data as a source. Our findings suggest that TMS monotherapy may be as effective as TMS add-on therapy to antidepressants when used as the first-line therapy for MDD, but randomized controlled trials are needed. [ABSTRACT FROM AUTHOR]

Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a significant medical challenge, with no indisputable pathophysiological mechanism identified to date. Methods: Based on clinical clues, we hypothesized that 5-hydroxytryptamine (5-HT) hyperactivation is implicated in the pathogenic causes of ME/CFS and the associated symptoms. We experimentally evaluated this hypothesis in a series of mouse models. Results: High-dose selective serotonin reuptake inhibitor (SSRI) treatment induced intra- and extracellular serotonin spillover in the dorsal raphe nuclei of mice. This condition resulted in severe fatigue (rota-rod, fatigue rotating wheel and home-cage activity tests) and ME/CFS-associated symptoms (nest building, plantar and open field test), along with dysfunction in the hypothalamic-pituitary-adrenal (HPA) axis response to exercise challenge. These ME/CFS-like features induced by excess serotonin were additionally verified using both a 5-HT synthesis inhibitor and viral vector for Htr1a (5-HT1A receptor) gene knockdown. Conclusions: Our findings support the involvement of 5-HTergic hyperactivity in the pathophysiology of ME/CFS. This ME/CFS-mimicking animal model would be useful for understanding ME/CFS biology and its therapeutic approaches. [ABSTRACT FROM AUTHOR]

Background: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention. Objective: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention. Methods: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar. Results: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being. Conclusions: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Uludag University Medical Faculty / Uludağ Üniversitesi Tıp Fakültesi Dergisi is the property of Journal of Uludag University Medical Faculty and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

People with severe mental disorders often require special care. Other than institutionalized care, some organizations provide housing options and special care in the form of organized living. Few studies provide a detailed description of nutrient intake in this type of care. The aim of this prospective study was to assess nutritional status and adherence to dietary recommendations in both people with mental disorders (beneficiaries) and their caregivers. Across three levels of care, 46 beneficiaries and 19 caregivers participated in the study. The mean intakes of energy (kcal/day) and macro- and micronutrients (g/day) were estimated from a 3-day dietary record and compared with dietary reference values (DRVs) set by the European Food Safety Authority (EFSA). The majority of participants did not meet energy intake recommendations (kcal/day). The contribution of total fat to energy intake (% E) was higher than recommended in both beneficiaries and caregivers with 42.1 ± 4.4% and 38.5 ± 6.5%, respectively. A total of 45.2% of beneficiaries and 61.1% of caregivers had their carbohydrate intake within the recommended range. Fiber intake was well below the recommendations (25 g/day) in all groups. Across all levels of care, lower micronutrient intake for pantothenic acid, biotin, folate, potassium, magnesium, copper, selenium, and iodine was observed. Based on the current results, the development and implementation of nutritional guidelines may be targeted to specific populations and nutrient intakes. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Psychotraumatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Depression is a prevalent mental disorder and has a profound impact on an individual's psychological and physical well-being. It is characterized by a persistently depressed mood, loss of interest, energy loss, and cognitive dysfunction. In recent years, more and more people have changed to mental diseases, such as depression, anxiety, mania and so on. In the incidence of depression, covering all ages, but still mainly young and middle-aged women. Traditional treatments for depression mainly rely on medication and psychotherapy, but these methods are not effective for all patients and are often accompanied by certain side effects. Therefore, finding safe and effective alternative or adjuvant treatments has become a priority. Here we highlight the research progress of acupuncture in the treatment of depression and to explore the mechanism of acupuncture in the treatment of depression. Acupuncture treatment of depression is an ancient and effective method, the mechanism involves multiple biological pathways, for example, by regulating neurotransmitter levels, regulating the neuroendocrine axis, improving neuroplasticity, anti-inflammatory and other effects, improving emotional state and play an antidepressant role. To provide evidence to support the widespread use of acupuncture in clinical practice. We hope to provide new treatment ideas and methods for patients with depression, and even reduce the incidence of depression. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and transmission are generally known to be produced by respiratory droplets and aerosols from the oral cavity (O.C.) of infected subjects, as stated by the World Health Organization. Saliva also retains the viral particles and aids in the spread of COVID-19. Angiotensin-converting enzyme Type 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) are two of the numerous factors that promote SARS-CoV-2 infection, expressed by O.C. structures, various mucosa types, and the epithelia of salivary glands. A systemic SARS-CoV-2 infection might result from viral replication in O.C. cells. On the other hand, cellular damage of different subtypes in the O.C. might be associated with various clinical signs and symptoms. Factors interfering with SARS-CoV-2 infection potential might represent fertile ground for possible local pharmacotherapeutic interventions, which may confine SARS-CoV-2 virus entry and transmission in the O.C., finally representing a way to reduce COVID-19 incidence and severity. [ABSTRACT FROM AUTHOR]

Background and Objective: United States Food and Drug Administration (USFDA) recently approved a novel combination of olanzapine-samidorphan (OLZSAM) for managing olanzapine-associated adverse events (weight gain) in adult patients with schizophrenia and bipolar disorder. To opine about the safety and efficacy of OLZSAM, authors performed a systematic review and meta-analysis to convene justifiable evidence. Methods: A thorough literature search was performed through the databases Embase, Cochrane Library, PubMed, and clinicaltrials.gov, from inception to September 2022, with the keywords: 'olanzapine and samidorphan' and schizophrenia; and "ALKS3831" and "lybalvi." Clinical trials published in English that analyzed the efficacy and safety of OLZSAM were included. The significant outcomes included in this study were change from baseline (CFB) in Positive and Negative Syndrome Scale (PANSS) at the end of the study, the proportion of patients with weight gain at the end of the study, the proportion of patients with at least one adverse event, and the incidence of drug discontinuation due to adverse events. Results: The change in PANSS score at the end of the study was comparable among groups receiving OLZSAM and olanzapine alone: standardized mean difference (SMD) = 0.04; 95% CI = -0.09 to 0.17; p = 0.57. The OLZSAM group reported less incidence of weight gain: risk ratio (RR) = 0.91; 95% CI = 0.62–1.34; p = 0.63, and any adverse event: RR = 0.99; 95% CI = 0.90–1.09; p = 0.81. Drug discontinuation incidence was higher in the OLZSAM group: RR = 1.22; 95% CI = 0.84–1.79; p = 0.30. Conclusions: The combination OLZSAM showed comparable efficacy to olanzapine alone in schizophrenia patients, with relatively less incidence of weight gain and adverse events; however, the drug discontinuation due to adverse events was more in the OLZSAM group. [ABSTRACT FROM AUTHOR]

The failure of preclinical research to advance successful candidate medications in psychiatry has created a paradigmatic crisis in psychiatry. The Research Domain Criteria (RDoC) initiative was designed to remedy this situation with a neuroscience-based approach that employs multimodal and cross-species in vivo methodology to increase the probability of translational findings and, consequently, drug discovery. The present review underscores the feasibility of this methodological approach by briefly reviewing, first, the use of multidimensional and cross-species methodologies in traditional behavioral pharmacology and, subsequently, the utility of this approach in contemporary neuroimaging and electrophysiology research—with a focus on the value of functionally homologous studies in nonhuman and human subjects. The final section provides a brief review of the RDoC, with a focus on the potential strengths and weaknesses of its domain-based underpinnings. Optimistically, this mechanistic and multidimensional approach in neuropsychiatric research will lead to novel therapeutics for the management of neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Purpose: In Finland, prevalence of schizophrenia is higher in the eastern and northern regions and co-occurs with the distribution of schizophrenia polygenic risk scores. Both genetic and environmental factors have been hypothesized to contribute to this variation. We aimed to examine the prevalence of psychotic and other mental disorders by region and degree of urbanicity, and the impacts of socio-economic adjustments on these associations. Methods: Nationwide population registers from 2011 to 2017 and healthcare registers from 1975 to 2017. We used 19 administrative and three aggregate regions based on the distribution of schizophrenia polygenic risk scores, and a seven-level urban–rural classification. Prevalence ratios (PRs) were calculated by Poisson regression models and adjusted for gender, age, and calendar year (basic adjustments), and Finnish origin, residential history, urbanicity, household income, economic activity, and physical comorbidity (additional adjustments) on an individual level. Average marginal effects were used to visualize interaction effects between region and urbanicity. Results: A total of 5,898,180 individuals were observed. All mental disorders were slightly more prevalent (PR 1.03 [95% CI, 1.02–1.03]), and psychotic disorders (1.11 [1.10–1.12]) and schizophrenia (1.19 [1.17–1.21]) considerably more prevalent in eastern and northern than in western coastal regions. After the additional adjustments, however, the PRs were 0.95 (0.95–0.96), 1.00 (0.99–1.01), and 1.03 (1.02–1.04), respectively. Urban residence was associated with increased prevalence of psychotic disorders across all regions (adjusted PR 1.21 [1.20–1.22]). Conclusion: After adjusting for socioeconomic and sociodemographic factors, the within-country distribution of mental disorders no longer followed the traditional east–west gradient. Urban–rural differences, on the other hand, persisted after the adjustments. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second leading cause of cancer death in men in the United States. Androgen deprivation therapy (ADT) is currently the primary treatment for metastatic prostate cancer, and some studies have shown that the use of anti-androgen drugs is related to a reduction in cognitive function, mood changes, diminished quality of life, dementia, and possibly Alzheimer's disease. ADT has potential physiological effects such as a reduction in white matter integrity and a negative impact on hypothalamic functions due to the lowering of testosterone levels or the blockade of downstream androgen receptor signaling by first- and second-generation anti-androgen drugs. A comparative analysis of prostate cancer patients undergoing ADT and Alzheimer patients identified over 30 shared genes, illustrating common ground for the mechanistic underpinning of the symptomatology. The purpose of this review was to investigate the effects of ADT on cognitive function, mood, and quality of life, as well as to analyze the relationship between ADT and Alzheimer's disease. The evaluation of prostate cancer patient cognitive ability via neurocognitive testing is described. Future studies should further explore the connection among cognitive deficits, mood disturbances, and the physiological changes that occur when hormonal balance is altered. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Brain Sciences" discussing the influence of stress on the nervous system and its impact on anxiety and affective disorders. The editorial highlights the prevalence of anxiety disorders and their effect on quality of life, particularly in young adults. It explores the interrelations between environmental stress factors, such as psychological and biological stressors, substance misuse, and their effects on brain structure and function. The editorial also discusses the shared clinical features and underlying mechanisms of anxiety and mood disorders, as well as the role of stress in modern society. It introduces a special issue that includes nine manuscripts covering various aspects of stress and its interactions with affective disorders. The manuscripts explore topics such as suicide risk assessment, immune regulatory mechanisms, genetic factors, diagnostic validity in psychiatry, and neuroimaging as a tool for treatment response monitoring. The editorial emphasizes the need for multidisciplinary perspectives in psychiatry and the importance of considering social and cultural influences in diagnostic frameworks. Overall, the articles in this special issue provide insights into the mechanisms and pathways of anxiety and affective disorders in relation to stress. [Extracted from the article]

Background: Sexual dysfunction is commonly observed in individuals with Major Depressive Disorder (MDD), along with various psychological symptoms such as anxiety, somatic complaints, interpersonal sensitivity, and obsessive-compulsive tendencies. However, there is a research gap in understanding the impact of these psychological symptoms on sexual functioning in MDD. Furthermore, there is limited data on the incidence of sexual dysfunction among drug-naive MDD patients in West China. This study aims to determine the prevalence of sexual dysfunction in this patient population and explore its association with other psychological indicators. Methods: We conducted a retrospective analysis of patient data from October 2020 to September 2022 using propensity score matching. A focused group of 165 males and 490 females was selected from a total of 1941 MDD patients. This allowed for a comparative analysis of demographic data, as well as scores from the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Symptom Checklist-90 (SCL-90), the Arizona Sexual Experience Scale (ASEX). Results: Our findings reveal that 46.2% of drug-naive MDD patients experienced sexual dysfunction. Notably, there was a higher prevalence of sexual dysfunction among female patients (50.3%) compared to males (37.5%). MDD patients without sexual dysfunction consistently exhibited higher SDS scores than those with sexual dysfunction (p < 0.01), There were no statistically significant differences between male and female MDD patients with or without concomitant sexual dysfunction in terms of Somatic complaints, Obsessive-compulsive, Interpersonal sensitivity, Anxiety, Phobic anxiety, Paranoid ideation, Psychoticism and Diet/sleep difficulties (p > 0.05). In addition, male MDD patients with sexual dysfunction showed a emerging trend towards elevated Hostility scores on the SCL-90 (p = 0.058), male MDD patients with sexual dysfunction showed an increasing trend in hostility scores on the SCL-90, whereas female MDD patients with sexual dysfunction did not show such a trend. Conclusion: The study highlights a significant gender difference in the prevalence of sexual dysfunction among MDD patients, with females being more susceptible than males. There is a positive correlation between the severity of depression and sexual dysfunction in both genders. Interestingly, male MDD patients demonstrated a potential protective effect of hostility against sexual dysfunction, which was not observed in female patients. [ABSTRACT FROM AUTHOR]

Background: Beauveria bassiana is a filamentous fungus commonly used as an insecticide that rarely causes keratitis. Methods: Patients affected by Beauveria bassiana keratitis were retrospectively recruited at San Raffaele Hospital (Milan, Italy) between 2020 and 2022. All subjects underwent comprehensive ophthalmic evaluation, including in vivo confocal microscopy (IVCM) and microbiologic examination of corneal scrapings. Beauveria bassiana was identified using 18S rDNA targeted PCR. Results: Four eyes of four patients (51 ± 8.8 years old) were evaluated. The main risk factors were soft contact lens wear (75%) and trauma with vegetative matter (50%). A superficial infiltrate was displayed in the majority of patients. Three cases (75%) showed hyphae on IVCM. All patients showed clinical improvement after topical antifungal therapy, although mostly through a combination of two antifungals (75%). One patient with a deeper infection required a systemic antifungal agent after one month of topical therapy. All cases required debridement to reduce the microbial load and enhance drug penetration. All patients experienced keratitis resolution following medical treatment (average: 3.3 months). Conclusions: The identification of risk factors and the early diagnosis of Beauveria bassiana keratitis are fundamental in order to avoid its penetration in the deeper corneal stromal layers. Topical antifungal drugs, possibly accompanied by ulcer debridement, may be a successful treatment if instilled from the early phases of the disease. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings. [ABSTRACT FROM AUTHOR]

Cerebrovascular disease is a significant cause of morbidity and mortality in Africa, and using neuroimaging techniques has improved the diagnosis and management of this disease. However, there is a lack of comprehensive reviews of the role and effectiveness of neuroimaging techniques in the African context. We reviewed the literature to evaluate the role of neuroimaging in diagnosing and managing cerebrovascular disease in Africa. Our search included electronic databases such as PubMed, Scopus, and Google Scholar from 2000 to April 2023. We included peer-reviewed studies written in English that reported on the use of neuroimaging in diagnosing and managing cerebrovascular disease in African populations. We excluded non-peer-reviewed articles, letters, editorials, and studies unrelated to cerebrovascular disease, neuroimaging, or Africa. A total of 102 potential articles were identified; after applying our exclusion criteria and removing duplicated articles, 51 articles were reviewed. Our findings suggest that neuroimaging techniques such as CT, MRI, and Skull x-ray play a crucial role in diagnosing and managing cerebrovascular disease in Africa. CT and MRI were the most commonly used techniques, with CT being more widely available and less expensive than MRI. However, challenges to using neuroimaging in Africa include the high cost of equipment and maintenance, lack of trained personnel, and inadequate infrastructure. These challenges limit the widespread use of neuroimaging in diagnosing and managing cerebrovascular disease in Africa. Neuroimaging techniques are essential for diagnosing and managing cerebrovascular disease in Africa, but challenges to their use must be addressed to improve healthcare outcomes. Our policy recommendations can help improve the availability and accessibility of neuroimaging services in Africa. [ABSTRACT FROM AUTHOR]

The previous few decades have seen widespread environmental exposure to munitions and explosives systems in and around war and weapon testing ranges. Most contemporary warheads use explosives to launch metal shards and charge jets to destroy targets. Presently, Warheads with improved blast performance and shelf life are manufactured using technologies with improved blast performance; among them, plastic-bonded explosives (PBX) have emerged as one of the most promising high explosives for use in various ammunition warheads. The hydroxyl-terminated polybutadiene (HTPB) binder-based PBX has metals like aluminium (Al) and magnesium (Mg) to improve munitions performance by boosting blast pressure. Further, magnesium hydride and other derivatives are commonly added to energetic formulations to enhance the heat of explosion of aluminized explosives and to improve burn rate of certain propellants. So, explosions of these warheads and munitions leave thick fumes and residues in the battlefield soil, which release toxic chemicals, including magnesium, into the air, water, and soil. Later on, magnesium metal contamination negatively impacts the environment and food chain. Hence, magnesium toxicity in the environment, including soil, water, plants, and animals, is the subject of this research and a significant concern to animal and human health. Until now, enough literature has been unavailable; hence, this review brings knowledge on the ecotoxicology of magnesium-based explosives and their possible alarming effects on animal and human health by affecting the food chain. [ABSTRACT FROM AUTHOR]

Introduction: Major depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data. Methods: In this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self expressive network. Results: Experimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism. Discussion: In this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection. [ABSTRACT FROM AUTHOR]

The role of altered brain mitochondrial regulation in psychiatric pathologies, including Major Depressive Disorder (MDD), has attracted increasing attention. Aberrant mitochondrial functions were suggested to underlie distinct inter-individual vulnerability to stress-related MDD syndrome. In this context, insulin receptor sensitizers (IRSs) that regulate brain metabolism have become a focus of recent research, as their use in pre-clinical studies can help to elucidate the role of mitochondrial dynamics in this disorder and contribute to the development of new antidepressant treatment. Here, following 2-week chronic mild stress (CMS) using predation, social defeat, and restraint, MDD-related behaviour and brain molecular markers have been investigated along with the hippocampus-dependent performance and emotionality in mice that received the IRS dicholine succinate (DS). In a sucrose test, mice were studied for the key feature of MDD, a decreased sensitivity to reward, called anhedonia. Based on this test, animals were assigned to anhedonic and resilient-to-stress-induced-anhedonia groups, using a previously established criterion of a decrease in sucrose preference below 65%. Such assignment was based on the fact that none of control, non-stressed animals displayed sucrose preference that would be smaller than this value. DS-treated stressed mice displayed ameliorated behaviours in a battery of assays: sucrose preference, coat state, the Y-maze, the marble test, tail suspension, and nest building. CMS-vulnerable mice exhibited overexpression of the inflammatory markers Il-1β, tnf, and Cox-1, as well as 5-htt and 5-ht2a-R, in various brain regions. The alterations in hippocampal gene expression were the closest to clinical findings and were studied further. DS-treated, stressed mice showed normalised hippocampal expression of the plasticity markers Camk4, Camk2, Pka, Adcy1, Creb-ar, Nmda-2r-ar, and Nmda-2r-s. DS-treated and non-treated stressed mice who were resilient or vulnerable to anhedonia were compared for hippocampal mitochondrial pathway regulation using Illumina profiling. Resilient mice revealed overexpression of the mitochondrial complexes NADH dehydrogenase, succinate dehydrogenase, cytochrome bc1, cytochrome c oxidase, F-type and V-type ATPases, and inorganic pyrophosphatase, which were decreased in anhedonic mice. DS partially normalised the expression of both ATPases. We conclude that hippocampal reduction in ATP synthesis is associated with anhedonia and pro-inflammatory brain changes that are ameliorated by DS. [ABSTRACT FROM AUTHOR]

Autonomous ships represent an emerging paradigm within the maritime sector, poised to bring multiple advantages. Although numerous prototypes have been developed, the deployment of large autonomous ships has predominantly remained confined to domestic waters or specialized military applications. The extensive adoption of autonomous ships is hampered by several challenges, primarily centered around safety. However, the direct assessment of autonomous technologies on large-scale vessels can be very costly. Small-scale autonomy testing may provide a cheaper option. This study reviews the current small autonomous ship models used by maritime researchers and industry practitioners. It aims to evaluate how these autonomous models currently augment and can augment safety assurances on larger autonomous ships. The review identifies relevant very small Unmanned Surface Vessels (USVs), the main research groups behind them and their applications. Then, the current use of USVs for safety and safety assurance is analyzed. Finally, the paper suggests innovative strategies and research directions for using USVs for the safety assurance of larger autonomous ships. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) has been a major challenge to global health and a financial burden. Little is known regarding the possible causal effects of COVID-19 on the macro- and micro-structures of the human brain. Objective: To determine the causal links between susceptibility, hospitalization, and the severity of COVID-19 and brain imaging-derived phenotypes (IDPs). Methods: Mendelian randomization (MR) analyses were performed to investigate the causal effect of three COVID-19 exposures (SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19) on brain structure employing summary datasets of genome-wide association studies. Results: In terms of cortical phenotypes, hospitalization due to COVID-19 was associated with a global decrease in the surface area (SA) of the cortex structure (β= –624.77, 95% CI: –1227.88 to –21.66, p = 0.042). At the regional level, SARS-CoV-2 infection was found to have a nominally causal effect on the thickness (TH) of the postcentral region (β= –0.004, 95% CI: –0.007 to –0.001, p = 0.01), as well as eight other IDPs. Hospitalized COVID-19 has a nominally causal relationship with TH of postcentral (β= –0.004, 95% CI: –0.007 to –0.001, p = 0.01) and other 6 IDPs. The nominally causal effects of critical COVID-19 on TH of medial orbitofrontal (β=0.004, 95% CI: 0.001to 0.007, p = 0.004) and other 7 IDPs were revealed. Conclusions: Our study provides compelling genetic evidence supporting causal relationships between three COVID-19 traits and brain IDPs. This discovery holds promise for enhancing predictions and interventions in brain imaging. [ABSTRACT FROM AUTHOR]