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The typical conundrum with cannabis use by adolescents and first episode psychosis — which came first? — was displaced by Steven Batki, M.D. in a plenary session at the Cape Cod Symposium on Addictive Disorders (CCSAD) last week. Bakti, who is professor of psychiatry at the University of California San Francisco Weill Institute for Neurosciences, presented his conclusions allowing the audience of some 1,000 to ask questions as he went along. [ABSTRACT FROM AUTHOR]

This article discusses the efficacy of tirzepatide in treating type 2 diabetes, emphasizing that females, individuals of white or Asian race, younger patients and those taking metformin benefit the most from the treatment, with higher doses associated with greater effectiveness.

The article focuses on proposed amendments to Processing, Classification Policies, and Review Criteria to establish the Birth Center category of service, ensuring consistency with IDPH regulations and repealing a section related to the Alternative Health Care Delivery Act. It mentions that the Health Facilities and Services Review Board invites comments within 45 days, emphasizing that the rulemaking does not create or expand a State mandate, with the economic impact yet to be determined.

Researchers who have completed a new cohort study involving alcohol users have suggested that evaluating alcohol use disorder (AUD) based on the severity of the various diagnostic criteria might offer a clearer picture than an approach that simply counts the number of criteria an individual endorses. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD. [ABSTRACT FROM AUTHOR]

The article addresses challenges of screening for autism in adults. Topics discussed include advantages of Autism Speaks' Adult Autism Diagnosis Tool Kit, mental health conditions experience by autistic adults, and major impact of age of diagnosis on mental health. It also mentions recommendations for adults seeking a diagnosis.

Craving is a great measure for outcomes for substance use disorders (SUDs) with one exception: how to measure it? Researchers at last week's College on Problems of Drug Dependence (CPDD) conference discussed the important topic. [ABSTRACT FROM AUTHOR]

Researchers from Wuhan University in Hubei, China, have developed a new model for classifying neuropsychiatric disorders using multi-modal data. The model combines a multi-scale image features extraction neural network with XGBoost to achieve high accuracy in classification. The study aims to provide psychiatrists with ideas for comparative examinations and diagnosis, enhancing interpretability and understanding of mental disorders. This research has been peer-reviewed and offers valuable insights into the field of neuropsychiatry. [Extracted from the article]

Researchers at the University Teaching Hospital have conducted a study on orthorexia nervosa, an unhealthy obsession with healthy eating. The study highlights how social media platforms like Instagram and TikTok can contribute to the development of eating disorders by showcasing idealized images of healthy eating and body image. Orthorexia nervosa is characterized by a fixation on food quality rather than body image or weight, leading to strict dietary restrictions and significant stress and anxiety over food choices. The impact of orthorexia on mental and physical health is a growing concern, prompting increased research and awareness in the field of eating disorders. [Extracted from the article]

A historical review published in the Psychology & Psychiatry Journal highlights the significant contributions of 19th-century French psychiatrists to the understanding of depression. The study examines a 1897 French monograph, "La Melancolie," by Jacques Roubinovitch and Edouard Toulouse, which has been overlooked in English-language psychiatric literature. The review provides an English translation of the original text, shedding light on the historical context and observations of the French psychiatrists, challenging the Anglo-centric view of psychiatric history and emphasizing the importance of a global perspective in mental health research. [Extracted from the article]

The article discusses the development, validation, and testing of an education program for identifying learning disabilities among school children by primary school teachers in Qatar. The study aims to increase teachers' knowledge and competency in identifying learning disabilities, leading to early diagnosis and referrals. The research focuses on creating an education module to help teachers recognize and support children with learning disabilities, emphasizing the importance of providing appropriate education to all children. [Extracted from the article]

A recent study conducted by the University of Ljubljana focused on the prevalence of attention-deficit hyperactivity disorder (ADHD) in children and adults worldwide. The study included 117 studies and found that the overall prevalence of ADHD varied depending on the type of study conducted, ranging from 1.6% in register studies to 5.0% in survey studies. The research highlighted the importance of considering different diagnostic criteria and sampling methods when comparing prevalence estimates of ADHD. [Extracted from the article]

A preprint abstract from biorxiv.org discusses the use of deep convolutional neural networks (DCNN) to analyze functional network connectivity (FNC) in the brain. The current methods for evaluating FNCs primarily capture linear statistical relationships, but this approach aims to account for the intricate nonlinear interactions involved in cognitive operations and psychiatric conditions like schizophrenia. The DCNN is trained to create heatmaps from different convolution layers, and a t-test-based feature selection method is used to analyze each heatmap. The results show promise in accurately distinguishing differences between schizophrenia patients and healthy controls. However, it is important to note that this research has not yet undergone peer review. [Extracted from the article]

A new report from the University of Health and Allied Sciences discusses research findings on mental health diseases and conditions. The study focuses on the perceptions of mental illness among members of two urban poor communities in Accra, Ghana. Through qualitative data collection, the researchers found that the communities had a high awareness of the factors contributing to mental illness risk and experiences, drawing on various sources of knowledge. The findings provide valuable insights for planning community mental health interventions that address both social and institutional care needs. [Extracted from the article]

The article presents the discussion on lack of specificity in the definitions of late onset ADHD as well as inconsistent assessment procedures. Topics include adult-onset ADHD being possible where many individuals affected and unevaluated showing subthreshold childhood symptoms; and adult ADHD being methodologically sufficient for empirically evaluating adult onset ADHD.

New research to demonstrate a wearable as a mental health treatment has produced rapid and clinically significant improvements within the first few weeks in individuals experiencing moderate to severe depression, according to researchers who conducted what is considered the largest clinical trial to demonstrate this technology in individuals with mental illness. [ABSTRACT FROM AUTHOR]

An interview with sex educator Tina Horn, author of the book "Why Are People Into That," based on her podcast of the same name, is presented. Horn talks about what appeared to her about being a dominatrix, what people can gain from learning about kink, and common reactions of people when they learn that she works in sex.

A growing body of research implicates inflammation as a potential pathway in the aetiology and pathophysiology of some mental illnesses. A systematic review was conducted to determine the association between parasitic infection and mental illnesses in humans in Africa and reviewed the state of the evidence available. The search focused on publications from Africa documenting the relationship between parasites from two parasite groups, helminths and protozoans, and four classifications of mental illness: mood affective disorders, neurotic and stress-related disorders, schizotypal disorders and unspecified mental illnesses. In the 26 reviewed papers, the prevalence of mental illness was significantly higher in people with parasitic infection compared to those without infection, i.e., 58.2% vs 41.8% (P < 0.001). An overall odds ratio found that the association of having a mental illness when testing positive for a parasitic infection was four times that of people without infection. Whilst the study showed significant associations between parasite infection and mental illness, it also highlights gaps in the present literature on the pathophysiology of mental illness in people exposed to parasite infection. This study highlighted the importance of an integrated intervention for parasitic infection and mental illness. [ABSTRACT FROM AUTHOR]

The article discusses the issue of what is sufficient and cost-effective method to assess attention deficit hyperactivity disorder (ADHD) in adult persons by a private practicing mental health professional.

The article presents a study which examined the effectiveness of the Siblings Are Special (SAS) program to improve sibling relationships in children with attention deficit hyperactivity (HDAD) disorder.

The article explores the issue of substance abuse, its effects, and some options and thoughts for recovery. Topics discussed include role of sleep disturbances in substance abuse, stages of sleep, and association between post-traumatic stress disorder (PTSD) and a failure to process and neutralize frightening memories.

A recent study conducted by researchers at Maria Grzegorzewska University in Warsaw, Poland, aimed to examine the factors related to the level of personality functioning and their implications for the mental health care system in Poland. The study, which included a representative sample of 1,030 participants, used the Level of Personality Functioning Scale-Brief Form 2.0 according to DSM-5 to measure the level of personality functioning. The results indicated the possibility of distinguishing three groups with different levels of personality functioning, which were associated with various socio-demographic variables, mental health symptoms, and history of traumatic experiences. The findings of this study may have significant implications for the development of effective mental health prevention strategies and tailored medical care for individuals with personality difficulties and disorders. [Extracted from the article]

The article discusses the U.S. Food and Drug Administration' (FDA) issuance of a Complete Response Letter (CRL) for 3,4-Methylenedioxy-methamphetamine (MDMA)-assisted therapy for Post-Traumatic Stress Disorder (PTSD), delaying its approval. Topics discussed include the FDA's request for additional phase 3 studies, the impact on PTSD treatment options, and the ongoing debate about the effectiveness and risks of MDMA-assisted therapy.

A preprint abstract discusses the use of self-supervised learning (SSL) in classifying neurodegenerative disorders using T1-weighted MRI scans. The study found that SSL models can effectively distinguish between different neurodegenerative disorders in an interpretable manner. The models achieved comparable results to state-of-the-art supervised deep learning approaches, suggesting that SSL can make use of unannotated neuroimaging datasets while remaining robust and interpretable. The study has not yet undergone peer review. [Extracted from the article]

A recent study conducted at the Centre Hospitalier Universitaire Brugmann in Brussels, Belgium, explored the use of transfer learning video classification in diagnosing patients with different levels of left ventricular ejection fraction (EF) in echocardiography. The researchers used an end-to-end video classification approach using AutoML in Google Vertex AI and obtained datasets from the Standford EchoNet-Dynamic repository. The study found that the classification of reduced EF (rEF) versus not rEF demonstrated good performance, while the classification of preserved EF (pEF) versus not pEF was slightly less accurate. The researchers concluded that this approach shows promise and should be further evaluated in prospective clinical studies. [Extracted from the article]

A research study conducted at Philipps-University Marburg in Germany explores the challenges faced by clinical psychology in developing more effective treatments for mental disorders. The study proposes a multimodal dynamical network approach that integrates various data sources, such as psychological, psychopathological, and neurobiological data, to improve the understanding and treatment of mental disorders. By dismantling artificial diagnostic boundaries and adopting a transdiagnostic view of psychopathology, this approach aims to provide individualized models of psychopathology and suggest personalized treatment strategies. The study suggests that the integration of longitudinal data and causal inference techniques can enhance the identification of influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. [Extracted from the article]

Researchers at Yale University have published new data on Alzheimer's disease. They developed a dual-objective adversarial learning framework that reconstructs higher quality brain magnetic resonance images (MRIs) while retaining disease-specific imaging features. The framework was trained using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and externally validated on the National Alzheimer's Coordinating Center (NACC) data. The generated images had better image quality and a classifier utilizing these images distinguished between progressive and stable mild cognitive impairment more accurately than with the original scans. This research shows the potential of competitive deep learning in facilitating disease-oriented image reconstruction for individuals at risk of developing Alzheimer's disease. [Extracted from the article]

A scoping review conducted by the University of Ottawa examined the impact of fathers' involvement in the treatment of eating disorders (ED). The review found that fathers' engagement in ED treatment improved their well-being and family functioning, but these gains were not consistently tied to treatment outcomes. Father attendance, improved caregiving skills, and their expectations of treatment correlated with better outcomes for their affected child. The research calls for further exploration of how to overcome barriers to fathers' involvement and optimize their contributions in ED treatment alongside female caregivers. [Extracted from the article]

A recent study conducted by researchers at the Graduate School of Science aimed to improve the classification performance for specific movements in patients with cervical spinal cord injury (SCI). The study utilized a low-frequency multi-class electroencephalography (EEG) dataset and combined convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations associated with attempted arm and hand movements. The researchers used three different methods to select relevant features and validated the model's robustness using cross-validation. The study concluded that the research aims to contribute to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning framework. [Extracted from the article]

A new study conducted by researchers at Neurophet Inc. in Seoul, South Korea, explores the use of deep learning-based segmentation methods for automated scoring of Alzheimer's disease (AD) atrophy scale using T1-weighted magnetic resonance imaging (MRI). The study enrolled 3,959 participants and used visual assessments by neuroradiologists as well as deep learning-based segmentation software for analysis. The results showed a high correlation between volumetric-driven scoring and visual scoring for AD, mild cognitive impairment (MCI), and cognitively normal (CN) participants. The researchers concluded that automated atrophy scoring could be useful in clinical settings and multicenter studies with large datasets for determining AD subtypes. [Extracted from the article]

The article discusses The clinical diagnostic criteria for Attention-Deficit/Hyperactivity Disorder (ADHD) have been in existence in various forms since 1968. Still, all versions have emphasized—to varying degrees—problems with inattention, hyperactivity, and impulsivity as being central to the disorder or at least to its behavioral and cognitive manifestations.

The article offers information on Attention deficit hyperactivity disorder (ADHD) which represents an etiologically distinct disorder by comparing adults with late-onset ADHD symptoms against adults with child-onset persistent disorder.

The article profiles the condition attention-deficit/hyperactivity (ADHD) disorder in 2022. Topics discussed include its historical development, common errors in ADHD diagnosis, its assessment in home-schooled children or during the pandemic, and other conditions co-occurring in ADHD patients that may need interventions. Also noted are factors to consider for a comprehensive diagnostic assessment of ADHD.

When cannabis use elevates to a problematic level that justifies a diagnosis of cannabis use disorder (CUD), the problems that individuals experience appear no less serious than those that characterize an alcohol use disorder (AUD), a newly published study has found. The results of the study, based on 2012–13 national survey data, would appear to counteract any perception of lesser harms associated with CUD compared with AUD. [ABSTRACT FROM AUTHOR]

Dimensional sentiment analysis has received considerable attention because it can represent affective states as continuous numerical values on multiple dimensions such as valence (positive–negative) and arousal (excited–calm). Compared to the categorical approach, which represents affective states as several discrete classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. Traditional approaches to predicting dimensional sentiment scores typically treat each dimension independently without consideration of relations between dimensions. In fact, different dimensions may correlate with each other. For example, expressions with a higher valence score usually have a higher arousal score, And higher irony expressions usually have a lower valence score. Such relations between dimensions are useful for dimension score prediction. To this end, this study proposes a multi-dimensional relation model to incorporate relations between dimensions into deep neural networks for dimension score prediction. The proposed method has two modes: internal and external. The internal mode incorporates the relations between dimensions into sentence representations before prediction, whereas the external mode builds a linear regression model that can capture the relations between dimensions to refine the predicted scores after prediction. To evaluate the proposed method, we created a Chinese three-dimensional corpus with valence-arousal-irony (VAI) ratings. Experiments using various neural network architectures demonstrate that the proposed multi-dimensional relation model outperformed those that treat each dimension independently. In addition, the internal mode outperformed the external mode, and a combination of the two modes achieved the best performance. [ABSTRACT FROM AUTHOR]

While the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5) has served as the standard diagnostic tool for substance use disorders (SUDs) since its publication eight years ago, many researchers continue to rely on older Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) criteria that have been used over the years in numerous clinical studies and in prominent surveys such as the annual National Survey on Drug Use and Health. This in theory could create discrepancies between diagnoses based on differences in the diagnostic manuals, but a new analysis suggests that studies using the different criteria are largely comparable. [ABSTRACT FROM AUTHOR]

The article discusses Working with a psychiatric diagnosis, which includes It's important for therapists to consider their own views about psychiatric diagnosis, in order to offer clients a safe space to explore its subjective meanings.

Last month's announcement of the availability of genetic data from the National Epidemiologic Survey on Alcohol and Related Conditions‐III (NESARC‐III) came as welcome news to researchers looking for nationally representative data. The degree to which this information will ultimately guide treatment for substance use disorders (SUDs) remains largely unclear, given the relatively small effect of most of the currently known genotypes that influence addiction. [ABSTRACT FROM AUTHOR]

The article presents Attention deficit hyperactivity disorder (ADHD) research, which include Maternal Diabetes linked to risk for Neurodevelopmental Disorders; and risk with effect of exposure to Anesthesia or Abdominal Surgery .

A recent report from Victoria, Australia discusses the topic of chronic temporomandibular disorder (cTMD), a condition characterized by non-mechanical association without an obvious organic cause. The study explores the association between neural and psychological pathways in cTMD patients, highlighting the increased responsiveness of nociceptive neurons in the central nervous system. While genetic susceptibility to pain and various genes associated with the nervous and musculoskeletal systems are believed to play a role, environmental triggers and epigenetic changes are also thought to contribute to cTMD development. The research emphasizes the importance of addressing biological, psychological, and social factors in cTMD assessment and management. [Extracted from the article]

A study evaluated the performance of large language models (LLMs) in extracting International Classification of Diseases- tenth revision clinical modification (ICD-10-CM) codes from unstructured inpatient notes. The study compared the performance of various LLMs against a human coder using 50 inpatient notes. The results showed that current LLMs have poor performance in extracting ICD-10-CM codes compared to a human coder. The study suggests that further improvements are needed in automating this task. [Extracted from the article]

A recent report from Auburn University discusses the use of deep neural networks (DNN) in neuroimaging research for diagnosing brain disorders. The researchers trained a DNN classifier to distinguish between healthy subjects and those with posttraumatic stress disorder (PTSD) using brain connectivity data from functional magnetic resonance imaging. They used various interpretability algorithms to generate heatmaps of significant connections, which aligned with prior knowledge about brain alterations in PTSD. The study highlights the importance of transparency and physiological plausibility in interpreting DNN classifications for medical diagnostics. [Extracted from the article]

A report from the Center for Mental Health in Pune, India, examines the criticisms expressed by psychiatrists regarding the Indian Mental Healthcare Act 2017 (IMHCA 2017) in light of the UN Convention on the Rights of Persons with Disabilities (CRPD) guidelines. The IMHCA 2017, which replaced the 1987 Mental Health Act, aims to promote and protect the rights of individuals with mental illness. The study analyzed 33 manuscripts authored by psychiatrists, identifying seven prominent themes of criticism. The researchers assessed these themes in accordance with CRPD guidelines and formulated corresponding recommendations. [Extracted from the article]

A recent pilot study conducted by researchers at the School of Computer Science and Engineering in Tamil Nadu, India, explores the use of artificial intelligence (AI) to classify mental health disorders. The study utilizes advanced machine learning and deep learning techniques to analyze lifestyle and environmental factors that correlate with the prevalence of mental disorders. The researchers found that their AI models, particularly the convolutional neural network (CNN) architecture, accurately predicted mental health occurrences with a maximum accuracy of 99.79%. The study aims to assist healthcare professionals in diagnosing and treating neurological disorders promptly and provide insights for policymakers in formulating regulations on pollution and addiction. [Extracted from the article]

A recent study conducted by researchers at HCMC University of Technology and Education in Ho Chi Minh City, Vietnam, explores the use of artificial intelligence (AI) programs to analyze chest X-ray (CXR) images for the diagnosis of thoracic diseases and conditions. The researchers propose incorporating a variational information bottleneck (VIB) at the patch level to improve the generalizability of diagnostic support models. The VIB introduces a probabilistic model that enhances the model's ability to generalize on unseen data. Experimental results demonstrate improved accuracy and robustness when training and testing on different datasets. For more information, readers can refer to the journal article published in Bioengineering. [Extracted from the article]

A report from the University of Khartoum in Sudan discusses the impact of traditional healing on psychiatric management in rural African communities. The study highlights the lack of integration between traditional healers and modern mental health services provided by primary healthcare. The researchers suggest that collaboration between traditional healers and consultant psychiatrists should be encouraged, particularly in rural areas, to improve the integration of mental health services into primary healthcare and achieve universal health coverage for psychiatric disorders in Sudan. [Extracted from the article]