Abstract Background Suicidal ideation and attempts among adolescents constitute a significant public health challenge globally, with varying prevalence rates influenced by socio-cultural, economic, and psychological factors. In Pakistan, where mental health awareness is limited, understanding the determinants of these phenomena is crucial. This study aims to assess the prevalence of suicidal thoughts and behaviors among adolescents in both public and private schools in Jhang, exploring associated factors such as socioeconomic status, academic pressure, and familial relationships. Methods This descriptive cross-sectional study was conducted in Jhang, Pakistan, targeting adolescents aged 12 to 18 years enrolled in both public and private schools. A multi-stage sampling technique was used to ensure a representative sample, with schools selected randomly from each sector. The final sample size was determined to be 384 students, calculated using the following statistical parameters: a 5% margin of error (e), a 95% level of confidence (z), a prevalence (p) of suicidal ideation estimated at 50%, and a 10% non-response rate. Data was collected using an adapted version of Columbia severity rating scale that assessed suicidal ideation and attempts, alongside potential associated determinants such as socio-economic status, and familial factors. Statistical analysis was performed using SPSS software, employing descriptive statistics to determine prevalence rates and chi square test to identify significant associations between variables. p values less than 0.05 was considered statistically significant. Ethical approval was obtained from relevant authorities, and informed consent was secured from participants and their guardian. Results Most of the respondents were males 236 (61.30%), having urban residence 300 (77.92%). The findings from the current study revealed a 25% lifetime prevalence of suicidal ideation while 22% of adolescents were involved in suicidal behavior during their lifetime. 17 (4.42%) of the participants were involved in actual attempts in a lifetime while 7 (1.82%) in past three months. Family history of suicide attempts with and without death, substance abuse, and use of social media were significantly associated with suicidal ideation and behavior. Conclusion A 25% lifetime prevalence of suicidal ideation and 22% of suicidal behavior is alarming and critical. Government, semi-government non- government concerned departments should design strategies to minimize risk.
Pierluigi Colli, Gianni Gilardi, Andrea Signori
et al.
We address an optimal control problem governed by a system coupling a Brinkman-type momentum equation for the velocity field with a sixth-order Cahn-Hilliard equation for the phase variable, incorporating curvature effects in the free energy. The control acts as a distributed velocity control, allowing for the manipulation of the flow field and, consequently, the phase separation dynamics. We establish the existence of optimal controls, prove the Fréchet differentiability of the control-to-state operator, and derive first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. We also discuss the aspect of sparsity. Beyond its analytical novelty, this work provides a rigorous control framework for Brinkman-Cahn-Hilliard systems incorporating a curvature regularization, offering a foundation for applications in microfluidic design and controlled pattern formation.
Background/Objectives: Age-related macular degeneration, glaucoma, diabetic retinopathy (DR), diabetic macular edema, and pathological myopia affect hundreds of millions of people worldwide. Early screening for these diseases is essential, yet access to medical care remains limited in low- and middle-income countries as well as in resource-limited settings. We develop InSight, an AI-based app that combines patient metadata with fundus images for accurate diagnosis of five common eye diseases to improve accessibility of screenings. Methods: InSight features a three-stage pipeline: real-time image quality assessment, disease diagnosis model, and a DR grading model to assess severity. Our disease diagnosis model incorporates three key innovations: (a) Multimodal fusion technique (MetaFusion) combining clinical metadata and images; (b) Pretraining method leveraging supervised and self-supervised loss functions; and (c) Multitask model to simultaneously predict 5 diseases. We make use of BRSET (lab-captured images) and mBRSET (smartphone-captured images) datasets, both of which also contain clinical metadata for model training/evaluation. Results: Trained on a dataset of BRSET and mBRSET images, the image quality checker achieves near-100% accuracy in filtering out low-quality fundus images. The multimodal pretrained disease diagnosis model outperforms models using only images by 6% in balanced accuracy for BRSET and 4% for mBRSET. Conclusions: The InSight pipeline demonstrates robustness across varied image conditions and has high diagnostic accuracy across all five diseases, generalizing to both smartphone and lab captured images. The multitask model contributes to the lightweight nature of the pipeline, making it five times computationally efficient compared to having five individual models corresponding to each disease.
Maria Voreakou, George Kousiouris, Mara Nikolaidou
Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-based benchmarking for RabbitMQ, one of the main open source messaging frameworks. The involved system is described, as well as required components, and setup scenarios, involving different workloads and configurations among the tests as well as messaging system use cases. Alternative architectures are investigated and compared from an energy consumption point of view, for different message rates and consumer numbers. Differences in architectural selection have been quantified and can lead to up to 31\% reduction in power consumption. The resulting dataset is made publicly available and can thus prove helpful for architectures' comparison, energy-based cost modeling, and beyond.
As social changes accelerate, the incidence of psychosomatic disorders has significantly increased, becoming a major challenge in global health issues. This necessitates an innovative knowledge system and analytical methods to aid in diagnosis and treatment. Here, we establish the ontology model and entity types, using the BERT model and LoRA-tuned LLM for named entity recognition, constructing the knowledge graph with 9668 triples. Next, by analyzing the network distances between disease, symptom, and drug modules, it was found that closer network distances among diseases can predict greater similarities in their clinical manifestations, treatment approaches, and psychological mechanisms, and closer distances between symptoms indicate that they are more likely to co-occur. Lastly, by comparing the proximity d and proximity z score, it was shown that symptom-disease pairs in primary diagnostic relationships have a stronger association and are of higher referential value than those in diagnostic relationships. The research results revealed the potential connections between diseases, co-occurring symptoms, and similarities in treatment strategies, providing new perspectives for the diagnosis and treatment of psychosomatic disorders and valuable information for future mental health research and practice.
Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in clinical genetics. Recently, the proposition of genomic foundation models has improved the generic variant effect prediction (VEP) accuracy via weakly-supervised or unsupervised training. However, these VEPs are not disease-specific, limiting their adaptation at the point of care. To address this problem, we propose DYNA: Disease-specificity fine-tuning via a Siamese neural network broadly applicable to all genomic foundation models for more effective variant effect predictions in disease-specific contexts. We evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus on various cardiovascular diseases, where gene-disease relationships of loss-of-function vs. gain-of-function dictate disease-specific VEP. For non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory axis of RNA splicing, the most common non-coding pathogenic mechanism in established clinical VEP guidelines. In both cases, DYNA fine-tunes various pre-trained genomic foundation models on small, rare variant sets. The DYNA fine-tuned models show superior performance in the held-out rare variant testing set and are further replicated in large, clinically-relevant variant annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant effect prediction method, excelling in intra-gene generalization and generalization to unseen genetic variants, making it particularly valuable for disease associations and clinical applicability.
Erica Chiang, Divya Shanmugam, Ashley N. Beecy
et al.
Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases. However, a significant limitation of existing models is that they do not account for health disparities that can bias the observed data. To address this, we develop an interpretable Bayesian disease progression model that captures three key health disparities: certain patient populations may (1) start receiving care only when their disease is more severe, (2) experience faster disease progression even while receiving care, or (3) receive follow-up care less frequently conditional on disease severity. We show theoretically and empirically that failing to account for any of these disparities can result in biased estimates of severity (e.g., underestimating severity for disadvantaged groups). On a dataset of heart failure patients, we show that our model can identify groups that face each type of health disparity, and that accounting for these disparities while inferring disease severity meaningfully shifts which patients are considered high-risk.
Introduction
During the second wave of Russia-Ukraine war, around 8 million were internally displaced. Negative mental health impact of the war cannot be underestimate. Among internally displaced persons (IDPs), particularly vulnerable category is people with neurocognitive deficits. Stress associated with displacement may cause a change not only in cognitive functions, but also affect the onset or evaluation of behavioral and psychological symptoms.
Objectives
to study the prevalence of neuropsychiatric disorders in hospitalized patients with dementia, who were internally displaced and to compare with general population frequency.
Methods
64 IDPs with dementia (moderate and severe neurocognitive deficits) who were examined during March-September 2022. Cases of newly arrived persons were taken into account, after 1 to 30 days had passed since their relocation. The diagnosis was verified based on the ICD-10 criteria (F00-F01). The degree of neurocognitive deficit was determined using the MMSE and MoCA tests. Affective pathology was studied using the HAM-D, HAM-A, PHQ-9, AES scales. Psychotic symptoms and behavioral disorders were studied based on clinical examination and medical records. The study was conducted in Lviv Regional Psychiatric Hospital.
Results
among the examined patients, 60 (94% of all examined) had neuropsychiatric disorders. Among this sample, neuropsychiatric symptoms (an isolated symptom or a combination of two or more symptoms) occurred with the following frequency: apathy 16 (26.7%), anxiety49 (81.7%), depressive symptoms 32 (53.3%), agitation and aggression 41 (68.3%), hallucinatory symptoms 8 (13.3%), delusional disorders 34 (56.7%), wandering and disorientation 18 (30%), refusal of food and medicine 12 (20%)
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Conclusions
In this study the frequency of occurrence of certain neuropsychiatric syndromes among IDPs with dementia differed from the studied average frequency of occurrence of the same symptomatology among the general population. In particular, anxiety symptoms among IDPs with dementia occurred 42% more often than on average among patients with dementia in the general population (with a frequency of 81% vs 39%), agitation and aggression - 28% more often (68% vs 40%), and delusions - 16% more often (57% vs 31%). At the same time, symptoms such as apathy (by 19%) and refusal to eat (by 14%) were observed less often among IDPs with dementia than among dementia patients from the general population
Disclosure of Interest
None Declared
Summary A 49‐year‐old man presented with 3 years of leg pain and involuntary toe movements. He described the pain as mild burning, radiating from the left foot upward to the leg. On examination, there were involuntary continuous flexion‐extension movements of his left toes (video). Strength, sensation, and reflexes were normal. Lumbosacral MRI demonstrated diffuse degenerative disc disease with multi‐level mild‐to‐moderate foraminal stenosis. Nerve conduction studies were normal. EMG showed neurogenic potentials and active denervation changes in the left anterior tibial and soleus muscles consistent with radiculopathy. The diagnosis of painful legs and moving toes is discussed.
Neurosciences. Biological psychiatry. Neuropsychiatry, Neurology. Diseases of the nervous system
Silje Støle Brokke, Thomas Bjerregaard Bertelsen, Nils Inge Landrø
et al.
Abstract Background Suicide attempt is the most predictive risk factor of suicide. Trauma – especially sexual abuse – is a risk factor for suicide attempt and suicide. A common reaction to sexual abuse is dissociation. Higher levels of dissociation are linked to self-harm, suicide ideation, and suicide attempt, but the role of dissociation in suicidal behavior is unclear. Methods In this naturalistic study, ninety-seven acute psychiatric patients with suicidal ideation, of whom 32 had experienced sexual abuse, were included. Suicidal behaviour was assessed with The Columbia suicide history form (CSHF). The Brief trauma questionnaire (BTQ) was used to identify sexual abuse. Dissociative symptoms were assessed with Dissociative experiences scale (DES). Results Patients who had experienced sexual abuse reported higher levels of dissociation and were younger at onset of suicidal thoughts, more likely to self-harm, and more likely to have attempted suicide; and they had made more suicide attempts. Mediation analysis found dissociative experiences to significantly mediate a substantive proportion of the relationship between sexual abuse and number of suicide attempts (indirect effects = 0.17, 95% CI = 0.05, 0.28, proportion mediated = 68%). Dissociative experiences significantly mediated the role of sexual abuse as a predictor of being in the patient group with more than four suicide attempts (indirect effects = 0.11, 95% CI = 0.02, 0.19, proportion mediated = 34%). Conclusion The results illustrate the importance of assessment and treatment of sexual abuse and trauma-related symptoms such as dissociation in suicide prevention. Dissociation can be a contributing factor to why some people act on their suicidal thoughts.
T. Carvalho, A.L. Côrte-Real Lopes Nunes, L. Benedito
et al.
Introduction
Multiple Sclerosis (MS) is a demyelinating, neurodegenerative, and immune-mediated disease that affects the central nervous system. Usually co-occurs with difficulties in emotional regulation and psychopathology. Anxiety is one of the most common psychiatric manifestations in patients with MS. Nonetheless, empirical evidences on the joint predictive effect of MS clinical conditions and emotion regulation processes on the development of anxiety in MS patients are scarce.
Objectives
This preliminary study aimed to explore whether fatigue, physical disability (MS clinical conditions) and a low compassionate attitude (maladaptive emotion regulation process based on self-judgment, over-identification, and isolation) predict anxiety symptoms in MS patients.
Methods
A convenience sample of 107 patients with MS diagnosis and without other neurological disorders was used in this cross-sectional study. Participants completed the Anxiety Subscale of the Depression, Anxiety and Stress Scales-21, the Analogic Fatigue Scale, the World Health Organization Disability Assessment Schedule, and the Self-judgment, Isolation and Over-identification Subscales of the Self-Compassion Scale.
Results
All potential predictors showed significant correlations with anxiety symptoms and predicted this symptomatology through simple linear regressions. Therefore, they were selected as covariates of the multiple linear regression model, which explained 32% of the variance of anxiety symptoms. This model revealed that fatigue, physical disability, and low compassionate attitude are significant predictors.
Conclusions
The results support the relevance of psychological interventions for MS patients to implement effective strategies to regulate anxiety associated with fatigue and physical disability. Helping patients to adopt a more compassionate attitude toward the self can reduce their anxiety.
Disclosure
No significant relationships.
ObjectiveWe previously identified the independent predictors of recurrent relapse in neuromyelitis optica spectrum disorder (NMOSD) with anti-aquaporin-4 antibody (AQP4-ab) and designed a nomogram to estimate the 1- and 2-year relapse-free probability, using the Cox proportional hazard (Cox-PH) model, assuming that the risk of relapse had a linear correlation with clinical variables. However, whether the linear assumption fits real disease tragedy is unknown. We aimed to employ deep learning and machine learning to develop a novel prediction model of relapse in patients with NMOSD and compare the performance with the conventional Cox-PH model.MethodsThis retrospective cohort study included patients with NMOSD with AQP4-ab in 10 study centers. In this study, 1,135 treatment episodes from 358 patients in Huashan Hospital were employed as the training set while 213 treatment episodes from 92 patients in nine other research centers as the validation set. We compared five models with added variables of gender, AQP4-ab titer, previous attack under the same therapy, EDSS score at treatment initiation, maintenance therapy, age at treatment initiation, disease duration, the phenotype of the most recent attack, and annualized relapse rate (ARR) of the most recent year by concordance index (C-index): conventional Cox-PH, random survival forest (RSF), LogisticHazard, DeepHit, and DeepSurv.ResultsWhen including all variables, RSF outperformed the C-index in the training set (0.739), followed by DeepHit (0.737), LogisticHazard (0.722), DeepSurv (0.698), and Cox-PH (0.679) models. As for the validation set, the C-index of LogisticHazard outperformed the other models (0.718), followed by DeepHit (0.704), DeepSurv (0.698), RSF (0.685), and Cox-PH (0.651) models. Maintenance therapy was calculated to be the most important variable for relapse prediction.ConclusionThis study confirmed the superiority of deep learning to design a prediction model of relapse in patients with AQP4-ab-positive NMOSD, with the LogisticHazard model showing the best predictive power in validation.
Qiaolan Deng, Jin Hyun Nam, Ayse Selen Yilmaz
et al.
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. We conducted simulation studies to evaluate GGPA 2.0. The results indicate that incorporating functional annotation data using GGPA 2.0 does not only improve detection of disease-associated variants, but also allows to identify more accurate relationships among diseases. We analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood, especially B cells and regulatory T cells across multiple diseases. Psychiatric disorders were enriched for brain, especially prefrontal cortex and inferior temporal lobe for bipolar disorder (BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully identified the pleiotropy between BIP and SCZ. These results demonstrate that GGPA 2.0 can be a powerful tool to identify associated variants associated with each phenotype or those shared across multiple phenotypes, while also promoting understanding of functional mechanisms underlying the associated variants.
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. However, these models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones. To the best of our knowledge, limited work has been done on identifying, let alone reducing, model bias in skin disease classification and segmentation. In this paper, we examine DL fairness and demonstrate the existence of bias in classification and segmentation models for subpopulations with darker skin tones compared to individuals with lighter skin tones, for specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we propose a novel preprocessing, data alteration method, called EdgeMixup, to improve model fairness with a linear combination of an input skin lesion image and a corresponding a predicted edge detection mask combined with color saturation alteration. For the task of skin disease classification, EdgeMixup outperforms much more complex competing methods such as adversarial approaches, achieving a 10.99% reduction in accuracy gap between light and dark skin tone samples, and resulting in 8.4% improved performance for an underrepresented subpopulation.