Abstract Background Body dysmorphic disorder is a common mental disorder among dermatology patients and causes significant psychological distress. However, its screening is usually missed in clinical settings. Moreover, limited studies have been conducted in sub-Saharan Africa, including Ethiopia. Hence, this study aimed to assess the magnitude and associated factors of body dysmorphic symptoms among dermatology patients in an Ethiopian setting. Objective The objective of this study was to assess the magnitude and associated factors of body dysmorphic disorder among dermatology patients during follow-up. Methods A hospital-based cross-sectional study was conducted on a total of 404 study participants from September 1 to 30, 2023. Systematic random sampling was utilized to recruit participants. The Body Dysmorphic Disorder Questionnaire (BDDQ), a screening tool based on DSM-IV criteria, was used to assess body dysmorphic disorders. Multivariable regression analysis was performed to evaluate the strength of associations. A P-value less than 0.05 indicated statistical significance. Results A total of 399 patients participated in the study, for a response rate of 98.8%. The prevalence of body dysmorphic disorder was 70(17.5%) with a 95% CI: (13.8, 21.30). Female sex (AOR = 2.81, 95% (CI = 1.30–6.08), low family income (AOR = 2.26, 95% (1.09–4.69), sexual abuse (AOR = 3.65, 95% (CI = 1.45–9.15), physical abuse (AOR = 2.85, 95% (CI = 1.10–7.39), low self-esteem (AOR = 3.31, 95% (CI = 1.14–9.61), depression (AOR = 2.64; 95% (CI = 1.26–5.53), anxiety (AOR = 2.23, 95% (CI = 1.07–4.63), stress (AOR = 2.76, 95% (CI = 1.34–5.684) and perceived stigma (AOR = 2.46, 95% (CI = 1.20–5.02) were factors associated with body dysmorphic disorder. Conclusion The prevalence of body dysmorphic disorder among dermatology patients was high and positively associated with symptoms such as depression, anxiety, stress, sexual and physical abuse, low self-esteem, and perceived stigma. Therefore, skin health care providers are recommended to screen the patients for body dysmorphic disorder among dermatology patients and facilitate referral to mental health care.
Laura Navarro, Laura Navarro, Laura Navarro
et al.
Brain damage (BD) caused by stroke, traumatic brain injury (TBI), or neurodegenerative conditions often results in persistent cognitive, motor, and emotional impairments. Music-based interventions (MI) have been explored as adjunctive rehabilitation strategies; however, the evidence remains fragmented. This systematic review and meta-analysis synthesize available research on the effects of MI on functional recovery following BD, due to acquired brain injury (ABI), including both TBI and non-TBI. From a total of 868 publications screened in PubMed, Embase, Scopus, Cochrane Library, Web of Science, and ClinicalTrials.gov, 90 were included, of which 41 met the criteria for quantitative evaluation and meta-analysis, to assess the state-of-the-art of research on music and BD in the fields of neuropsychology and cognitive sciences. The reviewed studies span a range of methodologies, including randomized controlled trials and qualitative research, and incorporate diverse MI strategies, such as active music-making, structured listening, and improvisational techniques. The findings indicate that music supports recovery across motor, cognitive, and, albeit to a lesser extent, communicative and psychosocial domains. The findings suggest beneficial effects of MI, particularly in gait function (z = 3.46, P < 0.01), upper extremity function (z = 6.11, P < 0.01; UEF), communication (z = 3.21, P < 0.01), cognitive rehabilitation (z = 3.29, P < 0.01), and emotional, behavioral, and social outcomes (z = 2.35, P = 0.02); notably, these effects were often supported by consistent statistical significance across multiple subgroup analyses (e.g., gait, UEF). This study highlights the therapeutic potential of music in neurorehabilitation and supports its integration into multidisciplinary treatment programs. Despite these promising findings, methodological heterogeneity, small sample sizes, and short intervention durations limit the generalizability of results. The evidence suggests that music may modulate key neurobiological pathways in BD, supporting its integration into evidence-based neurorehabilitation programs.
Neurosciences. Biological psychiatry. Neuropsychiatry, Neurology. Diseases of the nervous system
Vivien W. Y. Li, Yuliang Wang, Yuliang Wang
et al.
BackgroundThe purpose of this study is to determine the prevalence, risk factors, and characteristics of seizures and epilepsy in children with acquired brain injury (ABI), and compare their outcomes with children with ABI but no seizures.MethodBasic demographic data, clinical features, brain injury severity, seizure and epilepsy characteristics, and functional and neurodevelopmental outcomes of children with ABI with follow-up of at least 2 years were reviewed. Logistic regression was performed to determine the risk factors for seizures.ResultsThe study included 82 children with ABI due to tumors, trauma, hypoxia, stroke, infection, and neuro-inflammatory disorders. There were 43 (52%) boys. The median age at diagnosis was 2.9 years and median follow-up interval was 5 years. A total of 27 (33%) children experienced seizures and 20 (24%) were diagnosed as having epilepsy. Risk factors for seizures included cortical brain injury (p = 0.013) and central nervous system (CNS) infection (p = 0.001). Among those with seizures, seven had acute seizures within 7 days of ABI. The median time of onset of epilepsy after ABI was 2 years, and five children had refractory epilepsy (RE) needing more than two anti-epileptics. The hazard ratio (HR) for epilepsy in those with cortical brain injuries and CNS infections were 4.582 (95% CI [1.83, 11.49], p = 0.001) and 4.796 (95% CI [1.568, 14.67], p = 0.006), respectively. HR for epilepsy onset in those who had post-stroke seizures were 4.467, 95% CI [1.575, 12.67], p =0.005). Most subjects demonstrated significant improvements in Karnofsky Performance Scale (KPS) scores following rehabilitation (p < 0.0001); however, a greater proportion of children with post-ABI seizures required special educational services (p = 0.025).ConclusionCortical brain injuries, CNS infection and post-stroke seizures significantly increase the risk of epilepsy in children with ABI. While functional improvements were observed after rehabilitation, children with post-ABI seizures more often required special educational support. The identification of risk factors for seizures, time to epilepsy onset, and the functional outcomes can guide subsequent management and counseling.
Jorge Iranzo-Sánchez, Javier Iranzo-Sánchez, Adrià Giménez
et al.
This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track. Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system that adapts strong pre-trained models to streaming scenarios. We combine Whisper Large-V3-Turbo for ASR with the multilingual NLLB-3.3B model for MT, implementing lightweight adaptation techniques rather than training new end-to-end models from scratch. Our approach employs document-level adaptation with prefix training to enhance the MT model's ability to handle incomplete inputs, while incorporating adaptive emission policies including a wait-$k$ strategy and RALCP for managing the translation stream. Specialized buffer management techniques and segmentation strategies ensure coherent translations across long audio sequences. Experimental results on the ACL60/60 dataset demonstrate that our system achieves a favorable balance between translation quality and latency, with a BLEU score of 31.96 and non-computational-aware StreamLAAL latency of 2.94 seconds. Our final model achieves a preliminary score on the official test set (IWSLT25Instruct) of 29.8 BLEU. Our work demonstrates that carefully adapted pre-trained components can create effective simultaneous translation systems for long-form content without requiring extensive in-domain parallel data or specialized end-to-end training.
Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.
Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.
Abstract INTRODUCTION We tested associations between two retinal measures (optic disc pallor, peripapillary retinal nerve fiber layer [pRNFL] thickness) and four magnetic resonance imaging markers of cerebral small vessel disease (SVD; lacunes, microbleeds, white matter hyperintensities, and enlarged perivascular spaces [ePVSs]). METHODS We used PallorMetrics to quantify optic disc pallor from fundus photographs, and pRNFL thickness from optical coherence tomography scans. Linear and logistic regression assessed relationships between retinal measures and SVD markers. Participants (N = 108, mean age 51.6) were from the PREVENT Dementia study. RESULTS Global optic disc pallor was linked to ePVSs in the basal ganglia in both left (β = 0.12, standard error [SE] = 0.05, P < 0.05) and right eyes (β = 0.13, SE = 0.05, P < 0.05). Associations were also noted in different disc sectors. No pRNFL associations with SVD markers were found. DISCUSSION Optic disc pallor correlated with ePVSs in the basal ganglia, suggesting retinal examination may be a useful method to study brain health changes related to SVD. Highlights Optic disc pallor is linked to enlarged perivascular spaces in basal ganglia. There is no association between peripapillary retinal nerve fiber layer thickness and cerebral small vessel disease markers. Optic disc examination could provide insights into brain health. The sample included 108 midlife adults from the PREVENT Dementia study.
Neurology. Diseases of the nervous system, Geriatrics
Daniel Cortés-Denia, Manuel Pulido-Martos, Janine Bosak
et al.
Background
Several studies have examined the impact of leadership on employee well-being and health. However, this research has focused on a variable-centred approach. By contrast, the present study adopts a person-centred approach.
Aims
To (a) identify latent ‘resources’ profiles among two samples combining vigour at work, work engagement and physical activity levels; (b) examine the link between the identified profiles and indicators of psychological/physical health; and (c) test whether different levels of transformational leadership determine the probability of belonging to a particular profile.
Method
Two samples of workers, S1 and S2 (NS1 = 354; NS2 = 158), completed a cross-sectional survey before their annual medical examination.
Results
For S1, the results of latent profile analysis yielded three profiles: spiritless, spirited and high-spirited. Both high-spirited and spirited profiles showed a positive relationship with mental health, whereas spiritless showed a negative relationship. For S2, two profiles (spirited and spiritless) were replicated, with similar effects on mental health, but none of them was related to total cholesterol. In both samples, transformational leadership determined the probability of belonging to a particular profile.
Conclusions
Transformational leadership increased the probability of belonging to a more positive profile and, therefore, to better workers’ health.
Mateusz Daniol, Daria Hemmerling, Jakub Sikora
et al.
Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.
Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management.
This paper describes NAIST's submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-{German, Japanese, Chinese} speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance.
According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin. With the rise of AI, self-diagnosis and improved disease recognition have become more promising than ever; in spite of that, existing methods suffer from a lack of large-scale patient databases and outdated methods of study, resulting in studies being limited to only a few diseases or modalities. This study incorporates readily available and easily accessible patient information via image and text for skin disease classification on a new dataset of 26 skin disease types that includes both skin disease images (37K) and associated patient narratives. Using this dataset, baselines for various image models were established that outperform existing methods. Initially, the Resnet-50 model was only able to achieve an accuracy of 70% but, after various optimization techniques, the accuracy was improved to 80%. In addition, this study proposes a novel fine-tuning strategy for sequence classification Large Language Models (LLMs), Chain of Options, which breaks down a complex reasoning task into intermediate steps at training time instead of inference. With Chain of Options and preliminary disease recommendations from the image model, this method achieves state of the art accuracy 91% in diagnosing patient skin disease given just an image of the afflicted area as well as a patient description of the symptoms (such as itchiness or dizziness). Through this research, an earlier diagnosis of skin diseases can occur, and clinicians can work with deep learning models to give a more accurate diagnosis, improving quality of life and saving lives.
Intestinal microecology is established from birth and is constantly changing until homeostasis is reached. Intestinal microecology is involved in the immune inflammatory response of the intestine and regulates the intestinal barrier function. The imbalance of intestinal microecology is closely related to the occurrence and development of digestive system diseases. In some gastrointestinal diseases related to pediatric surgery, intestinal microecology and its metabolites undergo a series of changes, which can provide a certain basis for the diagnosis of diseases. The continuous development of microecological agents and fecal microbiota transplantation technology has provided a new means for its clinical treatment. We review the relationship between pathogenesis, diagnosis and treatment of pediatric surgery-related gastrointestinal diseases and intestinal microecology, in order to provide new ideas and methods for clinical diagnosis, treatment and research.
Rony Cleper, Nimrod Hertz-Palmor, Nimrod Hertz-Palmor
et al.
ObjectiveTo identify COVID-19 work-related stressors and experiences associated with sleep difficulties in HCW, and to assess the role of depression and traumatic stress in this association.MethodsA cross-sectional study of HCW using self-report questionnaires, during the first peak of the pandemic in Israel (April 2020), conducted in a large tertiary medical center in Israel. Study population included 189 physicians and nurses working in designated COVID-19 wards and a comparison group of 643 HCW. Mean age of the total sample was 41.7 ± 11.1, 67% were female, 42.1% physicians, with overall mean number of years of professional experience 14.2 ± 20. The exposure was working in COVID-19 wards and related specific stressors and negative experiences. Primary outcome measurement was the Insomnia Severity Index (ISI). Secondary outcomes included the Primary Care-Post Traumatic Stress Disorder Screen (PC-PTSD-5); the Patient Health Questionnaire-9 (PHQ-9) for depression; the anxiety module of the Patient-Reported Outcomes Measurement Information System (PROMIS); Pandemic-Related Stress Factors (PRSF) and witnessing patient suffering and death.ResultsCompared with non-COVID-19 HCW, COVID-19 HCW were more likely to be male (41.3% vs. 30.7%) and younger (36.91 ± 8.81 vs. 43.14 ± 11.35 years). COVID-19 HCW reported higher prevalence of sleep difficulties: 63% vs. 50.7% in the non-COVID group (OR 1.62, 95% CI 1.15–2.29, p = 0.006), mostly difficulty maintaining sleep: 26.5% vs. 18.5% (OR 1.65, 95% CI 1.11–2.44, p = 0.012). Negative COVID-19 work-related experiences, specifically witnessing patient physical suffering and death, partially explained the association. Although past psychological problems and current depression and PTSD were associated with difficulty maintaining sleep, the main association remained robust also after controlling for those conditions in the full model.Conclusion and RelevanceCOVID-19 frontline HCW were more likely to report sleep difficulties, mainly difficulty maintaining sleep, as compared with non-COVID-19 HCW working at the same hospital. Negative patient-care related experiences likely mediated the increased probability for those difficulties. Future research is needed to elucidate the long-term trajectories of sleep difficulties among HCW during large scale outbreaks, and to identify risk factors for their persistence.