Abstract Schizophrenia is a severe mental disorder with substantial clinical, economic, and humanistic impacts. This targeted literature review evaluated the burden of schizophrenia on patients and caregivers in Japan. Data were collected from PubMed, Ichushi, CiNii, J-STAGE, and the Cochrane Database (2013–2023) and supplementary materials from medical associations, government agencies, and patient organizations (2018–2023). The review focused on epidemiology, clinical management, societal, humanistic, and economic burdens experienced by patients and caregivers. The review identified 156 journal publications, 73 conference proceedings, and 37 additional data sources. Obesity, depression, and type 2 diabetes were highlighted as frequent comorbidities. Cognitive impairment in schizophrenia, assessed by the Brief Assessment of Cognition in Schizophrenia, indicated severe functional deficits with a Z-score of -2.1. Issues related to long-term hospitalization, including social isolation and inadequate post-discharge support, were also reported. Interventions aimed at improving cognitive function, fostering self-care, and strengthening community cooperation were identified as key factors in reducing early readmission rates. Caregivers experienced significant productivity losses, particularly due to presenteeism, leading to an estimated annual loss of JPY 2.4 million. The hand search further revealed a lack of stakeholder-driven initiatives to address the comprehensive burdens of schizophrenia, such as awareness campaigns, educational programs, and multidisciplinary approaches. This review underscores the multifaceted burdens of schizophrenia in Japan, emphasizing the urgent need for coordinated, evidence-based countermeasures involving multiple stakeholders, including patients, caregivers, healthcare professionals, and policymakers. To reduce burdens and improve healthcare, further research is needed to bridge the gap between required interventions and stakeholder engagement.
Zinc is one of the most abundant metal ions in the central nervous system (CNS), where it plays a crucial role in both physiological and pathological brain functions. Zinc promotes antioxidant effects, neurogenesis, and immune system responses. From neonatal brain development to the preservation and control of adult brain function, zinc is a vital homeostatic component of the CNS. Molecularly, zinc regulates gene expression with transcription factors and activates dozens of enzymes involved in neuronal metabolism. During development and in adulthood, zinc acts as a regulator of synaptic activity and neuronal plasticity at the cellular level. There are several neurological diseases that may be affected by changes in zinc status, and these include stroke, neurodegenerative diseases, traumatic brain injuries, and depression. Accordingly, zinc deficiency may result in declines in cognition and learning and an increase in oxidative stress, while zinc accumulation may lead to neurotoxicity and neuronal cell death. In this review, we explore the mechanisms of brain zinc balance, the role of zinc in neurological diseases, and strategies affecting zinc for the prevention and treatment of these diseases.
Background Ferroptosis, as a new form of cell death, is different from other cell deaths such as autophagy or senescence. Ferroptosis involves in the pathophysiological progress of several diseases, including cancers, cardiovascular diseases, nervous system diseases, and kidney damage. Since oxidative stress and iron deposition are the broad pathological features of neurological diseases, the role of ferroptosis in neurological diseases has been widely explored. Scope of review Ferroptosis is mainly characterized by changes in iron homeostasis, iron-dependent lipid peroxidation, and glutamate toxicity accumulation, of which can be specifically reversed by ferroptosis inducers or inhibitors. The ferroptosis is mainly regulated by the metabolism of iron, lipids and amino acids through System Xc−, voltage-dependent anion channels, p53, p62-Keap1-Nrf2, mevalonate and other pathways. This review also focus on the regulatory pathways of ferroptosis and its research progress in neurological diseases. Major conclusions The current researches of ferroptosis in neurological diseases mostly focus on the key pathways of ferroptosis. At the same time, ferroptosis was found playing a bidirectional regulation role in neurological diseases. Therefore, the specific regulatory mechanisms of ferroptosis in neurological diseases still need to be further explored to provide new perspectives for the application of ferroptosis in the treatment of neurological diseases.
Judith M. Gault, Nicola Cascella, Nidal Moukaddam
et al.
Abstract Introduction Ethical concerns have been raised by both current and historically controversial neurosurgical interventions for treatment-refractory schizophrenia and schizoaffective disorder (TR-SZ). Considering advances in next-generation deep brain stimulation (DBS), initial success in treating a few cases of TR-SZ, and how challenging trial enrollment is, transparency and disseminating knowledge about DBS is important, as is input from involved groups. Here information was presented about DBS as an experimental treatment option for TR-SZ to stakeholders to gauge enthusiasm after consideration of potential risks and benefits. Methods Stakeholders were presented with information about DBS (total n = 629). Opinions about whether DBS should be an option for people with TR-SZ and acceptable response rates considering DBS risks were collected from research participants with SZ, treatment-refractory Parkinson’s disease (TR-PD) approved for DBS, caregivers for either SZ or TR-PD participants, and attendees at medical school presentations. In addition, the attendees were asked to decide whether DBS is appropriate for 4 cases who want DBS, one with PD, 2 with OCD and 1 with SZ. Chi-square, pairwise comparisons, and Duncan Multiple Range Test were performed with significance at p < 0.05. Results Most (83%) research participants and presentation audience members agreed that DBS should be an option for TR-SZ and 40% thought the potential benefits outweigh the risks of DBS with at least a 41–60% response rate. Audience approval of DBS was similar for the PD (30%), SZ (52%) and the OCD case with psychosis (56%), but there was a higher rate of approval (77%) for the OCD case whose compulsions involved self-harm. The majority (73–86%) of the audience thought that they would want to try DBS if they had TR-PD, TR-OCD, or TR-SZ. Conclusions Despite difficulty in recruiting patients for DBS clinical trials for TR-SZ, the consensus among 83% of stakeholders was that DBS should be an option for people with severe TR-SZ. Our approach to disseminate general knowledge then gather opinions among diverse stakeholders was to ensure the development of DBS clinical trials for the new indication TR-SZ is a relevant option despite the known difficulties in enrollment. These findings may help prevent disparities in access to advanced DBS therapeutics.
Abstract Background Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection. Objectives To estimate the diagnostic accuracy of DL applied to NCCT for patient-level AIS detection and to examine prespecified sources of between-study heterogeneity. Methods We searched MEDLINE, Embase, and Web of Science (January 2010–May 2025). Eligible prospective or retrospective diagnostic studies evaluated DL on NCCT against an appropriate reference standard and reported (or allowed reconstruction of) patient-level 2 × 2 data. Two-gate case–control and lesion-only reports were excluded. Dual reviewers screened/extracted data; risk of bias was assessed with QUADAS-2, and AI-reporting against items adapted from STARD-AI/CLAIM/CONSORT-AI. Bivariate random-effects/HSROC models summarized sensitivity and specificity. Prespecified moderators were posterior-fossa inclusion, reference-standard robustness, and validation type. Sensitivity analyses included external-only cohorts, robust standards, posterior-fossa inclusion, and a “Direct AIS” construct subset. Results Of 1,899 records, 16 studies met inclusion; 13 contributed patient-level data to meta-analysis. Summary sensitivity was 0.91 (95% CI, 0.81–0.96) and specificity 0.90 (0.85–0.94). Sensitivity was lower for externally validated models than internally validated ones (0.82 [0.67–0.91] vs. 0.95 [0.89–0.98]) with similar specificity (0.88 [0.83–0.92] vs. 0.93 [0.82–0.97]). Findings were directionally robust across sensitivity analyses. QUADAS-2 frequently indicated concerns in patient selection and index-test domains; AI-reporting quality was mostly moderate, and explicit external validation remained uncommon. Conclusions DL applied to NCCT shows high accuracy for patient-level AIS detection. However, generalizability is the principal gap; broader external validation and guideline-concordant reporting are needed to support safe clinical adoption.
Abstract Background Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approaches. The advent of Artificial Intelligence (AI) offers a transformative opportunity to overcome these challenges. This study presents a novel contribution to the field by establishing an open-access dataset and developing advanced AI-driven tools to enhance the diagnostic accuracy of psychiatric disorders through NSR analysis. Methods This study introduces the world’s first open dataset specifically developed for AI studies of Niacin Skin-Flushing Response (NSR), a physiological biomarker associated with mental illnesses including depression, bipolar disorder, and schizophrenia. Leveraging this dataset, we developed an advanced Machine Learning (ML) approach designed for the broad diagnosis of mental disorders. Distinct from prior studies which are often limited to First Episode Schizophrenia and depend on specific devices, our approach champions device independence. The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. This segmentation is significantly enhanced by runtime data augmentation and trained on a robust train/validation/test dataset split. Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. The SVM training incorporates 5-fold cross-validation, Synthetic Minority Over-sampling Technique (SMOTE) for managing class imbalance, and hyperparameter tuning to optimize balanced accuracy. Results The established dataset comprises 600 high-quality NSR images from 120 individuals, encompassing a diverse cohort of healthy controls and patients with various mental illnesses. The developed AI tools offer an objective, swift, and highly accurate approach that is demonstrably independent of the diagnosed condition or the specific device used for image acquisition. Comparative results demonstrate that the ML-based diagnostic approach achieves a sensitivity ranging from 60.0 to 65.0% and a specificity from 75.0 to 88.3% across various types of illnesses, further underscoring its broad applicability and device independence. Conclusions This research conclusively demonstrates the significant potential of advanced AI tools in achieving precise diagnosis of psychiatric disorders, potentially surpassing human capabilities in both speed and accuracy. With the provision of the proposed open dataset and the introduction of novel methodologies, this study marks substantial progress in developing an objective and accurate NSR-based screening process for a wide spectrum of psychiatric disorders. Its enhanced applicability and independence from specific devices hold profound potential to substantially advance mental health diagnostics and contribute to improved patient outcomes globally.
Michelle Cherfane, Michelle Cherfane, Michelle Cherfane
et al.
ObjectivesThis study evaluates the effectiveness of a brief, culturally tailored educational video in improving stroke-related knowledge among residents of the United Arab Emirates (UAE).MethodsA pre-post intervention study was conducted with 407 UAE residents aged 25 years and older. Participants viewed a 3-min educational video addressing stroke symptoms, risk factors, and preventive strategies. Stroke knowledge was measured using a structured questionnaire immediately before and after the video. Statistical analyses included paired t-tests, repeated measures ANOVA, and linear regression models.ResultsStroke knowledge significantly increased following the intervention (mean score: 20.80 pre-test to 23.53 post-test; p < 0.001), with notable improvements in identifying symptoms and risk factors. Regression analyses indicated that female gender, higher education, and healthy lifestyle practices positively influenced knowledge gains, whereas older age was associated with smaller improvements.ConclusionA brief, culturally relevant audiovisual intervention effectively enhances stroke-related knowledge. Such scalable educational tools should be integrated into global public health strategies to promote earlier stroke recognition and intervention.
Omar Faruq Shikdar, Fahad Ahammed, B. M. Shahria Alam
et al.
Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee of success. Automating this process will improve both the efficiency and the success rate of identifying tea leaf diseases. The purpose of this study is to create an automated system that can classify different kinds of tea leaf diseases, allowing farmers to take action to minimize the damage. A novel dataset was developed specifically for this study. The dataset contains 5278 images across seven classes. The dataset was pre-processed prior to training the model. We deployed three pretrained models: DenseNet, Inception, and EfficientNet. EfficientNet was used only in the ensemble model. We utilized two different attention modules to improve model performance. The ensemble model achieved the highest accuracy of 85.68%. Explainable AI was introduced for better model interpretability.
Epstein-Barr virus (EBV), also known as human herpesvirus 4, is a double-stranded DNA virus that is ubiquitous in 90–95% of the population as a gamma herpesvirus. It exists in two main states, latent infection and lytic replication, each encoding viral proteins with different functions. Human B-lymphocytes and epithelial cells are EBV-susceptible host cells. EBV latently infects B cells and nasopharyngeal epithelial cells throughout life in most immunologically active individuals. EBV-infected cells, free viruses, their gene products, and abnormally elevated EBV titers are observed in the cerebrospinal fluid. Studies have shown that EBV can infect neurons directly or indirectly via infected B-lymphocytes, induce neuroinflammation and demyelination, promote the proliferation, degeneration, and necrosis of glial cells, promote proliferative disorders of B- and T-lymphocytes, and contribute to the occurrence and development of nervous system diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, acute cerebellar ataxia, meningitis, acute disseminated encephalomyelitis, and brain tumors. However, the specific underlying molecular mechanisms are unclear. In this paper, we review the mechanisms underlying the role of EBV in the development of central nervous system diseases, which could bebeneficial in providing new research ideas and potential clinical therapeutic targets for neurological diseases.
BackgroundThe new antiseizure medications (ASMs) and non-invasive brain stimulation (NIBS) are controversial in controlling seizures. So, this network meta-analysis aimed to evaluate the efficacy and safety of five third-generation ASMs and two NIBS therapies for the treatment of refractory epilepsy.MethodsWe searched PubMed, EMBASE, Cochrane Library and Web of Science databases. Brivaracetam (BRV), cenobamate (CNB), eslicarbazepine acetate (ESL), lacosamide (LCM), perampanel (PER), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS) were selected as additional treatments for refractory epilepsy in randomized controlled studies and other cohort studies. Randomized, double-blind, placebo-controlled, add-on studies that evaluated the efficacy or safety of medication and non-invasive brain stimulation and included patients with seizures were uncontrolled by one or more concomitant ASMs were identified. A random effects model was used to incorporate possible heterogeneity. The primary outcome was the change in seizure frequency from baseline, and secondary outcomes included the proportion of patients with ≥50% reduction in seizure frequency, and the rate of treatment-emergent adverse events.ResultsForty-five studies were analyzed. The five ASMs and two NIBS decreased seizure frequency from baseline compared with placebo. The 50% responder rates of the five antiseizure drugs were significantly higher than that of placebo, and the ASMs were associated with fewer adverse events than placebo (p < 0.05). The surface under the cumulative ranking analysis revealed that ESL was most effective in decreasing the seizure frequency from baseline, whereas CNB provided the best 50% responder rate. BRV was the best tolerated. No significant publication bias was identified for each outcome index.ConclusionThe five third-generation ASMs were more effective in controlling seizures than placebo, among which CNB, ESL, and LCM were most effective, and BRV exhibited better safety. Although rTMS and tDCS did not reduce seizure frequency as effectively as the five drugs, their safety was confirmed.Systematic review registrationPROSPERO, https://www.crd.york.ac.uk/prospero/ (CRD42023441097).
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its potential in streamlining the complex process of literature retrieval and summarization to identify diseases associated with specific genetic variations.
Xavier Sánchez-Sáez, Isabel Ortuño-Lizarán, Carla Sánchez-Castillo
et al.
Abstract Background The main clinical symptoms characteristic of Parkinson’s disease (PD) are bradykinesia, tremor, and other motor deficits. However, non-motor symptoms, such as visual disturbances, can be identified at early stages of the disease. One of these symptoms is the impairment of visual motion perception. Hence, we sought to determine if the starburst amacrine cells, which are the main cellular type involved in motion direction selectivity, are degenerated in PD and if the dopaminergic system is related to this degeneration. Methods Human eyes from control (n = 10) and PD (n = 9) donors were available for this study. Using immunohistochemistry and confocal microscopy, we quantified starburst amacrine cell density (choline acetyltransferase [ChAT]-positive cells) and the relationship between these cells and dopaminergic amacrine cells (tyrosine hydroxylase-positive cells and vesicular monoamine transporter-2-positive presynapses) in cross-sections and wholemount retinas. Results First, we found two different ChAT amacrine populations in the human retina that presented different ChAT immunoreactivity intensity and different expression of calcium-binding proteins. Both populations are affected in PD and their density is reduced compared to controls. Also, we report, for the first time, synaptic contacts between dopaminergic amacrine cells and ChAT-positive cells in the human retina. We found that, in PD retinas, there is a reduction of the dopaminergic synaptic contacts into ChAT cells. Conclusions Taken together, this work indicates degeneration of starburst amacrine cells in PD related to dopaminergic degeneration and that dopaminergic amacrine cells could modulate the function of starburst amacrine cells. Since motion perception circuitries are affected in PD, their assessment using visual tests could provide new insights into the diagnosis of PD.