Purpose: Schizophrenia is a challenging health condition characterized by a wide range of symptoms and varying responses to treatment. Studies on how genes affect drug responses have been promising. In particular, genes involved in the dopamine system of the brain, such as dopamine receptor D1 (<i>DRD1</i>), dopamine receptor D2 (<i>DRD2</i>), dopamine receptor D3 (<i>DRD3</i>), and catechol-O-methyltransferase (<i>COMT</i>) appear to play key roles in the response of patients to antipsychotic medications. This review examined studies on specific genes involved in the dopaminergic system, which plays a key role in how antipsychotic drugs work.Methods: The PubMed, Scopus, Web of Science, and Google Scholar databases were searched, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered with PROSPERO (CRD420251168294). Eligible studies included patients diagnosed with schizophrenia who were treated with antipsychotics and had dopaminergic gene variant data.Results: Of 73 screened articles, 26 met the inclusion criteria. Among the gene variants studied, <i>COMT</i> rs4680 and <i>DRD2</i> rs1800497 showed consistent links to treatment responses, particularly in reducing symptom severity as measured by the Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS) scores. The relationship between genetic variants and clinical outcomes was statistically significant (P=0.007), whereas the study design showed no such association (P=0.416). Forest plots revealed varied effect sizes across gene families, with <i>COMT</i> and <i>DRD2</i> generally showing odds ratios >1.0, indicating a favorable impact on treatment.Conclusion: Our findings suggest that certain dopaminergic gene variants, especially <i>COMT</i> rs4680 and <i>DRD2</i> rs1800497, may serve as valuable markers for predicting the response of patients with schizophrenia to antipsychotic therapy.

A 55-year-old woman with stage IV follicular lymphoma developed progressive multifocal leukoencephalopathy (PML) during rituximab maintenance therapy following bendamustine-rituximab induction. Although pembrolizumab administration was initiated for suspected PML, her neurological status deteriorated, ultimately leading to death. This case highlights the limited therapeutic response to pembrolizumab in profoundly immunocompromised patients, highlighting the need for timely diagnosis, immunovirological assessment, and individualized innovative management approaches for PML.

Purpose: This study assessed the neuromodulatory effect of transcranial direct current stimulation (tDCS) in internet gaming disorder (IGD), using source-level electroencephalography (EEG) to measure functional connectivity within the default mode network (DMN) and reward/salience network (RSN).Methods: Thirty-one patients with IGD participated in the study. After excluding five dropouts, 26 individuals (tDCS group n= 14; sham group n= 12) completed 10 sessions of either tDCS or sham stimulation. Resting-state EEG, IGD severity, and craving were measured before and 1 month after the intervention. Changes in connectivity within the DMN and RSN were assessed at each frequency band. <i>Post hoc</i> analysis was used to investigate the correlation between tDCS-induced connectivity changes and clinical improvement.Results: In the DMN, tDCS decreased delta and theta connectivity, and suppressed the increase in beta connectivity. Neural connections between the prefrontal cortex and parietal lobe were the main regions affected. In the RSN, tDCS increased neural connectivity in the theta and beta bands. The connections between the right dorsolateral prefrontal cortex (DLPFC) and cingulate cortex were the main regions affected. <i>Post hoc</i> analysis revealed a significant correlation between changes in right DLPFC connectivity and reduced craving.Conclusion: tDCS induces neural connectivity changes in the DMN and RSN. These findings support the potential use of tDCS as a neuromodulatory treatment for IGD, particularly in individuals with altered DLPFC connectivity patterns.

Psychiatric disorders exhibit complex genetic characteristics such as substantial polygenicity, pleiotropy, and genetic overlap, making them difficult to fully understand through studies focused solely on single genes or individual diseases. This review underscores the importance of multivariate and multi-trait analyses in psychiatric genetics and provides a comprehensive overview of major analytical tools, including their concepts, strengths, limitations, and applications. By addressing the current methodological challenges and proposing future directions, we aim to advance our understanding of the genetic architecture underlying psychiatric disorders and support progress towards precision medicine.

This report describes a diagnostically challenging case of bilateral atypical central serous chorioretinopathy (CSCR) in a 33-year-old man who presented with two distinct phenotypes. The left eye demonstrated bullous serous retinal detachment (SRD) with large retinal pigment epithelium (RPE) tears, while the right eye exhibited choroidal neovascularization and pigment epithelial detachment (PED) associated with subretinal scar tissue. The patient was initially misdiagnosed with unilateral Vogt–Koyanagi–Harada (VKH) disease at other hospitals and had received systemic corticosteroids, which may have precipitated an RPE tear and exacerbated SRD in the left eye. Multimodal imaging confirmed asymmetric atypical CSCR bilaterally. Following corticosteroid tapering, the patient received intravitreal ranibizumab and focal laser photocoagulation. Bullous SRD and RPE tears resolved, and both eyes remained stable for over 20 months. The absence of bullous SRD in the right eye may have been attributed to fibrosis overlying the PED. This case highlights the need to distinguish atypical CSCR from VKH disease.

Purpose: This study aimed to assess the global, regional, and national burdens of burns and airway injuries using Global Burden of Disease (GBD) data between 1990 and 2021, and to project future trends through 2050.Methods: Bayesian meta-regression was used to analyze the age- and sex-stratified burn prevalence and years lived with disability (YLDs). Future projections were estimated using the socio-demographic index (SDI)-adjusted regression and Das Gupta decomposition. We utilized GBD estimates from 1990 to 2021, covering burn and airway injury data stratified by age and sex across all countries and regions. Prevalence was measured as age- and sex-standardized rates per 100,000 individuals. YLDs were estimated as disability-adjusted burden measures. Future projections were derived using SDI-adjusted regression and Das Gupta decomposition.Results: In 2021, 248 million global burn cases and 6,900 airway burns were reported globally. By 2050, burns are projected to increase by 6.42%, mainly in Latin America and Eastern Europe, whereas airway burns are expected to decline 10-fold. From 1990 to 2021, the YLDs declined by 43.34% for burns and 26.79% for airway burns, with persistent disparities between low- and middle-income countries (LMICs).Conclusion: Burns pose substantial challenges to public health and the economy. Strengthening prevention, acute care, and rehabilitation is crucial, particularly in LMICs with limited access to healthcare.

Purpose: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are major gynecological malignancies. Precision oncology seeks to tailor treatments by targeting tumor-specific molecular profiles. Solute carriers (SLCs), a superfamily of over 400 transport proteins in 65 families, regulate molecular transport and represent potential therapeutic targets.Methods: Transcriptomic and proteomic datasets from The Cancer Genome Atlas and Human Protein Atlas were analyzed to evaluate the role of SLC transporters in CESC.Results: Comparative profiling revealed significant upregulation of SLC64A1, SLC7A1, SLC25A24, and SLC11A2 in tumor tissues versus normal controls. Elevated expression of these SLC genes correlated with reduced overall and disease-specific survival. Gene Set Enrichment Analysis showed enrichment of oncogenic pathways associated with SLC overexpression, including epithelial-mesenchymal transition, hypoxia, angiogenesis, ATP-binding cassette transporter activity, and transforming growth factor-beta, Notch, and Hedgehog signaling. These findings suggest that SLC transporters facilitate tumor progression and metastasis.Conclusion: SLC transporters may serve as predictive biomarkers and therapeutic targets. Their inhibition could disrupt metabolic and nutrient pathways essential for CESC progression, offering novel opportunities to improve outcomes.

Purpose: Colorectal cancer (CRC) is a major global health challenge, with an increasing incidence among younger populations. However, traditional screening methods lack comprehensiveness. The proposed work aimed to develop and evaluate a lightweight, accurate, deep learning model for classifying CRC from histo-logical images using MobileNetV3 (Google Research)-based transfer learning.Methods: This study presents a MobileNetV3-based transfer learning model, trained and validated on two publicly available datasets: LC25000 and Kather_texture_2016. The model was fine-tuned using optimized hyperparameters and evaluated in a Python-based environment with graphics processing unit (GPU) support. The performance metrics included classification accuracy and latency.Results: The proposed MobileNetV3-based model demonstrated high classification accuracy across all cat-egories and exhibited robust performance, even for cancer types not seen during training. The model achieved an average detection latency of approximately 0.2014 seconds per sample. These results highlight the efficiency of the model and its potential for integration into the clinical workflow.Conclusion: The proposed MobileNetV3-based transfer learning model offers a scalable and effective solution for analyzing CRC histological images. While the performance on benchmark datasets is promising, an external test using real-world clinical data is needed to support broader clinical deployment. Future studies will focus on external testing using hospital-grade datasets and on expanding the model’s capabilities to other cancer types.

Purpose: Gastroesophageal reflux disease (GERD) affects 13.98% of adults and is commonly linked to lower esophageal sphincter dysfunction. Symptoms include heartburn, regurgitation, and dysphagia. Initial therapy involves lifestyle measures and proton pump inhibitors, but many patients ultimately require surgery. Laparoscopic Nissen fundoplication remains the gold standard, though the optimal approach between Nissen-Rossetti and Floppy Nissen is debated. This study compared both techniques regarding operative parameters, complications, symptom control, and quality of life.Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Ovid Medline, Ovid Cochrane, and Scopus were searched for studies reporting demographic, operative, and outcome data. Risk of bias was assessed using the Cochrane RoB 2 tool for randomized trials and the MINORS index for non-randomized studies. Pooled analyses were conducted with random- and fixed-effects models in RevMan.Results: Twelve studies involving 1,165 patients were included: 320 underwent Nissen-Rossetti and 600 Floppy Nissen. Nissen-Rossetti demonstrated shorter operative times (mean difference –25.29 minutes, P < 0.00001) but was associated with higher esophageal dilation (16.3% vs. 4.4%; odds ratio [OR], 3.58; P = 0.0002) and dysphagia (25.1% vs. 15.6%; OR, 1.87; P = 0.009). Floppy Nissen showed shorter hospital stays (mean difference –0.18 days, P = 0.01), with comparable symptom resolution.Conclusion: The Floppy Nissen procedure offers superior postoperative outcomes, fewer complications, and shorter hospitalizations compared with Nissen-Rossetti. Additional randomized trials are needed to confirm these findings.

Endometriosis is a gynecologic inflammatory condition that affects up to 10% of reproductive-aged women worldwide. The disease exhibits heterogeneous presentations and is associated with a prolonged diagnostic delay, often exceeding seven years, because existing diagnostic modalities such as transvaginal ultrasound, magnetic resonance imaging, and the biomarker cancer antigen 125 (CA-125) are suboptimal. This review examines how machine learning (ML) is playing an increasingly significant role in early, non-surgical endometriosis diagnosis through two main approaches: symptom clustering and imaging integration. Unsupervised ML algorithms such as k-means, partitioning around medoids, and Bayesian networks have demonstrated success in identifying clinically informative endometriosis phenotypes from patient-reported symptoms and electronic health records. Concurrently, ML models such as convolutional neural networks and radiomics approaches have achieved high accuracy in lesion detection from imaging data, in some cases surpassing human interpretation. Despite these advances, significant challenges remain, including limited access to large, annotated multimodal datasets, the absence of widely accepted evaluation standards, and concerns regarding interpretability and generalizability. Multicenter, integrative studies and the incorporation of explainability techniques are recommended as potential strategies to address these gaps. Finally, multimodal ML approaches that combine symptomatology and imaging data hold substantial promise for reducing diagnostic delays, facilitating early intervention, and improving clinical outcomes in the management of endometriosis.