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Protocol for an undergraduate student-led scoping review of methods used to conduct inclusive focus groups with autistic adolescents
Although focus groups gather early-stage input effectively, our initial literature review found few focus group studies conducted with autistic adolescents (ages 12–19), despite the potential for focus groups to provide a safe, peer-based setting that encourages autistic adolescent engagement in research. Scoping reviews of focus groups for children and people with disabilities exist, but not for autistic adolescents. We aim to fill this gap. Consequently, we plan to conduct a scoping review to identify the methods used to design inclusive focus groups for autistic adolescents. Because few relevant studies exist, we describe steps to search both academic databases and online sources (X, YouTube, Google). We detail how we will leverage our team composition, which is led by a large group of undergraduate students, some of whom are neurodiverse, to enhance the rigor and reproducibility of the scoping review. These steps include accounting for algorithms personalizing search results from online sources and the risk of encountering false information that could cause harm. We will analyze the results to show 1) the extent to which focus groups on autistic adolescents are conducted with autistic adolescents; 2) characteristics of autistic adolescents included in focus groups and underrepresented populations; 3) steps taken to design accessible focus groups for autistic adolescents; 4) which methods were feasible for and acceptable to autistic adolescents. The results of our scoping review will be an important step toward including input from autistic adolescents in the early stages of a project and, more broadly, in the research process.
Bone regeneration using adipose derived stem cell spheroids within 3D printed scaffolds in a rabbit radial defect model
Predictors of self-reported practice in ventilator-associated pneumonia (VAP) prevention among critical care nurses in Sarawak public hospitals
Background and objective Ventilator-associated pneumonia (VAP), a leading cause of ICU mortality, remains prevalent in Southeast Asia, with limited data on critical care nurses’ knowledge and prevention practices in Malaysia. The purpose of this study was to assess knowledge, self-reported practices, barriers, and predictors of VAP prevention among critical care nurses in Sarawak, Malaysia. Methods This cross-sectional study was conducted from July to August 2023 at four public hospitals in Sarawak, Malaysia. Universal sampling was used to recruit nurses from various critical care units managing patients requiring ventilator support. A self-administered questionnaire, consisting of four sections, was employed to gather background information from nurses, assess their knowledge, self-reported practices, and identify barriers related to VAP prevention. Results A total of 298 critical care nurses participated in the study. Of these, 66.8% demonstrated poor knowledge of ventilator-associated pneumonia (VAP) prevention; however, self-reported practices with VAP prevention were significantly high at 76.5%. A Pearson’s correlation test revealed a significant association between the nurses’ knowledge and their self-reported practices related to VAP prevention (p < 0.001). Additionally, multiple regression analyses identified several significant predictors of critical care nurses’ self-reported practices in VAP prevention, including their level of knowledge, type of unit, number of official beds, and sociodemographic factors (p < 0.05). While knowledge positively influenced self-reported practices, its impact was relatively minor compared to sociodemographic factors. Barriers to VAP prevention included nursing staff shortages, forgetfulness, and lack of written protocols. Conclusions The prevention of ventilator-associated pneumonia (VAP) is a multidisciplinary challenge, emphasizing the crucial role of critical care nurses. The findings from this study underscore the necessity for updated, evidence-based interventions that target knowledge gaps, self-reported practices, and barriers to effective VAP prevention.
A zero-shot LLM framework for multimodal grievance classification, urgency scoring, and abuse detection in civic feedback systems
Predicting and explaining life satisfaction among older adults using tree-based ensemble models and SHAP: Evidence from the digital divide survey
As digital transformation continues to penetrate various sectors of society, the issue of the digital divide has become increasingly prominent. Against the backdrop of accelerating population aging, the barriers that older adults face in accessing and utilizing digital information have exerted a profound impact on their quality of life. This study employs tree-based ensemble learning algorithms to predict and identify the key factors of the digital divide that influence life satisfaction among older adults. It also evaluates the predictive performance of these models, thereby providing interpretive insights into the impact of the digital divide on subjective well-being. Using original data from the ‘2023 Report on Digital Information Divide Survey’ conducted by the National Information Society Agency of South Korea, this study constructs an analytical framework that integrates both predictive capability and interpretability. First, the XGBoost model is employed to conduct feature importance analysis, identifying 15 key variables that are highly influential in predicting life satisfaction. These variables are further examined using the SHAP method to provide interpretive insights into their contributions. Subsequently, multiple tree-based ensemble learning algorithms—including Random Forest, XGBoost, LightGBM, and CatBoost—are applied to compare their predictive performance. The results indicate that variables related to technological self-efficacy, digital information literacy, social capital, experience and perception of AI services, and household monthly income are significant predictors of life satisfaction among older adults. Among the models tested, CatBoost demonstrates superior overall predictive accuracy, suggesting its effectiveness in forecasting life satisfaction in this demographic. This study expands the application of machine learning in areas such as aging research and the digital divide and proves the effectiveness of ensemble learning algorithms in predicting digital divide factors that affect the life satisfaction of older adults. This approach provides a novel and powerful methodological for addressing complex social problems. Moreover, the study uncovers the structural configuration of key digital information factors associated with life satisfaction, offering data-driven insights into the mechanisms through which the digital divide influences well-being. These results have practical implications for enhancing digital inclusion, improving adaptability among older adults, and fostering a stronger sense of participation and happiness in digital society.
Effects of feeding different baits on the growth of Scylla paramamosain megalopa and the bacterial community in a mariculture system
Diagnostic yield of exome sequencing in nonobstructive azoospermia (NOA): A systematic review and meta-analysis
Azoospermia is considered as the most severe form of male infertility. The application value of exome sequencing (ES) in males with non-obstructive azoospermia (NOA) remains unclear. This study aims to review the known genetic causes of NOA and evaluate the diagnostic yield of ES in males diagnosed with idiopathic NOA. We performed a systematic database search in Ovid MEDLINE, EMBASE, CINAHL, Scopus, Cochrane Central Register of Controlled Trials, and Web of Science from database inception to March 2025. Two independent reviewers assessed the literature and included those studies investigating the utility of ES testing in men diagnosed with NOA and fulfilling the eligibility criteria. The pooled diagnostic yield was calculated using single-proportion analysis with random–effects modeling, and confidence intervals (CI) were estimated using the Clopper–Pearson exact method. A total of nine studies were included, and the qualities were assessed to be moderate to high via the modified STARD. Among the cohorts analyzed (nine studies comprising 1,728 individuals with NOA), the overall diagnostic yield of ES testing was 15% (95% CI: 10%–20%; low-certainty evidence). Of the 270 positive cases identified through ES testing, mutations in 262 genes were detected, with AR, TEX11, FANCM, TDRD9, PNLDC1, M1AP, FBXO15, and DMRT1 being the most frequently observed. Among these cases, only 11.11% (5/45) reported successful testicular sperm extraction. The considerable heterogeneity indicates that the pooled prevalence estimates of the diagnostic yield of ES testing in NOA—approximately 15%—may overestimate the true diagnostic rate in the general NOA population. This estimate should thus be interpreted as an average across diverse clinical and methodological contexts, rather than a precise point estimate reflecting a uniform underlying effect. Future research, particularly large-scale studies using standardized protocols, is crucial to generate more accurate, reliable, and generalizable estimates of the diagnostic yield of ES testing in NOA.
An MILP approach for optimal operation of unbalanced distribution networks through coordinated network reconfiguration and SOP utilization
Phase II trial protocol of focal prostate ablation combined with androgen deprivation therapy for prostate cancer treatment
Background Prostate cancer (PCa) is one of the most commonly diagnosed cancers. Treatments for PCa with less adverse effects than whole gland interventions, such as focal therapy (FT), often involve a higher risk of PCa recurrence. Combining FT with other treatments could increase the efficacy while maintaining a low side effect profile. This study aims to determine the proportion of residual/recurrent clinically significant PCa following the combination treatment of three months of androgen deprivation therapy (ADT) and FT of the prostate, as well as the safety of this treatment regimen. Methods This study will be a single arm phase II trial with a recruitment goal of 57 patients with treatment naïve non-metastatic intermediate risk PCa. Patients will complete the I-PSS, SHIM, and EPIC-26 questionnaires at the screening visit. Patients will then comply with a three-month treatment course of ADT. Eight weeks after beginning ADT, patients will undergo FT of the prostate via high intensity focused ultrasound or cryoablation. Follow up visits will occur every three months for a year post FT to monitor for side effects, repeat questionnaires, perform a clinical assessment, and obtain PSA and testosterone values. Twelve months after FT, a surveillance multiparametric MRI and MRI-targeted biopsy will be performed to assess for treatment failure. Discussion The primary endpoint of this trial is to determine the proportion of men with clinically significant PCa as evaluated by a surveillance mpMRI and MRI-TB at 12-months following FT. If successful, this treatment approach could offer a new option for treatment with fewer side effects than whole gland interventions and more efficacy than FT alone. Furthermore it could inform the need for further research into multimodal treatment options for PCa. Clinical trial registration ClinicalTrials.gov, NCT05790213. Registered on March 30, 2023.
Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding
Abstract The increasing complexity of whole slide images in histopathology requires models that would be accurate across magnifications while being robust to visual, topological, and contextual variations in the setting. Thus far, existing approaches have either failed to generalize across various resolutions, have ignored the inherent structural relationships within tissue architecture, or have not integrated a mechanism to adaptively prioritize samples during training. Aside from this, most approaches do not consider both pixel-level appearance and morphological context, which restricts their diagnostic reliability sets. This research aims to address these limitations by providing a framework for Multiscale Tumor Characterization by synergizing RepVGG-DINO encoders, self-distilled visual transformers, and hierarchical attentions. The Pathology-Adaptive Uncertainty-Aware Consistency (PAUAC) Framework ensures consistency in prediction across 10 and 40 and introduces a dual-branch consistency model with uncertainty-weighted KL divergence regularization. The Structural Attention-Constrained Graph Regularizer (SACGR) which is a topology-aware visual encoding technique focuses on constrains attention in the ViT decoders thereby embedding spatial priors from superpixel-based graphs into them. The Multiscale Pathology Curriculum Scheduler (MPCS) creates a sample prioritization mechanism based on entropy, guiding the training from simpler to more complex tissue patterns. The Transformer-Driven Dual-Modality Morphometry Network integrates H&E image features with Voronoi-based nuclear morphometry using cross-modal self-attention to enhance representational richness for the process. Finally, the Contrastive Cell-Contextual Representation Alignment (CCCRA) module improves embedding consistency across different magnifications by using positional contrastive learning sets. The combination of these modules brings measurable improvements (+2.3% Dice score, +21% faster convergence, +3.7% accuracy for morphologically ambiguous samples, and +12.6% normalized mutual information in embeddings) sets. This work marks a great leap in tumor characterization within histopathology, establishing resolution-aware, topology-constrained, and morphology-fused learning in an interpretable and scalable manner for the process.
Spatial autocorrelation and determinants of low uptake of breast cancer screening among women of reproductive age: A mixed-effect multilevel analysis of Tanzanian population-based survey
Introduction Breast cancer remains an important public health problem with high mortality in low-income countries like Tanzania. This is because of the low uptake of screening for breast cancer, an intervention that could be cost-effective and significant in reducing mortality and poor prognosis in such a setting. This is a population-based survey to uncover the spatial distribution and determinants of low uptake of breast cancer screening among women of reproductive age in Tanzania. Methods This analytical cross-sectional study utilized data from 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). A total of 15254 women aged 15–49 years were included in the analysis. The outcome variable was the uptake of breast cancer screening, coded as “1” for the women who reported a doctor or other healthcare provider examined their breasts to check for cancer, and “0” otherwise. Descriptive and geospatial analyses were conducted to assess patterns of screening uptake across regions. To identify associated factors, a mixed-effect multilevel logistic regression analysis was performed using Stata version 17. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported, and a significance level of p < 0.05 was used. Results The findings revealed that only 5% of the respondents reported having undergone breast cancer screening in Tanzania. The lowest uptake was in the Western (2.17%) and Southern (3.34%) zones of Tanzania. Regions with the poorest uptake of breast cancer screening were Kigoma (1.68%), Katavi (1.94%), Singida (1.54%), and Tabora (1.66%). Women with older ages, formal education, health insurance coverage, and reading newspapers, magazines, or using the internet had higher odds of uptake breast cancer screening than their counterparts. Conclusions The uptake of breast cancer screening remains low throughout Tanzania and worst situation was noted in rural areas. Formal education, insurance coverage, and access to information continue to propel the low uptake of breast cancer screening in Tanzania. The fair distribution of health-promotive services could be vital in increasing uptake of breast cancer screening for the early detection and prevention of mortalities and other outcomes related to this severe disease.
Modulating superconductivity in elementary materials by doping
SEANN: A domain-informed neural network for epidemiological insights
In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with explainable AI (XAI) tools, offer new opportunities for this task. Despite their potential, these methods face challenges due to the limited availability of high-quality, high-quantity data in this field. To address these challenges, we introduce SEANN, a novel approach for informed DNNs that leverages a prevalent form of domain-specific knowledge: Pooled Effect Sizes (PES). PESs are commonly found in published Meta-Analysis studies, in different forms, and represent a quantitative form of a scientific consensus. By integrating PES into the training loss, we demonstrate—under controlled simulations—significant improvements in predictive generalization and in the epidemiological plausibility of the learned relationships, relative to a domain-knowledge agnostic neural network.
Fast and efficient intracellular delivery of large size nanoparticles into mammalian cells by in situ electroporation
The innate immune IMD pathway is a key regulator of gut microbiome and metabolic homeostasis in the black tiger shrimp (Penaeus monodon)
The gut microbiome plays a fundamental role in host health and homeostasis, yet immune mechanisms regulating this relationship remain poorly understood in commercially important invertebrate such as the black tiger shrimp ( Penaeus monodon ). We employed a multiomics approach, combining RNA interference (RNAi) with transcriptomic, metabolomic, and 16S rRNA gene profiling, to investigate how the innate immune Toll and IMD pathways regulate gut health. We systematically suppressed key signaling components, MyD88 (Toll) and Relish (IMD), under non-pathogenic conditions. Knockdown of the IMD pathway transcription factor, Relish , triggered a profound and selective response across all measured biological layers. We observed a disproportionately large transcriptomic change, with 1,362 differentially expressed genes (DEGs) in the Relish knockdown group compared to only 333 DEGs in the MyD88 knockdown group. This was accompanied by a targeted alteration in immune effectors, including the upregulation of lysozyme C-like (log 2 fold change = 2.44) and a strong suppression of penaeidin 5 (log 2 fold change = −3.62). At the microbial level, while overall community structure remained stable, a selective shift was observed, the abundance of specific Gram-negative genera, particularly Photobacterium and Shewanella , was significantly reduced, yet Pseudoalteromonas were enriched in the Relish knockdown group. Metabolomic analysis further revealed that the Relish -suppressed shrimp had a distinct metabolic signature, marked by a decrease in bacterial-associated metabolites like D-alanyl-D-alanine and an increase in pro-inflammatory markers such as succinic acid and 8-HETE. Our findings showed that in P. monodon , the IMD pathway is the primary and central regulator of gut microbiome and metabolic homeostasis. This study provides novel insights into the dynamic interplay between innate immunity and the gut microbiome in a crustacean, identifying the IMD pathway as a promising target for developing strategies to enhance shrimp health and the sustainability of the global aquaculture industry.
Characteristics of the meibomian gland in a population without dry eye symptoms
Carbon footprint comparison of video intubation tools: Disposable laryngoscopes, reusable laryngoscopes, and stylets
Purpose As healthcare systems grapple with their 5% global carbon footprint contribution, sustainable medical device selection emerges as a critical decarbonization lever. This life cycle assessment (LCA) quantifies environmental disparities among three prevalent video intubation tools—Disposable video laryngoscopes (VLs), reusable VLs, and video Stylets—to guide evidence-based procurement. Methods Using International Organization for Standardization (ISO)14040 compliant life cycle assessment (LCA) methodology—the international standard defining LCA principles and framework—we quantified cradle-to-grave emissions for three video intubation devices manufactured by Zhejiang UE Medical Corp. The functional unit (one tracheal intubation) incorporated material extraction, manufacturing, low-temperature LTPS/ HLD, transportation, and disposal. SimaPro 9.4.0 with Ecoinvent 3.8 database calculated CO₂ equivalents (kg CO₂e), validated through sensitivity analyses of sterilization loading (10–80 devices/cycle) and regional grids. Results The HLD-disinfected video stylet demonstrated superior environmental performance, emitting 98.24 kg CO₂e per 500 procedures—45.8% and 42.0% lower than reusable VLs (181.45 kg CO₂e) and disposable VLs (169.47 kg CO₂e), respectively. Sensitivity analyses identified sterilization loading as the dominant variable: half-load (50% chamber utilization) reduced emissions by 89–91% versus single-device processing, with full-load optimization yielding incremental 11–14% reductions. Process and regional variability further revealed that HLD decreased emissions by 19–24% compared to LTPS, while grid carbon intensity caused 24–33% variability (India vs. EU). Scenario comparisons confirmed the video stylet’s environmental dominance across sterilization methods—even with LTPS (349.99 kg CO₂e/500 uses), it maintained a 45% reduction over reusable VL baselines, whereas HLD-treated video stylets (94.32 kg CO₂e) showed 6.7-fold lower emissions than disposable VLs and 59% below HLD-reprocessed reusable VLs. Conclusions HLD-reprocessed video stylets are the environmentally optimal choice for high-volume, low-infection-risk settings. For low-throughput or high-risk scenarios, providers should balance environmental impacts with clinical requirements through frequency and resource assessment.
Gut microbiota profiling in Lebanese ulcerative colitis patients and healthy controls from a pilot study
Disruption of epidermal growth factor receptor signaling and cytoskeletal dynamics by mebendazole and gefitinib synergistically impairs paracrine cytokine signaling in non-small cell lung cancer and triple-negative breast cancer Cell lines
Background Aberrant paracrine cytokine signaling and dysregulated signal transduction are critical drivers of tumor progression and therapeutic resistance in aggressive cancers such as non-small cell lung cancer and triple-negative breast cancer. This study aimed to explore a dual-targeting strategy using mebendazole, a repurposed anti-parasitic agent known to disrupt microtubules, in combination with gefitinib, an epidermal growth factor receptor tyrosine kinase inhibitor. The objective was to assess the combinatorial impact on cell viability and key regulatory pathways involved in inflammation, mitotic control, and nuclear transport. Methods Human lung adenocarcinoma (A549) and triple-negative breast cancer (MDA-MB-231) cell lines were treated with mebendazole, gefitinib, or a combination of both. Cell viability in both the cell lines was investigated using the MTT assay, while transcriptional profiling was conducted exclusively in the A549 NSCLC cell line to assess cytokine and regulatory gene modulation. Quantitative reverse transcription polymerase chain reaction was performed to evaluate changes in the expression of inflammatory cytokines (interleukin-1 beta, interleukin-6, TNF-alpha, IFN-gamma) and regulatory genes (MMP-2, STAT 4, RAN, and RCC1). Results Combined treatment with gefitinib (1 µM) and mebendazole (0.5 µM) elicited a pronounced synergistic cytotoxic response, reducing cell viability to ~8–10% in A549 and ~15% in MDA-MB-231 cells—representing an additional >50% and ~30–40% decrease, respectively, compared to the most effective single-agent treatment- gefitinib 1 µM. Gene expression analysis revealed significant downregulation of pro-inflammatory cytokines and alterations in genes involved in mitotic regulation and nuclear transport. These changes suggest impaired intracellular signaling and reduced tumor-supportive microenvironmental interactions. The dual approach disrupted both cytoskeletal architecture and receptor-mediated signal transduction, pointing to a multifaceted mechanism of action. Conclusions The combination of mebendazole and gefitinib effectively suppresses tumor cell viability and modulates key pathways involved in cancer progression. By targeting cytoskeletal integrity and EGFR signaling, it may disrupt cytokine and tumor–microenvironment interactions, supporting further exploration as a strategy to overcome resistance in lung and breast cancers.