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Mitochondrial oxidative phosphorylation inhibition by 9α,11α-dihydroxy-kaurenoic acid promotes ROS-associated apoptosis and suppresses cancer stemness in non-small cell lung cancer

Prevalence and risk factors of overactive bladder syndrome among Egyptian medical students, and its impact on health-related quality of life, cross-sectional study

Abstract Overactive bladder (OAB) is a common chronic condition that can have a significant impact on quality of life (HRQL). We aimed to assess the prevalence of OAB bother and its related quality of life among medical students in Egypt. A cross-sectional study among medical students in Egypt. An online questionnaire was shared via online platforms to collect the intended participants. We used the Overactive Bladder Symptoms and Health-Related Quality of Life Short Form (OAB-q SF). We assessed the difference between participants in their baseline characteristics using the Mann–Whitney U and Kruskal–Wallis Tests. We performed a multiple linear regression to assess the potential risk factors. 1003 participants completed the questionnaire. The median transformed OAB bother score was 10[3.33, 23.33], while the median transformed score of HRQL was 93.8[80, 98.46]. The prevalence was about 15%, while about 51.65% reported a decline in HRQL. A statistically significant association between OAB bother and academic phase, energy drink, or satisfaction with social life was reported. We found no association between OAB bother and caffeinated drinks or smoking. Finally, neither age, gender, nor BMI had an association with OAB bother. A low prevalence of OAB among medical students was found. Consumption of Energy drinks was found to be a risk factor, while caffeinated drinks had no effect. OAB was found to be higher in the academic phase than in the clinical one. Several studies should be conducted to further assess the risk factors of overactive bladder. Clinicaltrials.gov registration: NCT07044778, June 2025.

Automated RECIST tumor response classification through prompt-guided large language models

Abstract This study investigates whether an entirely offline, general-purpose large language model (LLM) can reliably automate the classification of routine radiology reports according to RECIST (Response Evaluation Criteria in Solid Tumors) guidelines, focusing on how different prompting strategies support accurate, privacy-preserving tumor response assessment without additional model fine-tuning. An offline, in-house implementation of LLaMA-3.3 (70B) was used to classify real-world CT imaging reports from oncology patients. Reports were authored following RECIST-structured reporting but had outcome labels programmatically withheld prior to processing. Three prompting strategies—zero-shot, few-shot, and chain-of-thought prompting—were tested to guide the model in assigning RECIST categories: Baseline (BL), Complete Response (CR), Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD). Model outputs were benchmarked against original expert labels using accuracy, precision, recall, and F1 scores. Across all tested prompting strategies, the LLaMA-3.3 model achieved strong classification performance. The best results were obtained with chain-of-thought prompting, reaching micro F1 scores of 0.81 across all RECIST categories. Overall model predictions aligned well with human expert assessments. Operating entirely offline within hospital infrastructure, the system preserved full compliance with stringent data privacy requirements. Prompt-driven large language models can accurately classify tumor response categories from real-world radiology reports in a scalable, reproducible, and privacy-preserving manner. Offline LLM deployment, combined with optimized prompting strategies, offers a promising approach for automating structured oncology report interpretation, potentially enhancing consistency and efficiency in clinical decision support workflows.

Transforming preservice teachers’ emotions and appraisals on outdoor learning in early childhood education

Distinct epigenetic alterations and accelerated mitotic aging define second primary breast cancers

Biobank analysis reveals more than 88,000 genetic associations with metabolic traits

Effects of septic shock vasopressors on the fitness of Escherichia coli

Synthesis of di- and tri-cellulose acetate from rice husk cellulose and commercial microcrystalline by copper perchlorate catalyst

Abstract This work describes the synthesis of various di- and tri-cellulose acetates from rice husk cellulose (RHC) and commercial microcrystalline cellulose (CMCC) using copper(II) perchlorate hexahydrate (Cu(ClO 4 ) 2 ·6H 2 O) as an effective catalyst at room temperature and 50 °C. This investigation was conducted using different amounts of Cu(ClO 4 ) 2 ·6H 2 O (100, 200, 300 mg) and 2 g of cellulose for various times (0.5–6 h) in the presence of a constant volume of acetic anhydride (15 mL). Multiple reactions have led to the formation of di- and tri-cellulose acetates. CMCC was remarkably converted into cellulose acetate at room temperature and 50 °C, with yields of 98.10% and 96.10%, respectively. The extracted cellulose from rice husk produced di- and tri-cellulose acetate at room temperature and 50 °C, yielding 86.93% and 92.85%, respectively. Critical expected results were obtained in this work: a strong relationship was found between the degree of substitution (DS) and acetyl percentage (AP%) of the products, the catalyst level in the reaction mixture, the presence or absence of temperature, and the reaction time. The DS and AP% were characterized using FTIR, 1 H-NMR, XRD, and the thermal stability was evaluated by TGA and DTA. This work presents a new catalyst that can produce varying degrees of cellulose acetylation by adjusting reaction temperature, catalyst amount, and reaction duration.

Sarcopenic obesity and body composition phenotypes in older adults with chronic kidney disease: associations with estimated mortality risk

Identification of ice loads on ship structure using a hybrid regularization strategy

Reliability and readability of AI platforms for pediatric health advice: a comparative analysis

Parameter calibration of finite element model of reinforced concrete arch bridge based on ISOA-RBF neural network

Structural analysis and optimization of an autonomous robot designed for greenhouse roof cleaning

Abstract Traditional greenhouse cleaning methods are labor-intensive, prone to human error, and inefficient, often compromising light transmittance and productivity. To address these challenges, this study proposes an autonomous robot designed to clean greenhouse roofs efficiently and reliably. The robot features an integrated cleaning system with adjustable brushes, wipers, and water sprinklers, ensuring optimal performance and significantly improving light transmittance. Powered by a 500 W PV system, it utilizes electric wheels for smooth, stable movement and incorporates a replaceable brush-wiper mechanism for enhancing durability and maintenance efficiency. The design process involved SolidWorks modeling for mass properties, CFD simulations with the k-ε turbulence model to evaluate wind load conditions, and ANSYS structural analysis to confirm durability under extreme wind speeds of up to 126 km/h (ten times greater than normal conditions). Structural tested at different robot’s rotational speeds 25 rpm and 50 rpm confirmed optimal performance at 25 rpm, balancing cleaning efficiency and long-term durability. Additionally, the robot incorporates advanced control unit with sensors for autonomous operation, real-time light transmission monitoring, and navigation capabilities, distinguishing it from traditional manual or semi-automated methods. The results demonstrated robust performance in extreme conditions, surpassing existing systems limited to standard weather. The robot’s performance is limited by speed (0.35 m/s), battery life, roof complexity, maintenance, adaptability, and cost, indicating areas for improvement. Future developments will integrate AI for autonomous decision-making, GPS for precise navigation, and a smart cleaning system to optimize performance based on real-time data, further reducing maintenance costs and ensuring optimal greenhouse lighting.

Use of public health benefits to design air pollution emission abatement strategies

Caregivers’ knowledge, attitudes, and practices regarding postpartum depression: a cross-sectional study

Daily briefing: The known protein universe just got a lot bigger

Diagnosing morphology-vitality relationships through multifractal urban form metrics and explainable machine learning

Barriers and facilitators to implementing childcare in long-term care homes: A scoping review protocol and consultative exercise

Background Long-term care (LTC) homes face persistent workforce recruitment and retention challenges, particularly among staff balancing professional responsibilities with childcare needs. Integrating childcare services within LTC homes has the potential to improve staff well-being, workforce stability, and resident experiences through intergenerational engagement. Despite this potential, the implementation literature remains fragmented, and no systematic synthesis of barriers, facilitators, or contextual determinants exists. Objective To systematically map the literature on barriers and facilitators to implementing childcare services within LTC homes and identify gaps to inform research, policy, and practice. Methods We will conduct a scoping review following the Arksey and O’Malley framework, enhanced by Levac et al., and report findings according to PRISMA-P and PRISMA-ScR guidelines. Literature searches will be conducted in MEDLINE, CINAHL, Embase, Scopus, and PsycINFO, supplemented by grey literature searches. Eligible studies include qualitative, quantitative, or mixed-methods research, program evaluations, and policy reports examining implementation of childcare services within or linked to LTC homes. Two reviewers will independently screen studies, extract data using a standardized form, and resolve discrepancies through discussion or a third reviewer. Extraction will capture study characteristics, childcare model details, reported barriers and facilitators, and outcomes. Findings will be synthesized narratively and organized thematically using the Consolidated Framework for Implementation Research (CFIR 2.0). Stakeholder engagement with LTC and early childhood centre staff will guide interpretation and knowledge translation. Expected outcomes The review will identify key determinants of successful implementation, highlight gaps in the evidence, and provide actionable insights for LTC administrators, early childhood partners, and policymakers seeking to develop sustainable, equitable co-located childcare programs that benefit residents, children, and staff.

A case-aware feature modulation framework for defect classification in power lines

Abstract Conductors and grounded transmission towers are separated by non-conductive overhead transmission line insulators are known as materials. They frequently meet with problems once they are put into use mechanical or electrical pressure and environmental pollution. It is important to carry out regular inspections to avoid power failures because adverse working conditions may lead to insulation breakdown. To do this, this study proposes a new method of classifying high-voltage surface conditions of insulators are given depending on the picture, which is founded on deep convolutional neural networks (CNNs). We suggest MS-CADFM-SSL, a new multi-task model of defect classification of various components of power lines. The strategy incorporates a communal EfficientNet Multi-scale case-dependent dynamic feature modulation, orthogonality regularization, backbone with multi-scale case-dependent dynamic feature modulation and self-supervised pretraining to learn jointly generalized representations and keep task specific discriminative features. The framework was tested on five nonhomogeneous defect cases has strong performance with regard to precision, recall, F1-score, and accuracy. Industrially, it guarantees dependable identification of severe errors, decreases computing expenses, and real time checking of the transmission lines. In comparison to traditional single-task or naive multitask.MS-CADFM-SSL has better adaptability to visually diverse and imbalanced datasets, where focus areas are physically meaningful due to Grad-CAM visualizations. Despite difficulties using infrequent or delicate anomalies and depending on fixed images, the structure offers a scalable basis of automated defect test. Future extensions consist of multimodal and temporal integration of data, semi-supervised learning and predictive maintenance prioritization to improve dependability and workability.

Transformation of artistic style and innovative design of oriental folk patterns based on AIGC Technology—A case study of Zhuxian town new year paintings from China

In response to the limited cross-domain innovation in the digitalization of traditional oriental folk art, this study takes the New Year pictures of China’s Zhuxian Town as a case. It develops a collaborative technical framework of combining the Liblib platform and a LoRA model to explore AI-generated digital re-creation of traditional art. A high-quality dataset was built through a three-stage process of image acquisition, multidimensional screening, and expert review. A three-layer keywords thesaurus was constructed through literature analysis, questionnaire surveys, and semantic clustering. Using a two-stage training strategy combining pre-training and fine-tuning, along with dynamic optimization, the model accurately captures the stylistic features of Zhuxian New Year pictures. The generated outputs integrate traditional aesthetics with modern design and are applied to cultural and graphic creative products. The results demonstrate the effectiveness of the proposed framework for preserving artistic style while enabling cross-domain innovation and offer a practical technical reference for the digital inheritance and modernization of related folk arts.