Search Results (7,196)

The ataxia–telangiectasia-mutated (ATM) protein plays a crucial role in the DNA damage response, particularly in the homologous recombination (HR) pathway. This study aimed to assess the impact of deleterious ATM variants on homologous recombination deficiency (HRD) and response to PARP inhibitors (PARPi) in melanoma patients, using a cell line established from melanoma tissue of a patient carrying the c.5979_5983del germline ATM variant. Despite proven loss of heterozygosity, lack of ATM activation, and HRD, our model did not show sensitivity to PARPi. We assessed the potential contribution of the Schlafen family member 11 (SLFN11) helicase, whose expression is inversely correlated with PARPi sensitivity in other cancers, to the observed resistance. The ATM mutant cell line lacked SLFN11 expression and featured hypermethylation-mediated silencing of the SLFN11 promoter. While sensitive to the ATR inhibitor (ATRi), the addition of ATRi to PARPi was unable to overcome the resistance. Our findings suggest that ATM mutational status and HRD alone do not adequately account for variations in sensitivity to PARPi in our model. A comprehensive approach is essential for optimizing the exploitation of DNA repair defects and ultimately improving clinical outcomes for melanoma patients. Full article

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. Full article

Background and Objective: Insulinoma-associated protein 1 (INSM1) is a novel immunohistochemical marker with potential utility in identifying neuroendocrine differentiation in lung cancer. Unlike conventional neuroendocrine (NE) markers, INSM1 can potentially serve as a standalone diagnostic biomarker. This study presents the first meta-analysis assessing the diagnostic accuracy of using INSM1 to distinguish LCNEC and SCLC from other lung cancer subtypes, addressing the variability across individual studies. Methods: A systematic review and meta-analysis were conducted to comprehensively evaluate the diagnostic performance of INSM1 in the pathological classification of lung cancer. The online databases PubMed, Web of Science, and Embase were systematically searched for data collection. Studies reporting the sensitivity and specificity of INSM1 in diagnosing LCNEC and SCLC were included. Pooled estimates were calculated using two models: the NSCLC model, which distinguishes LCNEC from other non-small cell lung cancers (NSCLCs), and the lung cancer model, which differentiates both LCNEC and SCLC from non-neuroendocrine (non-NE) lung cancer. Results: Fourteen studies comprising 3,218 specimens were included in this systematic review and meta-analysis. In the NSCLC model, INSM1 demonstrated a pooled sensitivity of 0.67 (95% CI: 0.61–0.73) and specificity of 0.97 (95% CI: 0.96–0.98), with an area under the curve (AUC) of 0.943. In the lung cancer model, the pooled sensitivity and specificity were 0.86 (95% CI: 0.84–0.88) and 0.97 (95% CI: 0.96–0.98), respectively, with an AUC of 0.974. Conclusions: INSM1 demonstrated excellent diagnostic accuracy and consistently high specificity for pulmonary neuroendocrine carcinomas, supporting its utility as a reliable standalone immunohistochemical marker with the potential to replace conventional NE markers in the pathological diagnosis of LCNEC and SCLC. Full article

Construction and demolition waste (C&D waste) contains various types of substances, which require different processing methods to maximize benefits and minimize harm to realize the goal of the circular economy. Therefore, it is urgent to promote the on-site sorting of C&D waste and explore the determinants of managers’ waste sorting intention. Based on a comprehensive literature review of C&D waste management, seven determinants are identified to explore how antecedent factors influence waste sorting intention by symmetric and asymmetric techniques. Firstly, the partial least squares structural equation modeling (PLS-SEM) was adopted to analyze the data collected from 489 managers to assess the net impact of each determinant on their intentions. Then, the fuzzy-set qualitative comparative analysis (fsQCA) provided another perspective by determining the configurations of the causal conditions that lead to higher or lower levels of intention. The PLS-SEM results reveal that all determinants show a significant positive relationship with the intention except for the perceived risks, which are negatively correlated with managers’ attitudes and intentions regarding C&D waste sorting. Moreover, top management support and subjective norms from other project participants and the public exhibit a huge impact, while the influence of perceived behavioral control (PBC) and policies is moderate. Meanwhile, fsQCA provides a complementary analysis of the complex causality that PLS-SEM fails to capture. That is, fsQCA identified six and five configurations resulting in high and low levels of intention to sort the C&D waste, respectively, and highlighted the crucial role of core conditions. The results provide theoretical and practical insights regarding proper C&D waste management and enhancing sustainable development. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Cloud computing offers a broad spectrum of services to users, particularly in multi-cloud environments where service-centric features are introduced to support users from multiple endpoints. To improve service availability and optimize the utilization of required services, cloud brokerage has been integrated into multi-cloud systems. The primary objective of a cloud broker is to ensure the quality and outcomes of services offered to customers. However, traditional cloud brokers face limitations in measuring service trust, ensuring validity, and anticipating future enhancements of services across different cloud platforms. To address these challenges, the proposed intelligent cloud broker integrates an intelligence mechanism that enhances decision-making within a multi-cloud environment. This broker performs a comprehensive validation and verification of service trustworthiness by analyzing various trust factors, including service response time, sustainability, suitability, accuracy, transparency, interoperability, availability, reliability, stability, cost, throughput, efficiency, and scalability. Customer feedback is also incorporated to assess these trust factors prior to service recommendation. The proposed model calculates service ranking (SR) values for available cloud services and dynamically includes newly introduced services during the validation process by mapping them with existing entries in the Service Collection Repository (SCR). Performance evaluation using the Google cluster-usage traces dataset demonstrates that the ICB outperforms existing approaches such as the Clustering-Based Trust Degree Computation (CBTDC) algorithm and the Service Context-Aware QoS Prediction and Recommendation (SCAQPR) model. Results confirm that the ICB significantly enhances the effectiveness and reliability of cloud service recommendations for users. Full article

Background: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) hold transformative potential for cardiovascular regenerative medicine, yet their clinical application is hindered by suboptimal mitochondrial maturation and metabolic inefficiencies. This systematic review evaluates targeted strategies for optimizing mitochondrial function, integrating metabolic preconditioning, substrate selection, and pathway modulation to enhance energy production and cellular resilience. Additionally, we examine the role of extracellular matrix stiffness and mechanical stimulation in mitochondrial adaptation, given their influence on metabolism and maturation. Methods: A comprehensive analysis of recent advancements in iPSC-CM maturation was conducted, focusing on metabolic interventions that enhance mitochondrial structure and function. Studies employing metabolic preconditioning, lipid and amino acid supplementation, and modulation of key signaling pathways, including PGC-1α, AMPK, and mTOR, were reviewed. Computational modeling approaches predicting optimal metabolic shifts were assessed, alongside insights into reactive oxygen species (ROS) signaling, calcium handling, and the impact of electrical pacing on energy metabolism. Results: Evidence indicates that metabolic preconditioning with fatty acids and oxidative phosphorylation enhancers improves mitochondrial architecture, cristae density, and ATP production. Substrate manipulation fosters a shift toward adult-like metabolism, while pathway modulation refines mitochondrial biogenesis. Computational models enhance precision, predicting interventions that best align iPSC-CM metabolism with native cardiomyocytes. The synergy between metabolic and biomechanical cues offers new avenues for accelerating maturation, bridging the gap between in vitro models and functional cardiac tissues. Conclusions: Strategic metabolic optimization is essential for overcoming mitochondrial immaturity in iPSC-CMs. By integrating biochemical engineering, predictive modeling, and biomechanical conditioning, a robust framework emerges for advancing iPSC-CM applications in regenerative therapy and disease modeling. These findings pave the way for more physiologically relevant cell models, addressing key translational challenges in cardiovascular medicine. Full article

This study presents a comprehensive evaluation of miscible gas injection (MGI) strategies for enhanced oil recovery (EOR) in high-salinity reservoirs, with a focus on the Raleigh Oil Field. Using a calibrated Equation of State (EOS) model in CMG WinProp™, eight gas injection scenarios were simulated to assess phase behavior, miscibility, and swelling factors. The results indicate that carbon dioxide (CO₂) and enriched separator gas offer the most technically and economically viable options, with CO₂ demonstrating superior swelling performance and lower miscibility pressure requirements. The findings underscore the potential of CO₂-EOR as a sustainable and effective recovery method in pressure-depleted, high-salinity environments. Full article

Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (initial support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

Evaluating risk in complex systems relies heavily on human auditors whose subjective assessments can be compromised by knowledge gaps and varying interpretations. This subjectivity often results in inconsistent risk evaluations, even among auditors examining identical systems, owing to differing pattern recognition processes. In this study, we propose a causality model that can improve the comprehension of risk levels by breaking down the risk factors and creating a layout of risk events and consequences in the system. To do so, the initial step is to define the risk event blocks, each comprising two distinct components: the agent and transfer mechanism. Next, we construct a causal map that outlines all risk event blocks and their logical connections, leading to the final consequential risk. Finally, we assess the overall risk based on the cause-and-effect structure. We conducted real-world illustrative examples comparing risk-level assessments with traditional experience-based auditor judgments to evaluate our proposed model. This new methodology offers several key benefits: it clarifies complex risk factors, reduces reliance on subjective judgment, and helps bridge the gap between subjective and objective uncertainty. The illustrative examples demonstrate the potential value of the model by revealing discrepancies in risk levels compared to traditional assessments. Full article

Background/Objectives: Legislative changes in educational systems have influenced how student learning is understood and promoted. In physical education (PE), there has been a shift from behaviorist models to more holistic approaches. In this context, physical literacy (PL) is presented as an emerging pedagogical model in school PE, aimed at fostering students’ motor competence in a safe, efficient, and meaningful way. The aim of this study is to analyze the origins, foundations, methodological elements, and educational value of PL, highlighting its potential to promote holistic and inclusive learning as the basis for an emerging PL model. Methods: A narrative review was conducted through a literature search in the Web of Science, PubMed, Scopus, and SportDiscus databases up to June 2025, focusing on scientific literature related to PL and PE. The analysis included its historical background, philosophical and theoretical foundations, and the key methodological elements and interventions that support its use as a pedagogical model. Results/Discussion: The findings indicate that the PL model can be grounded in key principles, such as student autonomy, teacher training, connection with the environment, inclusion, and collaboration. Additionally, motivation, enjoyment, creativity, and continuous assessment are identified as essential components for effective implementation. Moreover, this model not only guides and supports teachers in the field of PL but also promotes comprehensive benefits for students at the physical, cognitive, affective, and social levels, while encouraging increased levels of physical activity (PA). Conclusions: PL is understood as a dynamic and lifelong process that should be cultivated from early childhood to encourage sustained and active participation in PA. As a pedagogical model, PL represents an effective tool to enhance student learning and well-being in PE classes. Full article

The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article