Search Results (907)

Unlike skeletal Class I and II patients, the application of maxillomandibular advancement (MMA) in skeletal Class III patients with obstructive sleep apnea (OSA) is not well documented. The aim of this scoping review was to explore the variations and outcomes of MMA techniques in this unique subgroup. A comprehensive search of PubMed, Embase, Cochrane and LILACS databases were conducted for articles published up to May 2025. Nine studies met the inclusion criteria. Three main variations of MMA were identified: (1) Bimaxillary advancement, which provides the most significant airway enlargement across all pharyngeal regions but carries the highest risk of facial aesthetic distortion; (2) Maxillary advancement with mandibular auto-rotation, a less invasive option suited for patients with isolated maxillary retrusion and symmetrical mandibular anatomy; (3) Maxillary advancement with mandibular setback, which addresses aesthetic concerns in patients with mandibular excess but may compromise oropharyngeal airway space. All variations were reported to be effective in treating OSA (Reduction of AHI by at least 50%) but with different considerations. Surgical planning for skeletal Class III patients with OSA should be individualized based on craniofacial morphology, anatomical site of airway obstruction, and aesthetic considerations. A decision flowchart was shared in this study. Full article

The application of Generative Artificial Intelligence (GenAI)—defined as a class of AI systems capable of autonomously generating new content such as images, texts, and design solutions based on learned data patterns—has become increasingly widespread in creative design. By supporting ideation, rapid trial-and-error, and data-driven decision-making, GenAI enables designers to explore design alternatives more efficiently and enhances human–computer interaction experiences. In design practice, GenAI functions not only as a productivity-enhancing tool but also as a collaborative partner that assists users in visual exploration, concept refinement, and iterative development. However, users still face a certain learning curve before effectively adopting these technologies. Within the framework of human-centered artificial intelligence, contemporary design practices place greater emphasis on inclusivity across diverse user groups and on enabling intuitive “what-you-think-is-what-you-get” interaction experiences. From a sustainable design perspective, GenAI’s capabilities in digital simulation, rapid iteration, and automated feedback contribute to more efficient design workflows, reduced collaboration costs, and broader access to creative participation for users with varying levels of expertise. These characteristics play a crucial role in enhancing the accessibility of design resources and supporting the long-term sustainability of creative processes. Focusing on the context of China’s digital design industry, this study investigates the application of GenAI in design workflows through an empirical case study of Zhitu AI, a generative design tool developed by Beijing Didi Infinity Technology Development Co., Ltd. The study conducts a literature review to outline the role of GenAI in visual design processes and employs observation-based experiments and semi-structured interviews with users of varying levels of design expertise. The findings reveal key pain points across stages such as prompt formulation, secondary editing, and asset generation. Drawing on the Kano model, the study further identifies potential design opportunities and discusses their value in improving efficiency, supporting non-expert users, and promoting more sustainable and inclusive design practices. Full article

In the aftermath of the COVID-19 pandemic, instructors in higher education have reported a decline in foundational reading habits, particularly in STEM courses where dense, technical texts are common. This study examines a low-barrier instructional intervention that used generative AI (GenAI) to support pre-class preparation in two upper-division biology courses. Weekly AI-generated audio overviews—“podcasts”—were paired with timed, textbook-based online quizzes. These tools were not intended to replace reading, but to scaffold engagement, reduce preparation anxiety, and promote early familiarity with course content. We analyzed student engagement, perceptions, and performance using pre/post surveys, quiz scores, and exam outcomes. Students reported that the podcasts helped manage time constraints, improved their readiness for lecture, and increased their motivation to read. Those who consistently completed the quizzes performed significantly better on closed-book, in-class exams and earned higher final course grades. Our findings suggest that GenAI tools, when integrated intentionally, can reintroduce structured learning behaviors in post-pandemic classrooms. By meeting students where they are—without compromising cognitive rigor—audio-based scaffolds may offer inclusive, scalable strategies for improving academic performance and reengaging students with scientific content in an increasingly attention-fragmented educational landscape. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype, with limited diagnostic options and no targeted early detection tools. Liquid biopsy represents a minimally invasive approach for detecting tumor-derived molecular alterations in body fluids. This scoping review aimed to comprehensively synthesize all liquid biopsy-derived molecular biomarkers evaluated for the diagnosis of TNBC in adults. Methods: This review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Systematic searches of PubMed, Scopus, Embase, and Web of Science identified primary human studies evaluating circulating molecular biomarkers for TNBC diagnosis. Non-TNBC, non-human, hereditary, treatment-response, and nonmolecular studies were excluded. Data on study design, patient characteristics, biospecimen type, analytical platforms, biomarker class, and diagnostic performance were extracted and synthesized descriptively by biomolecule class. Results: Thirty-two studies met the inclusion criteria, comprising 15 protein-based, 12 RNA-based, and 6 DNA-based studies (one reporting both protein and RNA). In total, 1532 TNBC cases and 3137 participants in the comparator group were analyzed. Protein biomarkers were the most frequently studied, although only APOA4 appeared in more than one study, with conflicting results. RNA-based biomarkers identified promising candidates, particularly miR-21, but validation cohorts were scarce. DNA methylation markers showed promising diagnostic accuracy yet lacked replication. Most studies were small retrospective case–control designs with heterogeneous comparators and inconsistent diagnostic reporting. Conclusions: Evidence for liquid biopsy-derived biomarkers in TNBC remains limited, heterogeneous, and insufficiently validated. No biomarker currently shows reproducibility suitable for clinical implementation. Robust, prospective, and standardized studies are needed to advance liquid biopsy-based diagnostics in TNBC. Full article

Background: Gold (Au) complexes have emerged as promising anticancer candidates due to their distinct coordination chemistry and ability to modulate thiol-dependent and redox-regulated cellular pathways, particularly thioredoxin reductase (TrxR). In recent years, structurally diverse Au(I) and Au(III) complexes have been reported with potent in vitro anticancer activity; however, cross-study comparability and design principles remain unclear. Aim: This systematic review critically evaluates anticancer Au(I/III) complexes reported since 2016, with the specific aim of identifying how oxidation state, coordination geometry, and ligand class influence in vitro potency, selectivity, and translational potential. Methods: A PRISMA-guided literature search was performed in Scopus, Web of Science, PubMed, and ScienceDirect for studies published between January 2016 and March 2025. Two independent reviewers screened titles/abstracts and full texts according to predefined inclusion criteria. Only original studies reporting anticancer activity of structurally characterized Au(I/III) complexes in human cancer models were included. After the removal of duplicates, 1100 records were screened at the title and abstract level. Of these, 240 articles were assessed in full text for eligibility. Ultimately, 128 studies reporting anticancer activity of structurally characterized Au(I/III) complexes met the inclusion criteria and were included in the qualitative synthesis. Biological potency data were harmonized to μM units where applicable, and results were synthesized qualitatively due to heterogeneity in experimental design. Results: A total of 128 studies met the inclusion criteria. Au(I) complexes—particularly phosphine- and N-heterocyclic carbene (NHC)-based compounds—consistently showed sub-micromolar cytotoxicity in TrxR-dependent cancer cell lines, whereas Au(III) complexes displayed greater structural diversity but variable stability and redox behavior. In vivo efficacy was reported for a limited subset of compounds and was frequently constrained by solubility, systemic toxicity, or metabolic instability. Conclusions: The available evidence indicates that anticancer activity of gold complexes is strongly dependent on oxidation state, ligand environment, and redox stability. While Au(I) scaffolds show more reproducible in vitro potency, successful translation to in vivo models remains limited. This review defines structure–activity and structure–liability relationships that can guide the rational design of next-generation gold-based anticancer agents. Full article

In this paper, we investigate new geometric properties of normalized analytic functions associated with the generalized Marcum Q-function. In particular, we focus on two analytic forms derived from a normalized derivative of a representation involving the Marcum Q-function, and its Alexander transform. For these functions, we establish sufficient conditions ensuring membership in the classes of lemniscate starlike and lemniscate convex functions. Special attention is given to the case $\nu =1$, where explicit admissible parameter ranges for b are derived. We further examine inclusion relations between these normalized analytic forms and lemniscate subclasses, complemented by several corollaries, illustrative examples, and graphical visualizations. These results extend and enrich the geometric function theory of special functions related to the generalized Marcum Q-function. Full article

Carotid atherosclerosis remains one of the primary etiological factors underlying ischemic stroke, contributing to adult neurological disability and mortality. In recent years, non-coding RNAs (ncRNAs) have emerged as key regulators of gene expression, actively modulating molecular pathways involved in atherogenesis. This systematic review, the first to be exclusively focused on carotid atherosclerosis, aimed at synthesizing current findings on the differential expression of ncRNAs throughout the natural history of the disease, thus providing the first comprehensive attempt to delineate a stage-specific ncRNA expression profile in carotid disease. A comprehensive literature search was conducted in PubMed and Scopus databases in January 2025, following PRISMA guidelines. Original studies involving human subjects with carotid atherosclerosis, evaluating the expression of intracellular or circulating ncRNAs, were included and then categorized according to their association with cardiovascular risk factors, carotid intima-media thickness (cIMT), presence of atherosclerotic plaques, plaque vulnerability, clinical symptoms, and ischemic stroke. Out of 148 articles initially identified, 49 met the inclusion criteria and were analyzed in depth. Among the different classes of ncRNAs, microRNAs (miRNAs) were the most frequently reported as dysregulated, followed by circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs). Notably, the majority of identified ncRNAs were implicated in key pathogenic mechanisms such as inflammatory signaling, vascular smooth muscle cell (VSMC) phenotypic modulation, and ABCA1-mediated cholesterol efflux. Collectively, the evidence underscores the association and possible involvement of ncRNAs in the initiation and progression of carotid atherosclerosis and its cerebrovascular complications. Their relative stability in biological fluids and cell-specific expression profiles highlight their strong potential as minimally invasive biomarkers and—possibly—novel therapeutic targets. Full article

Sugarcane diseases cause estimated global annual losses of over $5 billion. While deep learning shows promise for disease detection, current approaches lack transparency and confidence estimates, limiting their adoption by agricultural stakeholders. We developed an uncertainty-aware detection system integrating Monte Carlo (MC) dropout with MobileNetV3, trained on 2521 images across five categories: Healthy, Mosaic, Red Rot, Rust, and Yellow. The proposed framework achieved 97.23% accuracy with a lightweight architecture comprising 5.4 M parameters. It enabled a 2.3 s inference while generating well-calibrated uncertainty estimates that were 4.0 times higher for misclassifications. High-confidence predictions (>70%) achieved 98.2% accuracy. Gradient-weighted Class Activation Mapping provided interpretable disease localization, and the system was deployed on Hugging Face Spaces for global accessibility. The model demonstrated high recall for the Healthy and Red Rot classes. The model achieved comparatively higher recall for the Healthy and Red Rot classes. The inclusion of uncertainty quantification provides additional information that may support more informed decision-making in precision agriculture applications involving farmers and agronomists. Full article

Background/Objective: Systematic reviews (SRs) with network meta-analysis (NMA) support evidence-based decision-making by enabling both direct and indirect comparisons across multiple interventions. Given the expanding use of Janus kinase (JAK) inhibitors in rheumatoid arthritis (RA), the methodological rigor of SRs with NMA is essential for trustworthy conclusions. This study is aimed at evaluating the methodological quality of SRs with NMA assessing the efficacy and/or safety of JAK inhibitors in RA. Methods: PubMed and Embase were searched for full-text SRs with NMAs evaluating JAK inhibitors as a therapeutic class in RA. Eligible publications were English-language articles reporting efficacy and/or safety outcomes. Narrative reviews, letters, duplicates, reviews focused on a single JAK inhibitor, and reviews without quantitative synthesis were excluded. Three independent reviewers assessed methodological quality using AMSTAR 2. Descriptive statistics were used to summarize findings. Results: Of the 222 records identified, 18 SRs with NMA met the inclusion criteria: 5 focused on efficacy, 5 on safety, and 8 assessed both. The most consistently fulfilled AMSTAR 2 items were a clearly defined PICO question (100%), duplicate study selection (100%), and reporting of conflicts of interest (100%). Common shortcomings included lack of protocol registration (44%), incomplete reporting of the search strategy (39%), and absence of publication bias assessment (50%). Risk-of-bias assessment varied by review focus: all safety reviews complied (100%), compared with 20% of efficacy reviews and 37% of mixed reviews. Conclusions: Most SRs with NMA of JAK inhibitors in RA present relevant methodological limitations, particularly in protocol registration, search reporting, and risk-of-bias assessment. Methodological standards were generally higher in safety-focused reviews, underscoring the need for more consistent and rigorous conduct and reporting, especially in efficacy and mixed reviews, to strengthen confidence in NMA-derived conclusions. Full article

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

Virtual Field Trips (VFTs) have emerged as an alternative to Traditional Field Trips (TFTs), addressing logistical, financial, and accessibility constraints in geoscience education. This study presents a comparative analysis of the educational impact of a VFT and a TFT implemented with the same 10th-grade class in a Portuguese secondary school. The VFT, focused on volcanism and its socioeconomic impacts, used Google Earth to explore the island of São Miguel in the Azores. The TFT, centred on the rock cycle, was conducted at the Lavadores Beach geological site. Both interventions followed the field-based learning model by Orion and were structured around three phases: preparation, field trip (virtual or traditional), and post-activity synthesis. Data was collected through diagnostic tests, schematization, observation grids, student reports (snapshot), group projects, and written responses to a fieldwork guide recorded on Padlet during the VFT and TFT. The results showed that both VFTs and TFTs enhance conceptual understanding and student engagement, though they foster different skills: VFTs strengthen digital literacy, improve accessibility and inclusion for students with mobility or geographic constraints, allow for content revisitation, foster collaboration among students, integrate multimedia resources, and enable virtual exploration of remote locations that would otherwise be inaccessible. They also offer reduced costs, greater scheduling flexibility, and allow for individualised pacing of student learning. In contrast, TFTs provide richer sensory and practical experiences that are essential for hands-on scientific inquiry and foster stronger connections with the natural environment. The study concludes that a complementary use of both strategies offers the most inclusive and effective approach to teaching geosciences. Full article

Accurate credit risk assessment for small firms and farmers is crucial for financial stability and inclusion; however, many models still rely on binary default labels, overlooking the continuum of borrower vulnerability. To address this, we propose Transformer–LightGBM–Stacked Logistic Regression (TL-StackLR), a hybrid stacking framework for multi-class loan default prediction. The framework combines three learners: a Feature Tokenizer Transformer (FT-Transformer) for feature interactions, LightGBM for non-linear pattern recognition, and a stacked LR meta-learner for calibrated probability fusion. We transform binary labels into three risk tiers, Low, Medium, and High, based on quantile-based stratification of default probabilities, aligning the model with real-world risk management. Evaluated on datasets from 3045 firms and 2044 farmers in China, TL-StackLR achieves state-of-the-art ROC-AUC scores of 0.986 (firms) and 0.972 (farmers), with superior calibration and discrimination across all risk classes, outperforming all standalone and partial-hybrid benchmarks. The framework provides SHapley Additive exPlanations (SHAP) interpretability, showing how key risk drivers, such as income, industry experience, and mortgage score for firms and loan purpose, Engel coefficient, and income for farmers, influence risk tiers. This transparency transforms TL-StackLR into a decision-support tool, enabling targeted interventions for inclusive lending, thus offering a practical foundation for equitable credit risk management. Full article

Background: Alternative pre-mRNA splicing is a combinatorial process involving serine/arginine-rich (SR) and heterogeneous nuclear ribonucleoprotein (hnRNP) splicing factors. These proteins can silence or enhance splicing based on their expression levels and binding positions. Objectives: To better understand the combinatorial and interdependent regulation between SR and hnRNP splicing factors during alternative splicing. Methods: Computational analyses were performed using cell knockdown and binding datasets from available databases. Results: Analyses of differential splicing data for 9 SR proteins and 21 hnRNP knockdowns revealed statistically significant interdependent regulation among several RNA-binding protein (RBP) combinations, albeit at different levels. Neither SR proteins nor hnRNPs showed strong preferences for collaborating with specific RBP classes in mediating exon inclusion. While SRSF3, hnRNPK, hnRNPC, and hnRNPL stand out as major influencers of alternative splicing, they do so predominantly independent of other RBPs. Minor influencers of alternative splicing, such as hnRNPDL and hnRNPR, predominantly regulate exon inclusion in concert with other RBPs, indicating that exon inclusion can be mediated by both single and multiple RBPs. Interestingly, the higher the number of RBPs that regulate the inclusion of an exon, the more variable exon inclusion preferences become. Interdependently regulated exons are more modular and can be characterized by weaker splice sites compared to their independently regulated counterparts. A comparison of RBP interdependence between HeLa and other cell lines provides a framework that explains cell-type-specific alternative splicing. Conclusions: Our study highlights the importance of the interdependent regulation of alternative exons and identifies characteristics of interdependently regulated exons that differ from independently regulated exons. Full article

This study examines the profiles of secondary special education teachers regarding their readiness for inclusive teaching, with technology-enhanced practices operationalized through participation in STEAM-related professional development. A total of 323 teachers from vocational high schools and integration classes participated. Four indicators of professional preparation were assessed: years of teaching experience, formal STEAM training, exposure to students with special educational needs (SEN), and perceived success in inclusive teaching, operationalized as self-reported competence in adaptive instruction, classroom management, positive attitudes toward inclusion, and collaborative engagement. Cluster analysis revealed three distinct teacher profiles: less experienced teachers with moderate perceived success and limited exposure to students with SEN; well-prepared teachers with high levels across all indicators; and highly experienced teachers with lower STEAM training and perceived success. These findings underscore the need for targeted professional development that integrates inclusive and technology-enhanced pedagogy through STEAM and is tailored to teachers’ experience levels. By integrating inclusive readiness, STEAM-related preparation, and technology-enhanced pedagogy within a person-centered profiling approach, this study offers actionable teacher profiles to inform differentiated professional development in secondary special education. Full article

Background and Objectives: The integrated stress response (ISR) is a convergent node in neurodegeneration. We systematically mapped open-access mammalian in vivo evidence for synthetic ISR modulators, comparing efficacy signals, biomarker engagement, and safety across mechanisms and disease classes. Methods: Following PRISMA 2020, we searched PubMed (MEDLINE), Embase, and Scopus from inception to 22 September 2025. Inclusion required mammalian neurodegeneration models; synthetic ISR modulators (eIF2B activators, PERK inhibitors or activators, GADD34–PP1 ISR prolongers); prespecified outcomes; and full open access. Extracted data included model, dose and route, outcomes, translational biomarkers (ATF4, phosphorylated eIF2α), and safety. Results: Twelve studies met the criteria across tauopathies and Alzheimer’s disease (n = 5), prion disease (n = 1), amyotrophic lateral sclerosis and Huntington’s disease (n = 3), hereditary neuropathies (n = 2), demyelination (n = 1), and aging (n = 1). Among interpretable in vivo entries, 10 of 11 reported benefit in at least one domain. By class, eIF2B activation with ISRIB was positive in three of four studies, with one null Alzheimer’s hAPP-J20 study; PERK inhibition was positive in all three studies; ISR prolongation with Sephin1 or IFB-088 was positive in both studies; and PERK activation was positive in both studies. Typical regimens included ISRIB 0.1–2.5 mg per kg given intraperitoneally (often two to three doses) with reduced ATF4 and phosphorylated eIF2α; oral GSK2606414 50 mg per kg twice daily for six to seven weeks, achieving brain-level exposures; continuous MK-28 delivery at approximately 1 mg per kg; and oral IFB-088 or Sephin1 given over several weeks. Safety was mechanism-linked: systemic PERK inhibition produced pancreatic and other exocrine toxicities at higher exposures, whereas ISRIB and ISR-prolonging agents were generally well-tolerated in the included reports. Conclusions: Directional ISR control yields consistent, context-dependent improvements in behavior, structure, or survival, with biomarker evidence of target engagement. Mechanism matching (down-tuning versus prolonging the ISR) and exposure-driven safety management are central for translation. Full article