Search Results (17,785)

Hybrid role-playing games are increasingly used to support democratic learning, yet there is limited empirical evidence on how such hybrid designs function across contexts. This study analyses the pedagogical and deliberative effects of Empaville, a hybrid role-playing game designed to simulate a green participatory budgeting process by embedding deliberation, competition, and voting within a fictional urban setting. We analyse six implementations conducted between 2023 and 2025 in the United Kingdom and Morocco (N = 118), combining participant observation with post-game survey data. The analysis examines role activation, phase-level enjoyment, and participants’ reported learning and deliberative experiences, using descriptive statistics, non-parametric tests, effect size measures, and qualitative thematic analysis. Across contexts, participants report that the game supports perspective-taking, intellectual humility, and constructive engagement with disagreement, while perceived learning and participation intensity vary more substantially across individuals and sessions. Cross-national comparisons reveal some statistically detectable differences in how specific phases are experienced, particularly voting, but effect sizes are generally small or trivial, indicating limited substantive divergence overall. These findings suggest that hybrid role-playing games can foster deliberative learning outcomes in short educational interventions, while highlighting the importance of distinguishing between enjoyment, engagement, and perceived pedagogical value. The study contributes an exploratory but systematic mixed-methods evaluation suitable for small-N pedagogical interventions without causal claims. Full article

Background/Objectives: This study aimed to describe cumulative reproductive outcomes across three embryo transfer (ET) cycles after hysteroscopic endometrial polypectomy using a hysteroscopic tissue removal system (HTRS) and to identify determinants of cumulative and per-cycle pregnancy. Methods: In this single-center retrospective cohort study, we included infertile women who underwent HTRS-based endometrial polypectomy between January 2023 and December 2024 and subsequently initiated at least one ET cycle. Patients were followed from ET1 through ET3. The primary endpoint was the cumulative clinical pregnancy rate (CCPR) within three ET cycles. In the observed cumulative analysis, treatment discontinuation was considered as non-pregnancy. Kaplan–Meier (KM) analysis was used to estimate the cumulative pregnancy probability, with treatment discontinuation considered as censoring. Multivariate logistic regression and generalized estimating equations were used to identify patient-level and cycle-level predictors. Results: Among 100 patients, 79 achieved clinical pregnancy within three ET cycles (CCPR 79.0%). The KM estimate at ET3 was 87.4%, and the cumulative live birth rate was 65.0%. Pregnancy rates declined with advancing maternal age (≤34 years, 91.9%; 35–39 years, 78.3%; ≥40 years, 52.9%). Maternal age independently predicted lower cumulative pregnancy and lower per-cycle pregnancy probability, whereas blastocyst transfer was associated with a higher probability of pregnancy per cycle. Conclusions: In women who underwent ET after HTRS polypectomy, cumulative pregnancy across three ET cycles was relatively high; however progression to live birth declined with advancing maternal age. As no non-surgical comparison group was included, these findings should be interpreted as descriptive rather than causal. Full article

Metabolic heterogeneity within the cell microenvironment is a key driver of cancer progression and resistance to therapy. However, current approaches lack the spatial and temporal resolution required to capture its dynamics in living systems. While recent advances in 3D cell culture models and metabolomic profiling have improved our understanding of the tumor niche, their integration with real-time optical sensing remains underdeveloped. Here, we present an integrated platform combining a 3D-printed microreactor culture chamber with Raman spectroscopy to enable non-invasive, spatially resolved metabolic monitoring of living cell cultures. Our microreactor platform generates controlled oxygen and nutrient cues while simultaneously acquiring label-free Raman spectra, revealing extracellular metabolic fingerprints linked to cell catabolism (e.g., glucose and lactate shifts) and acidification. Analysis across four cell lines uncovered temporal evolution as the dominant source of metabolic variance, while spatial heterogeneity along oxygen gradients is a secondary factor. In particular, diffusion-limited regions exhibited localized acidification and accumulation of stress biomarkers—such as the release of nucleotides—features that cannot be detected using conventional bulk assays. By providing a versatile platform for real-time mapping, this work enables the mechanistic dissection of cell adaptation to microenvironmental stress and supports the prediction of metabolic signatures underlying drug response and treatment outcomes. Full article

Background/Objectives: Sickle cell disease (SCD) is the most prevalent inherited pediatric hematologic disease. Pain is the most common complaint and primary reason for emergency care. Effective coping is critical to improved quality of life for individuals with SCD and other chronic illnesses. Hope, engendered by provision of comprehensive care, may explain the positive impact of effective coping and improved health outcomes. The relevance of effective coping skills and hope’s impact on repeated hospitalizations and/or length of hospitalization stay (LOS) among adolescents with SCD is considered. A regional, comprehensive pediatric sickle cell center (RCPSCC) provided the services. Methods: Patients with SCD, ages 13 through 21 years seen in a university RCPSCC (URCPCC-SCD), completed surveys: a general scale providing a broad range of positive and maladaptive coping-related issues, and KIDCOPE, a standardized scale measuring pediatric coping strategies. Medical records were reviewed for frequency of hospitalization and length of stay (LOS) for the eight months before study entry. Results: Thirty-four URCPCC-SCD outpatients, mean/median age of 16 years, entered the study, and data were analyzed for 33. All reported some sense of future hopefulness, and almost half reported feeling “tense or wound up” most of the time. Use of avoidant or negative coping strategies in response to daily stress correlated positively with increased LOS. Conclusions: Youths with SCD require effective coping strategies to improve self-efficacy and related hope for brighter futures. Individualized, comprehensive treatment and support to families and individuals at risk for sickle cell crisis are uniquely offered in a URCPCC-SCD. Their contributions to service delivery and clinical outcome are expected to enhance hope, mitigate prolonged hospitalizations, and improve adherence to treatment (N = 268). Full article

Introduction: Type 2 diabetes mellitus predisposes patients to neuropathy, peripheral arterial disease, and diabetic foot ulcers, which may become infected and progress to osteomyelitis, increasing the risk of amputation. The growing prevalence of multidrug-resistant organisms complicates management. Photodynamic therapy (PDT), which combines a photosensitizer with light-emitting diode irradiation to generate reactive oxygen species, has emerged as a potential adjunctive antimicrobial strategy without inducing resistance. Objective: To describe clinical outcomes observed in patients with diabetic foot osteomyelitis treated with adjunctive photodynamic therapy (PDT), with emphasis on wound evolution, limb preservation, and healing time. Methods: This prospective case series included patients with osteomyelitis secondary to infected diabetic foot ulcers treated at a university hospital. Demographic and clinical data were collected from medical records. Serial photographic documentation was used to monitor wound progression and tissue response during therapy. Results: Sixteen patients with diabetic foot osteomyelitis were included. Complete healing was achieved in 13 patients (81.25%), while 2 patients (12.5%) remained under treatment with partial healing and 1 (6.25%) underwent major amputation. Among healed patients, healing time ranged from 19 to 546 days, with a median of 118 days. The number of photodynamic therapy sessions ranged from 2 to 12, depending on the clinical course of each case. Healing time varied among patients, and the hallux was the most frequent site of osteomyelitis. During follow-up, only one patient underwent major amputation, whereas the remaining patients either achieved complete healing or were still under treatment at the time of analysis. Healing time was comparable between insulin-dependent and non-insulin-dependent diabetes, although numerically shorter in the latter. Longer healing periods were associated with more treatment sessions. Conclusions: In this prospective uncontrolled case series, adjunctive PDT was associated with favorable clinical evolution in a subset of patients with diabetic foot osteomyelitis. However, because of the small sample size and the absence of a control group, these findings should be considered preliminary and hypothesis-generating. Full article

Background/Objectives: Acute kidney injury (AKI) is a clinically important complication among hospitalized urology patients. However, data from general urology inpatient populations remain limited. We aimed to assess AKI frequency in a monitored urology inpatient cohort, identify associated predictors, and develop an exploratory admission-based risk stratification model. Methods: We conducted a retrospective observational cohort study of adults admitted to a tertiary urology ward between June 2023 and May 2024 who had at least two serum creatinine measurements during hospitalization. AKI was defined according to Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria. Demographic, clinical, laboratory, and procedural data were analyzed. Multivariable logistic regression identified factors associated with AKI and was used to construct a reduced exploratory admission-based risk model. Results: Among 196 monitored patients, 67 (34.2%) developed AKI during hospitalization, and 82.1% had KDIGO Stage 1 AKI. Higher admission serum creatinine, hypertension, nephrolithiasis, and ureteral interventions were independently associated with AKI. AKI was also associated with longer hospitalization (6.4 ± 4.2 vs. 5.1 ± 3.2 days, p = 0.044). The reduced exploratory model identified low-, intermediate-, and high-risk groups with progressively increasing AKI incidence (7.7%, 32.3%, and 76%, respectively; AUC = 0.76). Conclusions: In this monitored cohort, AKI was frequent and associated with admission characteristics and prolonged hospitalization. These findings support targeted renal monitoring in higher-risk patients. The admission-based risk model is exploratory and requires validation in prospective multicenter cohorts before clinical implementation. Full article

Background: Headache is a frequent but often underestimated complaint in patients with sarcoidosis. In clinical practice, headache is commonly interpreted as secondary to neurosarcoidosis, potentially overlooking the presence of primary headache disorders, particularly migraine. The prevalence and clinical relevance of migraine in sarcoidosis remain insufficiently characterized. Objective: To investigate the prevalence and clinical characteristics of migraine in patients with sarcoidosis and to explore its association with pulmonary functional outcomes. Methods: The OUtcome and Clinical characteristics of primary Headache in patients with Sarcoidosis (OUCH!) Study is a multicenter, retrospective, observational study including adult patients evaluated at pulmonology outpatient clinics and headache centers between January 2019 and January 2021. Demographic, clinical, radiological, and pulmonary function data were collected. Patients were stratified according to the presence or absence of migraine. Pulmonary function parameters were compared using non-parametric statistical tests. Results: Seventy-two patients with sarcoidosis were included; 21 (29.2%) were diagnosed with migraine. Migraine prevalence was higher than expected for the general population. Pulmonary involvement was the most frequent disease manifestation. Patients with migraine showed significantly lower DLCO values compared with those without migraine (median ( IQR): 55 (40–70) vs. 78 (65–90); p = 0.0009). No significant differences were observed in spirometric parameters or radiological patterns between groups. Conclusions: Migraine is a common comorbidity in sarcoidosis and is associated with reduced DLCO, suggesting a link with greater functional disease burden rather than structural lung damage. Migraine should be recognized as a primary headache disorder in this population, rather than automatically attributed to neurosarcoidosis. These findings support a multidisciplinary, patient-centered approach and warrant prospective studies to clarify shared inflammatory, vascular, and neuroimmune mechanisms. Full article

In recent years, plant stress biology has moved beyond single-pathway descriptions toward an integrated framework in which stress perception, hormonal control, and gene regulation are tightly interconnected. Early events such as membrane-associated sensing, calcium influx, reactive oxygen species (ROS) generation, and kinase activation converge with phytohormonal networks to shape context-dependent responses. Within this framework, abscisic acid, salicylic acid, jasmonates, ethylene, auxin, cytokinins, gibberellins, brassinosteroids, and strigolactones function not as isolated regulators but as components of a dynamic signaling matrix that balances survival, defense, growth restraint, and recovery. These hormonal signals are ultimately translated into adaptive outcomes through extensive transcriptional and post-transcriptional reprogramming mediated by transcription factors, RNA-based regulators, chromatin remodeling, and stress memory mechanisms. This review synthesizes current understanding of how plants integrate stress perception, phytohormonal crosstalk, and transcriptional regulation to establish stress tolerance. We first examine the molecular basis of stress sensing and early signaling. We then discuss the central functions of major phytohormones and the logic of hormone–hormone interaction networks in coordinating stress adaptation. Next, we analyze transcriptional, post-transcriptional, and epigenetic mechanisms that determine response specificity, intensity, and persistence. We further highlight points of convergence between abiotic and biotic stress responses and discuss how combined stresses challenge traditional single-stress models. Finally, we consider the roles of omics, systems biology, and translational technologies in decoding and engineering stress-resilient phenotypes. By integrating these perspectives, this review presents plant stress tolerance as a multilevel systems property and outlines key priorities for future research aimed at developing climate-resilient crops. Full article

Higher education institutions are under increasing pressure to strengthen environmental education (EE) due to critical environmental challenges, while also addressing learner support, engagement, and instructional resource constraints. Recent advances in conversational artificial intelligence (AI), particularly generative AI systems based on large language models such as ChatGPT, enable new forms of human–machine cooperation and provide opportunities for interactive guidelines and individualized feedback. This study evaluates AI-supported EE compared with conventional classroom instruction using a quasi-experimental pre-test/post-test research design. Forty undergraduate students from a Libyan university were recruited and assigned to either the AI-supported EE group (n = 20) or a conventional classroom control group (n = 20). Both groups followed the same EE curriculum over eight weeks. Learning outcomes were assessed across environmental knowledge, attitudes, and environmentally responsible behavior using structured instruments. Paired-samples t-tests indicated statistically significant improvements within the AI-supported group across all outcomes (p < 0.05). However, between-group comparisons did not show statistically significant differences. Analysis controlling for baseline differences indicated a statistically significant group effect for knowledge (p < 0.05), while attitudes and behavior remained non-significant. These findings suggest that AI-supported learning may support EE learning for higher education. Full article

Androgen receptor (AR) signaling has emerged as a potential molecular target in triple-negative breast cancer (TNBC), a clinically aggressive and biologically heterogeneous subtype of breast cancer with limited targeted treatment options. Androgens, the main ligands of AR, have been reported to exert antiproliferative and anti-estrogenic effects in normal mammary epithelium; however, the role of AR signaling in TNBC remains controversial and appears to depend strongly on tumor molecular context. In certain experimental settings, elevated androgen levels have been associated with reduced tumor growth, whereas AR activation has also been linked to signaling pathways involved in cell survival, migration, and invasiveness. AR signaling can occur through classical androgen-dependent mechanisms, as well as through ligand-independent activation mediated by protein kinases and intracellular pathways. Increasing interest in AR biology has led to the evaluation of several anti-androgen therapies in AR-positive TNBC, including agents such as enzalutamide, enobosarm, orteronel, bicalutamide, and seviteronel. Although clinical activity has generally been modest, these studies highlight the potential relevance of AR-targeted strategies in selected patient subgroups. This review summarizes current knowledge on androgen and AR signaling in TNBC, integrating molecular mechanisms, preclinical evidence, and clinical studies, and discusses emerging therapeutic strategies aimed at improving patient treatment outcomes. Full article

Purpose: Oral squamous cell carcinoma (OSCC) carries a substantial burden in low- and middle-income countries as well as underserved subpopulations within high-income settings, where structural barriers contribute to worse outcomes. While evidence supports targeted screening of high-risk groups, practical guidance for designing organized, quality-assured programs remains limited. This review proposes a framework to translate contemporary cancer-screening principles into operational criteria for OSCC. Methods: A review following the Scale for the Assessment of Narrative Review Articles principles was conducted. Conceptual papers, international evaluations, implementation studies, and programmatic guidance were included. The evidence was synthesized narratively, with emphasis on contemporary cancer-screening principles, implementation frameworks, and their applicability to OSCC. Results: Clinical oral examination can improve the detection of OSCC in early stages and reduce mortality among high-risk groups when embedded in coordinated care pathways. Effective programs require governance structures, screening policies, risk-stratified approaches, and robust information systems capable of call-recall, referral tracking, and quality monitoring. Dental schools and academic clinics may serve as feasible regional hubs for programs within mixed health systems. Conclusions: Aligning core OSCC-screening principles with operational enablers offers a practical pathway to develop context-appropriate programs that strengthen capacity, promote equity, and generate evidence for responsible scale-up. Full article

Background/Objectives: Oral squamous cell carcinoma (OSCC) exhibits substantial biological heterogeneity, and current clinicopathological risk stratification incompletely reflects tumor metabolic behavior. Stable isotope ratio mass spectrometry enables the quantitative assessment of carbon and nitrogen isotopic composition, potentially capturing cumulative metabolic reprogramming associated with tumor aggressiveness. This study evaluated whether isotopic signatures of tumor tissue and surgical margins are associated with lymph node metastasis and survival outcomes in OSCC. Methods: In this prospective study, 54 consecutive patients undergoing primary surgical treatment for OSCC were enrolled. Paired samples derived from tumor tissue and surgical margins were analyzed using isotope ratio mass spectrometry to determine the relative abundance of nitrogen-15 and carbon-13 isotopes. The primary endpoint was pathological lymph node metastasis. Secondary endpoints included disease-free survival and overall survival. Paired comparisons were performed using Wilcoxon signed-rank tests with false discovery rate correction. Logistic regression models for nodal metastasis were constructed using Firth penalization with bootstrap internal validation, while survival outcomes were evaluated using Cox proportional hazards models with model complexity restricted according to the number of events. Results: Tumor tissues demonstrated significantly lower δ¹³C and δ¹⁵N values and higher nitrogen-to-carbon ratios compared with surgical margins (all adjusted p < 0.05). In multivariable analysis, tumor δ¹⁵N was independently associated with lymph node metastasis and modestly improved model discrimination. However, it was not independently associated with disease-free or overall survival. Exploratory analyses indicated that higher δ¹³C values in surgical margins were independently associated with shorter disease-free survival. Conclusions: These findings suggest that isotope ratio mass spectrometry-based isotopic profiling identifies reproducible metabolic differences between tumor and margin tissues in OSCC. Tumor δ¹⁵N is associated with lymph node metastasis, whereas margin δ¹³C may reflect recurrence risk and potentially capture metabolic field effects. These findings are hypothesis-generating and warrant validation in larger, independent cohorts. Full article

Underwater image enhancement has been widely studied to improve visual quality; however, its impact on downstream geometric tasks such as sparse 3D reconstruction remains insufficiently understood. In particular, visually enhanced images do not necessarily lead to improved feature matching or reconstruction performance. This work addresses the problem of selecting appropriate preprocessing strategies for underwater Structure-from-Motion (SfM) pipelines from a task-oriented perspective. We propose a lightweight machine-learning-based preprocessing selector that predicts reconstruction performance from image statistics and recommends suitable enhancement strategies for each input sequence. To ensure reliable evaluation, we introduce a grouped leave-one-parent-sequence-out protocol that avoids overlap-induced bias common in clip-wise splitting. Experiments are conducted on challenging underwater datasets derived from the Real-world Underwater Image Enhancement (RUIE) benchmark, with the primary comparison variable defined as the number of reconstructed sparse 3D points. Supporting geometric variables, including the number of registered images, mean track length, and mean reprojection error, are recorded for interpretation. Results show that preprocessing choices significantly affect reconstruction outcomes and that the optimal strategy is scene-dependent. The proposed selector consistently improved over raw input on the evaluated grouped subset and remained competitive with a strong fixed preprocessing baseline. The grouped leave-one-parent-sequence-out protocol is intended to reduce overlap-induced bias common in clip-wise splitting and to provide a more conservative estimate of generalization. This work highlights the importance of task-aware preprocessing and reliable evaluation in underwater vision systems, offering practical insights for deploying enhancement strategies in real-world 3D reconstruction pipelines. Full article

This study investigates the educational potential of Large Language Models (LLMs) for automating rule-checking tasks in Building Information Modeling (BIM) instruction. A quasi-experimental classroom implementation was conducted over two consecutive semesters with 55 graduate students in a Construction Management program. In Fall 2024, students were taught manual rule-checking techniques, whereas in Spring 2025, students received additional instruction in LLM-based prompting and Python code generation for automated compliance checking. A mixed-methods evaluation was conducted using surveys, NASA Task Load Index ratings, assignment-based learning outcomes, and structured interviews. Compared with the manual-only cohort, the LLM-assisted cohort reported significantly lower mental, temporal, and frustration demands, as well as higher perceived time efficiency and overall effectiveness. The LLM-assisted group also achieved significantly higher performance in violation detection and method accuracy, although no significant differences were observed in code interpretation or reflective analysis. Qualitative findings further revealed both the efficiency benefits of AI-assisted automation and persistent challenges related to prompt refinement, debugging, and output validation. These findings suggest that LLMs can enhance BIM instruction when paired with structured pedagogical scaffolding to support critical oversight and novice learners. Full article

CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited individual-level mechanistic personalisation under data constraints. At its core, we employed a reaction–diffusion–chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was used to recover model-consistent latent-parameter pseudo-labels generated by a limited inverse problem. Within this surrogate-target setting, parameters characterising the tumour microenvironment and CAR-T cell exhaustion were reproduced most robustly, whereas antigen escape and individualised initial conditions were substantially less well constrained. As an auxiliary reference point, we also considered a direct empirical baseline for binary clinical outcomes. This baseline indicated that the observed clinical features contained a more stable signal for disease control than for objective response. A favourable response was associated with high CAR-T cell infiltration and cytotoxic potency, whereas resistance was linked to exhaustion, antigen escape, and a suppressive microenvironment. Overall, the proposed approach should be interpreted as an internally validated, hypothesis-generating proof-of-concept platform for mapping clinical features to mechanistically interpretable surrogate latent targets, rather than as evidence for validated recovery of true patient-specific biological parameters. Full article