Search Results (3,010)

Tumour development and progression involve complex cell-cell interactions and dynamic co-evolution between cancer cells, immune cells and stromal cells in the tumour microenvironment and this may influence therapeutic resistance. A large proportion of this network relies on direct physical interactions between cells, particularly T-cell mediated interactions. Cell-cell communication inference has now become routine in downstream scRNAseq analysis but this mostly fails to capture physical cell-cell interactions due to tissue dissociation. Doublets occur naturally in scRNA-seq and are usually excluded from analysis. However, they may represent directly interacting cells that remain undissociated during library preparation. In the present study, we uncover the physical interaction landscape of the ovarian tumour microenvironment using the scRNAseq datasets from 13 treatment-naive ovarian cancer patients. Focusing on T-cell-Macrophage (T-Mac) interaction doublet, we reveal the modulatory effect of macrophages on T cells and the potential influence of this interaction on therapeutic response. Our findings show that T-Macs from resistant patients are functionally polarized to the M2 phenotype and engage T cells to induce T-cell exhaustion. Whereas, T-Macs from sensitive patients are predominantly of the M1 polarized phenotype, physically engaging T cells that lack exhaustion signatures. We also demonstrate that T cells and macrophages in T-Mac doublet are interacting primarily for the purpose of antigen presentation, with the enrichment of several ligand-receptor pairs involved in TCR-MHC interactions and immune synapse formations. We partly validated some of these findings from a spatial transcriptomics dataset of ovarian cancer patients from a separate cohort. Full article

Background/Objectives: Large language models have created new pathways for patients to access health information, yet little is known about how the general population uses these conversational artificial intelligence (AI) tools for oral health concerns. This study investigated patterns of AI use as a source of oral health information, the nature of data shared with these systems, users’ perceptions, and the impact on dental care-seeking behavior. Methods: A cross-sectional study was conducted among adults from Bihor County, Romania, using a structured 16-item online questionnaire distributed via social media, with eligibility restricted to individuals who had previously used conversational AI for oral health information. The final sample comprised 393 valid responses from this self-selected group of users. Fisher’s exact test and Z-tests with Bonferroni correction were applied (α = 0.05). Results: Most participants were female (68.2%), university-educated (52.9%), and lived in an urban setting (88.3%). Significant differences in patterns of AI use for oral health information were identified according to age, sex, and living environment (p < 0.001). Younger participants used AI more frequently, while older individuals perceived the information as less clear. The vast majority used AI for informational or preliminary guidance purposes, with very few treating it as a substitute for professional opinion. A relevant subset shared visual data (intraoral photographs or radiographs) with AI systems, raising data privacy concerns. Rural participants more frequently delayed dental visits and less often discussed AI-derived information with their dentist compared to urban participants. Conclusions: When used by the public as a source of oral health information, AI is increasingly adopted, with adoption shaped by age, sex, and socioeconomic context. These findings concern only this informational use and do not extend to other applications of AI in dentistry, such as diagnostic support, image analysis, or clinical decision-making. Dental professionals should proactively engage patients about their use of AI for oral health information to ensure that digitally obtained content is appropriately contextualized. Full article

Inspiratory Muscle Training (IMT) is a well-established intervention for enhancing respiratory strength; however, its clinical efficacy is frequently compromised by low adherence and high dropout rates due to the repetitive nature of traditional protocols. While screen-based gamified biofeedback is the most common digital training approach, these non-immersive (NI) conditions often lead to split attention and lack the controlled, private environments necessary for optimal engagement and exercise quality. This study evaluated the impact of immersion level on respiratory performance and user experience by comparing two conditions in gamified Virtual Reality (VR) environments: fully immersive (FI) and NI. The IMT was performed using an instrumented threshold load device, where real-time mouth pressure drove gamified environments designed to guide respiratory patterns. The results demonstrate that perceived effort and fatigue remained consistent across immersion levels; however, the FI condition provided a higher level of immersion despite a reported reduction in breathing control. Notably, 73% of participants found the FI condition more motivating, with higher scores in focus (60%), user preference (63%), and efficacy (40%). Crucially, increased task complexity widened the performance gap between immersion levels. In scenarios requiring higher respiratory muscle workload, significant differences emerged in breathing regulation, specifically regarding the inspiratory duty cycle, favoring the NI condition. Specifically, the NI condition exhibited a higher inspiratory duty cycle and higher scores compared to the FI condition. These findings suggest that while FI significantly boosts user motivation, the synergy between the head-mounted display (HMD) and the physical demands of the threshold device may disrupt respiratory regulation during high-effort tasks. This highlights a critical design trade-off between immersion levels and physiological precision that must be addressed in the development of future digital pulmonary interventions. Full article

Higher Education Institutions (HEIs) are increasingly tasked with fostering pro-environmental agency to address the global climate crisis. Within physical activity and sport sciences, future nautical sports managers occupy a unique role as stewards of marine ecosystems. This study investigates the sustainability consciousness of 170 undergraduate university students from a single institution. It focuses on analyzing the knowledge–action gap within the framework of the 2030 Agenda. Utilizing the Sustainability Consciousness Questionnaire (SCQ-S), this work assessed knowledge, attitudes, and behaviors to quantify this dissonance through a robust non-parametric statistical pipeline. Results indicate a significant knowledge–action gap (r_B = 0.903), representing a large matched-pairs rank-biserial correlation effect size for the Wilcoxon test. Furthermore, Kruskal–Wallis analyses reveal that this discrepancy is transversal across student profiles, suggesting a state of informed paralysis within this specific cohort, where high theoretical mastery fails to translate into sustainable professional routines. Crucially, the frequency of engagement in nature-based sports, encompassing both nautical and terrestrial outdoor activities, does not significantly reduce this dissonance. These contextual findings indicate that physical exposure to nature does not automatically mitigate the knowledge–action gap, highlighting the need for explicit reflective pedagogies in higher education. Full article

Small cell lung cancer (SCLC) has historically been treated as a single, uniformly aggressive disease defined by neuroendocrine differentiation, near-universal loss of TP53 and RB1, and the absence of classical druggable oncogene addictions. Two converging lines of evidence are now reshaping that view. First, transcriptomic profiling has resolved SCLC into molecular subtypes—SCLC-A (ASCL1-driven), SCLC-N (NEUROD1-driven), SCLC-P (POU2F3-driven), and SCLC-I (inflamed)—with distinct immune microenvironments, surface-antigen expression patterns, and emerging therapeutic vulnerabilities, although intratumoral heterogeneity and phenotypic plasticity complicate clean subtype assignment. Second, the development of delta-like ligand 3 (DLL3)-directed therapies provides a natural experiment: the same validated surface antigen failed as an antibody–drug conjugate (rovalpituzumab tesirine, three negative randomized trials) yet succeeded as a bispecific T-cell engager (tarlatamab, which received FDA accelerated approval in 2024 and subsequent traditional FDA approval in 2025 following positive confirmatory phase 3 data). In this review, we integrate the current first-line standard of care—chemoimmunotherapy with atezolizumab- or durvalumab-based regimens followed by maintenance intensification with lurbinectedin–atezolizumab (IMforte)—with the molecular framework of subtypes and biomarkers, and we use DLL3 as a case study to propose that delivery modality is an important determinant of therapeutic success in SCLC and should be considered alongside target biology and tumor heterogeneity. Rapid proliferation, antigen heterogeneity, subtype plasticity, and a relatively less immunogenic microenvironment systematically penalize modalities dependent on payload accumulation and cell-cycle progression and reward modalities that recruit catalytic, cell-cycle-independent cytotoxic effectors. The emerging B7-H3 and SEZ6 programs—including ifinatamab deruxtecan and ABBV-706—are the next test of this framework. We discuss implications for biomarker development, trial design, and the operational challenges of subtype-guided precision oncology in a disease where tissue is scarce and biology shifts under therapy. Full article

Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η² = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments. Full article

This article addresses the theological implications of Artificial Intelligence (AI) within the broader Catholic theology of technology. The emergence of AI challenges the traditional theological accounts of technicity and human action and demands a renewed reflection capable of engaging current technological developments and transformations. The study first examines artificial intelligence through the point of view of postphenomenology, highlighting the mediating role of technology in human perception, agency, and world-formation. It then revisits the theological concept of the imago Dei, central to Christian reflections on human activity and technology, by placing in dialogue different theological interpretations of the image of God and their anthropological implications. Offering a brief bibliographic and interdisciplinary review, the article analyses how current debates on AI and the imago Dei reshape questions concerning human uniqueness, creativity, embodiment, and moral responsibility. A renewed theological interpretation of the image of God, integrated with contemporary philosophies of technology, offers valuable insights into the nature of human action and human nature within technologically mediated contexts. The article concludes that Catholic theology can contribute a distinctive and critically constructive perspective to current discussions on artificial intelligence by articulating a more dynamic and relational understanding of humanity, human activity and technological mediation. Full article

Background/Objectives: Malignant melanoma is characterized by constitutive PI3K/Akt/mTOR and MAPK activation, driving aggressive behavior and therapeutic resistance. Alpha-lipoic acid (αLA), a naturally occurring dithiol compound with an established clinical safety profile, has shown anticancer potential; however, its integrated molecular mechanisms in melanoma remain poorly defined. This study aimed to comprehensively evaluate the cytotoxic and mechanistic effects of αLA in B16F10 murine melanoma cells. Methods: Antiproliferative effects were assessed by MTT assay at four concentrations (250, 500, 750, 1000 µM) over 48 h. Protein levels of apoptotic markers (Bax, Bcl-2, Caspase-3, AIF), kinase signaling components (p-Akt, p-mTOR, p-ERK, p-JNK), ER stress markers (GRP78, GADD153/CHOP), and cell cycle regulator Wee1 were quantified by ELISA at a specifically selected sub-lethal concentration of 750 µM (inducing ~38% growth inhibition). Results: αLA dose-dependently inhibited B16F10 proliferation. At 750 µM, it triggered robust intrinsic apoptotic signaling, evidenced by a nearly 10-fold shift in the Bax/Bcl-2 ratio and greater than 9-fold Caspase-3 activation. Elevated AIF suggested profound mitochondrial stress and the potential priming of concurrent caspase-independent cell death mechanisms. αLA suppressed survival signaling by reducing p-Akt (44%), p-mTOR, p-ERK, and p-JNK. Treatment triggered lethal ER stress via GRP78 and GADD153/CHOP upregulation and upregulated Wee1, suggesting the induction of stress-responsive checkpoint signaling. The simultaneous CHOP upregulation and p-Akt suppression highlight a concurrent dysregulation of stress and survival pathways, suggesting a potential pro-apoptotic interplay. Conclusions: αLA exerts potent multi-target anticancer effects by inducing a broad spectrum of associated molecular changes, including the suppression of PI3K/Akt/mTOR and MAPK networks, induction of ER stress, engagement of cell cycle checkpoints, and activation of the mitochondrial Bax/Bcl-2/Caspase-3 axis. Importantly, these correlative findings do not establish proven pathway dependencies. Nevertheless, this concurrent dysregulation positions αLA as a potential disruptor of inter-pathway resilience underlying drug resistance. Full article

The rapid evolution of Padel in Saudi Arabia (SA) has positioned the sport as a popular recreational and social activity, mainly among young adults. However, limited research has examined how different forms of sport motivation are associated with mental health outcomes in this emerging context. Drawing on Self-Determination Theory (SDT), this study investigated the associations between intrinsic and extrinsic motivation and depression, stress, and anxiety among Padel players in SA. A quantitative, cross-sectional online survey was conducted with a sample of 475 players, the majority of whom were aged 17–35 and held at least a bachelor’s degree. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to evaluate the relationships between multidimensional motivation factors and mental health symptoms. The findings revealed a nuanced, at times paradoxical, pattern of relationships. Intrinsic motivation to experience stimulation (engaging in an activity because of the positive sensations, excitement, enjoyment, or stimulation that the activity itself provides, rather than for external rewards or pressures) was consistently associated with lower levels of depression, stress, and anxiety, suggesting that enjoyment-driven involvement is associated with better mental health outcomes. In contrast, intrinsic motivation to accomplish was positively correlated with all three mental health indicators, indicating that achievement-oriented engagement might intensify emotional pressure. Among extrinsic motivations, external regulation was significantly associated with poorer mental health outcomes. In contrast, introjected regulation unexpectedly displayed a negative association with psychological distress, demonstrating a potentially adaptive role in this setting. Identified regulation, however, was not significantly associated with any mental health symptoms. These results underscore the “double-edged” nature of sport motivation, showing that not all internal or external motives yield uniformly positive consequences. The study contributed to the growing literature by providing a context-specific understanding of how motivational dynamics function within a rapidly growing sport in Saudi Arabia. In practice, the findings suggested that enjoyment-based involvement was associated with more favourable mental health outcomes, whereas performance-related pressures might be associated with less favourable outcomes. Full article

Extracurricular activities (ECAs) are widely recognized as contributors to holistic student development, although the nature and magnitude of their associations with academic performance remain context-dependent across educational levels. At the same time, artificial intelligence and machine learning are increasingly used in many educational settings, while variables that are not directly related to academic content, such as participation in extracurricular activities, are very rarely used. The present review is a systematic literature review conducted according to the PRISMA 2020 framework. It consisted of 30 empirical studies published between 2010 and 2025 which examined the relationship between ECAs and academic performance, with focused attention on studies that incorporated AI and ML techniques. The majority of the studies, across all education levels, reported neutral to positive associations between ECAs and academic performance metrics, such as grades, test scores, and engagement indicators. The evidence suggests that moderate involvement in ECAs generally does not harm academic performance, while excessive involvement can generate time conflicts that undermine study. A substantial proportion of the studies reviewed (12 of 30, or 40%) applied AI or ML methods to predict academic outcomes. These studies reported improvements in predictive accuracy when ECA-related variables were included, though performance metrics varied widely across algorithms, datasets, and outcome measures, precluding direct comparison. Overall, the findings suggest that ECAs complement student development, with effects contingent on activity type, intensity, and educational context. Future research should prioritize longitudinal designs, standardized ECA measurement, and interpretable AI models that support transparent and equitable decision-making. Longitudinal studies are needed to clarify the temporal sequence of associations between ECA participation and academic outcomes, and to assess equity of access. Full article

Hypoxic training is widely used to enhance endurance performance, yet its application in combat sports such as judo is poorly understood. This study explored (1) hypoxic training practices, (2) perceptions, and (3) educational pathways among judo athletes and performance staff (coaches/practitioners). A total of 173 judo athletes and 39 performance staff completed an online questionnaire covering participant characteristics, hypoxic practices, education, and perceptions. Closed-ended responses were analysed using frequency statistics, and open-ended responses using thematic analysis. Hypoxic training was not widely used, with most respondents reporting no engagement. Among participants currently using hypoxic training, the primary aim was to enhance sea-level performance (13.7%), with limited use for altitude competition (8.5%). Natural altitude was the most common modality, with 7.1% respondents currently using it, typically at ≤1500 m. Only 20.8% of participants reported receiving or delivering education on hypoxic training. Perceptions were mixed, with 38.7% agreeing it benefits judo performance, although agreement was higher among performance staff than athletes. Thematic analysis identified perceived benefits (e.g., time-efficient fitness gains) and drawbacks (e.g., cost, access, and scheduling constraints). Hypoxic training is not a common practice in judo, but amongst some respondents it is perceived as potentially beneficial; these perceptions should not be interpreted as evidence of effectiveness. Its use is primarily oriented towards improving sea-level performance, and current knowledge appears largely informal. Greater sport-specific guidance and education may support more informed application in practice. Full article

The transition to university is a major life shift shaped by academic, social and personal change. Guided by Schlossberg’s Transition Theory, this study provides rare insight into pre-arrival nervousness among first-year undergraduates (UG) entering an English university. This paper reports findings from the qualitative component of a wider study exploring students’ pre-arrival experiences and expectations. A total of 1578 incoming UG students across twelve academic schools at a UK university provided qualitative responses to an open-ended survey question designed to capture self-reflective accounts of nervousness prior to enrolment. Using an inductive content analysis approach, four key themes were identified: social integration and belonging, academic adjustment and workload, health and wellbeing, and financial and practical concerns. Findings highlight the multi-faceted and anticipatory nature of transition, demonstrating that students’ perceptions of readiness are formed before direct engagement with the university environment. The study extends existing literature by positioning nervousness as an integral component of the transition process rather than a reaction to it. These insights offer evidence-based guidance for institutions to implement proactive, inclusive pre-arrival strategies that support early engagement, enhance student confidence and promote successful adjustment to higher education settings. Full article

This study presents a numerical investigation of a slit fin-enhanced phase change material (PCM)-based battery thermal management system (BTMS) for an 18650 cylindrical LiNi_xCo_γMnzO₂ lithium-ion battery. The proposed design modifies the conventional solid rectangular external fins by introducing four longitudinal slit fins with uniformly distributed rectangular through-thickness slot cutouts along the fin height. This modification increases the PCM-fin interfacial contact area and creates additional natural convective heat dissipation pathways from the PCM region to the ambient environment while maintaining the same BTMS envelope, PCM thickness, fin count, housing geometry, and material selection as the validated rectangular-fin baseline. The lumped-capacitance thermal model was used for battery heat generation, while the enthalpy-porosity approach was employed to model PCM melting. Simulations were performed in ANSYS Fluent 2024/R2 at 1C, 3C, 5C, and 7C discharge rates at an ambient temperature of 308.15 K. Paraffin wax PCM with a latent heat of approximately 240,000 J/kg was used. The rectangular fin model was first validated against the baseline study, achieving an average cell wall temperature error of 1.03% and a maximum error of 1.47% at 5C, while the total temperature and liquid fraction deviations remained below 0.73%, confirming the reliability of the numerical model. Mesh independence and temporal convergence studies further confirmed that the selected 0.50 mm polyhedral mesh and 0.5 s time step provided accurate and stable results. The results demonstrate that the slit fin geometry provides metric-dependent improvements in PCM utilization, thermal protection duration, and high-rate latent-heat activation rates. At 1C, both configurations remained well below the 318.15 K safety threshold, but the slit fin configuration maintained approximately 0.7 K lower total temperature at 2500 s and delayed PCM melting by about 300 s compared with rectangular fins, preserving more latent heat capacity for later thermal loading. At 3C, the slit fin design extended the thermal protection duration from 1650 s to 2500 s, corresponding to a 51.5% improvement, and increased PCM latent heat utilization from LF = 0.42 to LF = 0.49, representing a 16.7% increase. At 5C, slit fins initiated PCM melting approximately 3.5 times earlier, around 100 s, compared with 350–400 s for rectangular fins, and reached LF = 0.50 at 620 s, whereas rectangular fins reached only LF = 0.37 at 1480 s. This corresponds to approximately 2.87 times faster PCM utilization and 35.1% greater PCM melting. At 7C, the slit fin system again showed stronger PCM engagement, corresponding to 35.7% greater PCM utilization. Temperature and liquid fraction contours confirmed that the slit openings intensify localised PCM melting near the heat source, improve heat spreading through the PCM domain, and support natural convection-assisted melting. Overall, the slit-fin geometry provides a geometry-based enhancement for PCM utilization and thermal protection without changing system size or material selection for PCM-based BTMSs, improving latent heat utilization and thermal protection without increasing system size, PCM volume, or material complexity. Full article

With recent breakthroughs in affective computing and AI algorithms, government chatbots are evolving from function-oriented tools to emotionally responsive agents capable of detecting, decoding, and responding to citizens’ emotional cues. This shift holds significant potential for enhancing citizens’ continued usage intention. Drawing on social presence theory and employing a scenario-based experimental design, this study investigates whether, how, and under what conditions empathy by government chatbots affects citizens’ willingness to continue using such services. The findings reveal that empathy positively influence continued usage intention by enhancing users’ psychological engagement and perceived information richness. However, the strength of this effect is significantly moderated by the type of time pressure. Specifically, under endogenous time pressure, the positive effect of government chatbot empathy is amplified, whereas under exogenous time pressure, the effect is attenuated. This study uncovers the double-edged nature of empathic design in government chatbots, contributing to the literature on human–robot interaction in public service contexts. It also offers practical implications for the adaptive design of empathic government chatbots to optimize citizen engagement and service effectiveness. Full article

Urban green infrastructure (GI) is widely promoted for cooling, stormwater regulation, biodiversity support, and human health benefits; however, these benefits remain unevenly distributed across communities with different socio-economic status. Machine learning (ML) is increasingly used in GI planning and design through high-resolution mapping and demand and exposure modelling, enabling planners and landscape architects to explore scenarios and assess alternative solutions based on the benefits generated. However, ML risks generating or perpetuating spatial injustice through biassed training, opaque optimisation priorities, and epistemic exclusion of Indigenous and local knowledge when models are not developed and applied transparently and collaboratively. This perspective discusses recent GI and ML trends and debates to: (i) clarify how ML can support equity-oriented GI planning; (ii) identify technical and socio-economic risks; and (iii) outline ethical and governance pathways supportive of legitimate and accountable GI planning. We argue that ML should be treated as a component of socio-technical governance rather than a neutral technical tool and therefore should be applied through collaborative design and periodic re-evaluations. Full article