Search Results (15,752)

Background: Oral squamous cell carcinoma (OSCC) is a multifactorial malignancy traditionally associated with tobacco exposure, alcohol consumption, and human papillomavirus (HPV) infection. It may also arise through the malignant transformation of oral potentially malignant disorders (OPMDs). However, a substantial proportion of tumors arise without clinically detectable precursor lesions, suggesting heterogeneous carcinogenic pathways. This study aimed to compare the clinicopathological characteristics of OPMDs-associated and apparently de novo OSCC. Methods: A retrospective analysis was conducted on patients with histologically confirmed OSCC referred to the department between 2015 and 2025. Baseline patient characteristics, clinical, and histopathological diagnostic management data were collected from medical records, and patients were classified as the OSCC with OPMD group or the de novo OSCC group based on the presence or absence of a history of OPMDs. Results: Seventy-seven patients were included (mean age: 70.4 years; 53.2% males, 46.75% females). OPMDs were documented in 37.66% of cases, while 62.34% of tumors developed without documented precursor lesions. No significant difference was observed in histological grade distribution between the two groups (p = 0.206), although G1 tumors were more frequent in the OSCC with OPMD group (25.93%) than in the de novo OSCC group (9.30%). The de novo OSCC group showed a significantly higher prevalence of denture-related lesions. No significant differences were observed between the two considered groups in age, sex, tobacco or alcohol exposure, HPV status, systemic diseases, or oncological history. Patients with OPMDs underwent significantly more biopsies in total and biopsies per year. Conclusions: OSCC may develop through distinct pathogenic pathways, emphasizing the need for broader clinical vigilance beyond OPMD surveillance to improve early diagnosis. Full article

Pathogenic fungi and their synergistic interactions with bark beetles, leading to vascular dysfunction, physiological stress, and eventual tree mortality, increasingly threaten forest ecosystems. Because fungal colonization often precedes visible macroscopic symptoms, early detection remains a critical yet challenging task in forest health monitoring. This study proposes a real-time deep learning-based object detection framework for identifying harmful fungi in proximity to host trees to support early intervention strategies. A custom dataset comprising 8900 images was constructed to represent two classes: Healthy and Unhealthy trees, where fungal presence is detected either directly on the tree or within its immediate ecological vicinity (e.g., near root systems). A fine-tuned YOLOv11 detection architecture is developed and augmented with a squeeze-and-excitation (SE)-like attention mechanism to enhance texture-sensitive feature representation. The model is trained and evaluated using precision, recall, F1-score and mean Average Precision (mAP). Experimental results demonstrate an overall mAP@0.5 of 0.825, with class-wise average precision values of 0.926 (Healthy) and 0.724 (Unhealthy). The Healthy class achieved classification accuracy of 0.92, while 0.71 of Unhealthy instances were correctly detected. F1-Confidence and recall-Confidence metrics indicate that optimal operational performance occurs within a confidence threshold range of 0.30–0.35, balancing false positives and false negatives. Despite the approximately balanced class distribution (50.6% Healthy and 49.4% Unhealthy), detection performance for the Unhealthy class was comparatively lower because of its greater intra-class variability, heterogeneous fungal appearance, and subtle visual manifestations. Findings demonstrate the feasibility of deploying real-time object detection models for early-stage fungal surveillance and highlight the importance of confidence calibration for operational disease monitoring systems. Full article

Lung cancer is currently the most frequently diagnosed malignancy worldwide, accounting for approximately 12.4% of all cancers and, despite major advances in molecularly targeted therapies and immunotherapy, represents the leading cause of cancer-related mortality, In recent years, antibody–drug conjugates (ADCs) have emerged as a novel therapeutic strategy, combining the specificity of monoclonal antibodies with the potent cytotoxic activity of highly active payloads to selectively target tumor cells while limiting systemic toxicity. This narrative review summarizes the current role of ADCs in lung cancer, with particular focus on their structural components, mechanisms of action, and the biological features that determine treatment efficacy. We discuss the rationale for targeting established and emerging antigens in non-small cell and small cell lung cancer, including HER2, TROP2, c-MET, HER3, CEACAM5, DLL3, and other promising targets currently under clinical investigation. The principal mechanisms of primary and acquired resistance are also reviewed, including antigen modulation, altered intracellular trafficking, lysosomal dysfunction, drug efflux, tumor microenvironment-mediated immune suppression, and intratumor heterogeneity. In addition, we provide an overview of the safety profile of ADCs, highlighting the most clinically relevant adverse events and their underlying biological mechanisms. We also examine the evolving landscape of predictive biomarkers beyond antigen expression, including genomic, transcriptomic, proteomic, and liquid biopsy-based approaches, together with emerging spatial and single-cell technologies that may improve patient selection. Finally, we discuss future directions in the field, including novel payloads, next-generation linker technologies, bispecific ADCs, combination strategies, and personalized ADC development. Overall, ADCs are rapidly reshaping the therapeutic landscape of lung cancer. Continued optimization of drug design, biomarker-driven patient selection, and a deeper understanding of resistance mechanisms will be essential to fully realize their clinical potential. Full article

Failures in cloud-native systems can disrupt service availability and system reliability, while their early symptoms are often weak and dispersed across metrics, logs, traces, and interdependent services. Existing methods commonly flatten heterogeneous telemetry or model monitoring variables without preserving service identities. We propose LTFP, a lead-time-aware failure-prediction framework whose graph nodes represent services. LTFP uses modality-specific temporal encoders and gated fusion to form service states, as well as an edge-weight-aware Graph Attention Network to propagate these states over a sparse hybrid graph constructed from known dependencies and training-fitted correlations. Joint graph-level and node-level heads predict whether a failure will occur within a configured future window and rank likely responsible services. We evaluate LTFP on seven subsets from three representative cloud-native systems. Comparisons with representative source-code baselines are reported at the pipeline level, with each method retaining its original learning objective and input configuration. At the 600 s prediction-window setting, LTFP obtains a macro-average window-level precision, recall, and F1 of 92.0%, 90.4%, and 90.5%, respectively. Together with the localization and ablation results, these findings support the effectiveness of service-centered multimodal modeling under the evaluated protocol. Full article

Internet of Vehicles (IoV) intrusion detection systems (IDSs) require collaborative learning that preserves raw-data locality while producing independently checkable post-run evidence. This paper presents FL-BC-IDS, an evidence-native, privacy-aware hierarchical federated IDS in which vehicles train Differentially Private XGBoost models, roadside units perform deterministic admission and tree-bagging aggregation, and the GLOBAL stage forms an equal-weight ensemble over validated RSU models. Signed reports, privacy records, SHA-256/Poseidon commitments, scoped Groth16 proofs, reconstructable public inputs, and digest-pinned blockchain receipts provide a unified verification path. Across 10 seed-controlled runs, the mean±SD accuracy/F1 values were $0.998021\pm 0.000246$/$0.983597\pm 0.002053$ on CSE-CIC-IDS2018 and $0.999867\pm 0.000152$/$0.999495\pm 0.000579$ on CICIoV2024. With thresholds fixed exclusively from development data, the strict held-out-attack macro recall was 0.8031 and 0.9090 on CSE-CIC-IDS2018 and CICIoV2024, respectively, indicating residual attack-specific generalization limitations; supervised rolling-origin temporal refresh on CSE-CIC-IDS2018 achieved 0.984788 pooled seen-attack recall at a 0.005700 test FPR. A controlled 20-vehicle, eight-round heterogeneity and participation stress test retained 0.998151 accuracy and 0.984782 F1-score. Verification rejected invalid or context-mismatched artifacts and independently checked model–anchor consistency, RSU aggregation replay, commitments, and public inputs. The reported DP budgets are conditional learner-stage bounds for learner-input record instances, not end-to-end guarantees for original pre-preprocessing records. Full article

The equitable allocation of health resources is fundamental to building healthy cities and advancing sustainable regional development. Using panel data for 122 county-level units in Hunan Province, China, from 2011 to 2022, this study constructs an evaluation index system for healthcare resource allocation and applies trend surface analysis, spatial autocorrelation, the Dagum Gini coefficient, and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal evolution, equity, and driving factors of health resource allocation. The results show the following. (1) The level of health resource allocation in Hunan Province rose steadily, with the composite score increasing by 74%, yet a persistent spatial pattern of higher allocation in the east and north than in the west and south remained; hot spots clustered in Changsha, while cold spots concentrated in parts of Southern Hunan and Western Hunan. (2) Regional disparities narrowed gradually, and the Dagum decomposition identified transvariation density as the dominant source of inequality, with an average contribution of 53.58%, exceeding intra-group and inter-group differences. (3) The effects of the drivers exhibited marked spatiotemporal heterogeneity. Per capita GDP promoted health resource allocation mainly in developed regions, while urbanization exerted stronger positive effects in less-developed regions. The positive effect of per capita disposable income gradually shifted from less-developed to developed regions over time, and population density generally showed a positive effect, with stronger influences concentrated in the Greater Western Hunan region. This study contributes to a deeper understanding of how regional disparities and heterogeneous driving mechanisms shape health resource allocation, providing evidence for more adaptive and equitable healthcare governance. Full article

Microplastics and nanoplastics (MNPs) have emerged as widespread environmental contaminants, with increasing evidence suggesting that chronic exposure may adversely affect human health. Experimental and emerging human studies indicate that MNPs can interact with multiple biological systems and trigger a range of mechanisms, including oxidative stress, mitochondrial dysfunction, cellular injury, barrier disruption, and immune and inflammatory responses. These effects may involve direct interactions with tissues and cells as well as indirect pathways, including alterations in gut microbial and intestinal homeostasis. In turn, persistent inflammatory and metabolic disturbances may contribute to tissue dysfunction and the development or progression of chronic diseases. This narrative review summarizes current evidence on the biological effects of MNP exposure, focusing on the mechanisms linking environmental exposure to chronic inflammation and disease. We discuss their potential involvement in cardiovascular, liver, kidney, respiratory, and neurological diseases, while highlighting the emerging role of the gut microbiota and intestinal barrier as potential modulators of these effects. Given the limitations of current human evidence and the heterogeneity of experimental models, we also discuss major knowledge gaps and the need for further mechanistic and epidemiological studies to clarify the clinical relevance of chronic MNPs exposure. Full article

In recent years, the agentive role of youth groups in urban regeneration has garnered increasing academic attention. As vital carriers of urban cultural heritage, the ways in which historic districts can leverage youth dynamics to achieve vitality revitalization have emerged as a key research issue. This study selects the Fayuan Temple Historic and Cultural District in Beijing as an empirical case, constructs a public space evaluation system grounded in the concept of “youth-friendliness,” adopts a mixed-method approach integrating field surveys, questionnaire administration, and multi-source data mining, and employs Importance–Performance Analysis (IPA) modeling for deconstruction. Based on behavioral characteristic differences, youth within the district are categorized into two groups: “resident/local youth” and “transient visiting youth.” The comprehensive experience quality scores were 3.559 (Fair) for resident and local youth and 3.85 (Fair) for temporary visitors, with cultural space scoring highest for both groups. Resident youth demonstrate stronger demands for renewal concerning the completeness of cultural facilities, diversity of commercial formats, and street navigability. Transient visiting youth, by contrast, exhibit greater concern for eight factors: pedestrian safety and comfort, static traffic order, diversity of commercial formats, quality of consumption environment, richness of social venues, pleasantness of spatial scale, landscape interactivity, and street navigability. These findings indicate that youth groups with varying interactive relationships with historic districts possess significantly heterogeneous needs regarding public space utilization, and their expectations for historic district regeneration also manifest differentiated characteristics. This provides a scientific basis for formulating precise, multi-layered strategies for district renewal. Full article

This study presents an integrated geochemical approach to distinguish between genetic and reservoir-related factors controlling oil compositional variability, evaluate reservoir compartmentalization, and reconstruct hydrocarbon migration pathways within the Nuraly field and the Akshabulak group of fields in the South Turgay Basin, Kazakhstan. The study aims to develop and validate an integrated approach combining biomarker analysis and oil fingerprinting to improve the reliability of oil genetic interpretation, assess reservoir fluid communication, and reconstruct secondary hydrocarbon migration pathways. This study analyzed 164 unique crude oil samples from the Akshabulak and Nuraly fields. Oil fingerprinting was performed on all 164 samples, including 128 samples from the Akshabulak group and 36 samples from the Nuraly field. A representative subset of 75 samples, comprising 39 Akshabulak oils and 36 Nuraly oils, was additionally analyzed for biomarkers. Oil fingerprinting was conducted using low thermal mass multidimensional gas chromatography (LTM-MD-GC), whereas biomarker analysis was performed using gas chromatography–mass spectrometry (GC–MS). Principal component analysis (PCA) and hierarchical cluster analysis were applied separately to the oil-fingerprinting and biomarker datasets. The resulting classifications were subsequently compared and integrated to distinguish source-related genetic variability from reservoir-related compositional effects, including hydrocarbon migration, oil mixing, and reservoir compartmentalization. The proposed approach is based on the complementary diagnostic capabilities of the applied geochemical methods. Biomarkers provide information on the origin of organic matter, depositional environment, and thermal maturity of the source rocks, whereas oil fingerprinting is sensitive to hydrocarbon migration processes and the degree of hydrodynamic connectivity between reservoirs. The results indicate that the investigated oils are predominantly derived from terrigenous organic matter of lacustrine origin. The Akshabulak group is characterized by genetic homogeneity of oils despite pronounced reservoir compartmentalization, whereas the Nuraly field contains at least two genetically distinct oil populations and hydrocarbon mixing zones. Regional hydrocarbon migration was reconstructed from southeast to northwest. Paleochannel sandstones were identified as high-permeability migration conduits, while tectonic faults and facies heterogeneity were recognized as the principal controls on reservoir hydrodynamic isolation. The results demonstrate that integrating biomarker analysis with oil fingerprinting provides an effective tool for distinguishing between genetic and reservoir-related controls on oil compositional variability, evaluating reservoir compartmentalization, and improving the reliability of geological and reservoir models in structurally complex petroleum systems. Full article

Immunonutrition continues to generate heterogeneous and often contradictory clinical outcomes, suggesting that nutrients do not exert fixed immunological effects but interact with the biological context in which they operate. Sarcopenic obesity (SO) represents a paradigmatic clinical model of this complexity, where chronic low-grade inflammation, mitochondrial dysfunction, anabolic resistance, metabolic inflexibility, and microbiota remodeling converge to compromise the adaptive capacity of integrated immunometabolic networks. We propose that this condition may be interpreted as a state of impaired immunometabolic plasticity, which may help explain the context-dependent variability of nutritional responses. Within this perspective, micronutrients are viewed not simply as cofactors supporting immune competence but as dynamic regulators of interconnected immunometabolic pathways. Particular attention is devoted to vitamin D and resveratrol, presented as complementary regulators of immunometabolic plasticity. Within the proposed framework, vitamin D may contribute to immunometabolic competence, whereas resveratrol may act as a broader signaling modulator through the SIRT1/AMPK–PGC-1α axis, influencing mitochondrial function, inflammatory tone, metabolic flexibility, and epigenetic adaptation. Beyond isolated compounds, bioactive-rich food matrices, exemplified by Opuntia ficus-indica, are discussed as examples of systems-level modulators capable of coordinating inflammatory, metabolic, redox, and microbiota-dependent biological circuitry. This review introduces immunometabolic plasticity as a conceptual framework linking nutritional signals to the coordinated regulation of immune and metabolic adaptation across diverse biological contexts. Finally, we discuss how biomarker-guided phenotyping, multi-omics integration, and context-aware nutritional interventions may provide a foundation for precision immunonutrition, shifting the field from generalized supplementation strategies toward restoration of adaptive immunometabolic resilience. Full article

Objective: This study aimed to systematically evaluate the efficacy and safety of cadonilimab in the treatment of digestive system neoplasms. Methods: PubMed, Embase, Cochrane Library, Web of Science, Wiley Online Library, CNKI, Wanfang Data, CBM, and VIP databases were systematically searched to collect randomized controlled trials, non-randomized studies, and real-world studies investigating cadonilimab for the treatment of digestive system neoplasms. The initial search spanned from the inception of the databases to 4 February 2026, with an update search conducted in June 2026. Meta-analysis was performed using Stata 16.0 to systematically assess efficacy and safety outcomes. Results: A total of 10 studies, predominantly single-arm or retrospective, were included in this meta-analysis. Regarding the primary outcomes, because of the clinical heterogeneity across tumor types, pooled estimates should be interpreted as a summary across diverse diseases rather than as a single-tumor estimate. The overall pooled disease control rate (DCR) was 84% (95% CI = 76–91%), with tumor-specific DCRs of 87% in ESCC, 79% in HCC, 88% in G/GEJ adenocarcinoma, and 86% in PDAC. The pooled incidence of any-grade treatment-related adverse events (TRAEs) was 98% (95% CI = 95–100%), and the pooled incidence of any-grade immune-related adverse events (irAEs) was 32% (95% CI = 18–47%). For secondary outcomes: The partial response (PR) rate was 33% (95% CI = 21–46%), and the progressive disease (PD) rate was 11% (95% CI = 5–18%). The pooled incidence of grade ≥ 3 TRAEs was 52% (95% CI = 38–66%), and the pooled incidence of grade ≥ 3 irAEs was 11% (95% CI = 8–16%). However, incomplete reporting of low-frequency AEs across studies likely underestimates the pooled irAE rate. Conclusions: This study suggests that cadonilimab has the potential for antitumor activity in digestive system neoplasms. The observed safety profile was consistent with the known toxicities of immunotherapy, with notably high rates of TRAEs warranting vigilant monitoring. These findings should be considered hypothesis-generating and are insufficient to establish clinical benefit. Full article

Background: Mobile health interventions offer a potential solution to adherence challenges, yet evidence regarding their collective efficacy in schizophrenia spectrum disorders has not been formally synthesized. This systematic review evaluates the impact of mobile health (mHealth) interventions on medication adherence as a primary outcome and on daily functioning and psychotic symptoms as secondary outcomes in individuals with schizophrenia spectrum disorders. Methods: Using the Population, Intervention, Comparison, Outcome (PICO) framework, a systematic search was conducted across multiple databases to identify relevant randomized controlled trials (RCTs) evaluating mHealth strategies for medication adherence in adults with schizophrenia spectrum disorders. The PubMed, CINAHL, PsycINFO, EMBASE, and JBI databases were searched from inception until 24 February 2026, using combinations of search terms such as “Schizo” OR “Psychos” AND “mHealth” OR “Digital Health” AND “Medication Adherence”. Data extraction was conducted using a standardized data extraction table and narratively synthesized. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring structured and comprehensive reporting of the findings. Results: Fourteen randomized controlled trials (RCTs) with 1717 participants were included in this review. Four studies evaluated text messaging interventions, four employed phone call interventions, three used electronic medication monitoring systems, and three used mobile applications. Nine of the fourteen studies reported statistically significant improvements in medication adherence. For secondary outcomes, the results were highly inconsistent: only three studies demonstrated significant reductions in psychotic symptoms, and none showed benefits for daily functioning. While various theoretical frameworks, such as the Health Belief Model and Cognitive Behavioral Therapy and intervention modalities, were utilized, the overall evidence was limited by high clinical heterogeneity and a lack of robust long-term data. Conclusions: mHealth interventions, particularly text messaging and mobile applications, demonstrate clear potential to improve medication adherence in individuals with schizophrenia spectrum disorders. Given the high heterogeneity and lack of long-term evidence, future research should prioritize standardized outcome measurements, rigorous designs, and extended follow-up periods to confirm clinical utility. Full article

The semiconductor industry faces converging pressures from energy-constrained edge electronics and energy-bottlenecked high-performance computing, motivating heterogeneous monolithic three-dimensional (M3D) integration as a system-level response. M3D imposes a strict back-end-of-line (BEOL) thermal budget on upper-tier devices, restricting the channel materials and contact processes available and degrading conventional thin-film transistor performance. The source-gated transistor (SGT), in which drain saturation is set by gate-modulated injection across an engineered source barrier rather than by drain-side channel pinch-off, provides a device-level response: low saturation voltage, high output impedance, large intrinsic gain, and tolerance to channel-length variation, all achieved with moderate-mobility and nonideal-contact channel materials. This review organizes reported SGTs by source-barrier architecture and channel-material platform, develops a spatial characterization framework that complements electrical measurements for unambiguous identification of source-controlled operation, and surveys applications across standalone edge electronics and BEOL-compatible upper tiers in M3D stacks. Integrating non-volatile memory mechanisms into the source barrier further extends SGTs into a compute-in-memory and neuromorphic upper-tier role in which the voltage-invariant saturation current itself functions as a programmable, read-bias-robust state variable. Together, these considerations position SGTs as a flexible architectural primitive for heterogeneous M3D platforms that address the energy demands of both edge and high-performance computing. Full article

Short-term electric load forecasting is essential to the secure and stable operation and economic dispatch of power systems, and its accuracy directly affects grid dispatch decisions and operational efficiency. To address the inadequate modeling of heterogeneity among historical exogenous variables, the underutilization of known future calendar information, and the difficulty of correcting local biases in multi-step forecasts, this paper proposes KCD-GEIRTimeXer, a short-term electric load forecasting method built upon TimeXer. Before exogenous variable embedding, a Grouped Exogenous Importance Recalibration (GEIR) module is introduced. Historical exogenous variables are first grouped a priori according to their sources and physical meanings. Importance scores are then computed at the feature, variable-group, and time-step levels, and the corresponding exogenous variable weights are obtained through a bounded residual gating mechanism. At the prediction stage, a Known Calendar–Causal Dual Correction (KCD) module is further incorporated. The module refines the initial forecasts using known future calendar variables and causal statistical features derived exclusively from historical load observations, thereby mitigating local biases in multi-step forecasting. Experiments on the publicly available Panama electricity load dataset demonstrate that the proposed model outperforms all baseline models. Averaged over three random seeds, KCD-GEIRTimeXer reduces MSE, RMSE, MAE, and MAPE by 26.25%, 14.12%, 13.10%, and 12.91%, respectively, compared with TimeXer. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article