Search Results (1,943)

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Osteosarcoma (OS) is the most common primary bone cancer in adolescents and young adults. Despite tremendous preclinical and clinical efforts to advance therapy for OS, the standard of care, consisting of surgical resection and pre- and postoperative chemotherapy, has remained unchanged for over 40 years. Growing molecular understanding of OS highlights tumor heterogeneity as a major obstacle to therapeutic advances. In this narrative review, we comprehensively discuss current evidence of OS heterogeneity and strategies to overcome the barrier. Evidence shows that OS heterogeneity is multifactorial: it retains complex and dynamic somatic genomics, including genomic instability, alterations in tumor suppressors, and amplification/overexpression of oncogenes such as MYC. The tumor is associated with various germline vulnerabilities. OS’s tumor microenvironment has intense cellular and spatial diversity, which significantly shapes its heterogeneity. The effects of lineage plasticity, as well as epigenetic and metabolomic mechanisms, on OS heterogeneity are under study. To overcome this extreme heterogeneity, the therapeutic strategies for OS must be comprehensive and diversified. While surgical resection remains a mainstay of treatment, efforts to identify actionable biomarkers that guide risk stratification and therapy are ongoing. Diverse preclinical models offer insights into OS biology and novel therapeutics. To enhance combinational therapy for OS, various agents, including multi-targeted receptor tyrosine kinase inhibitors, immunotherapies, and epigenetic and metabolic modifiers, are being investigated. Distinctive efforts are continuing to establish maintenance therapy for OS. In summary, elucidating the complex drivers of OS heterogeneity, together with the development of multifaceted strategies to address them, is critical to accelerating therapeutic progress in OS. Full article

Cirsium arvense (L.) Scop is a perennial plant of the family Asteraceae that is mainly distributed in the temperate regions of the Northern Hemisphere. Despite being widely recognized as an invasive weed in agriculture, most of the scientific evidence shows its significant phytochemical and pharmacological importance. In the present review article, a comprehensive summary of the available literature on C. arvense’s botanical properties, phytochemical composition, biological activities, standardization potential, and future therapeutic prospects has been carefully provided. This plant has been used traditionally for the treatment of inflammation, infections, bleeding disorders, and liver-related disorders. Phytochemical investigations showed the presence of many bioactive compounds such as flavonoids, phenolic acids, triterpenes, sterols, tannins, glycosides, and volatile compounds. Among the reported biological activities, antioxidants and antimicrobial properties are the most studied activities. In addition, anticancer, antidiabetic, neuroprotective, anti-inflammatory, and antiproliferative activities have also been investigated. The environmental adaptability, rapid growth, and extensive root system of C. arvense highlight its potential for development as a sustainable medicinal and industrial crop. However, there are critical research gaps present in phytochemical standardization, toxicity assessment, pharmacokinetics, and clinical validation, warranting further comprehensive studies. Full article

Mineral waters represent unique limnological ecosystems with stable physicochemical conditions and specialised microbial communities adapted to extreme environments. Bulgarian mineral waters remain comparatively underexplored despite their considerable ecological and biotechnological significance. These studies present a systematic narrative review of microbial diversity, ecological functions, and biotechnological potential of microbial communities from Bulgarian mineral springs. A total of 233 scientific sources published between 1990 and 2026 were analysed, of which 33 focused on Bulgarian sites. Data were retrieved from major scientific databases, regional reports and grey literature. Due to strong methodological heterogeneity, a qualitative synthesis was conducted, supported by bibliometric summaries of research focus and environmental context. The available evidence demonstrates that microbial communities in Bulgarian mineral waters include diverse bacteria, archaea, cyanobacteria, and microalgae that adapt to broad thermal and geochemical gradients. These microorganisms actively participate in element cycles, form complex biofilms, and show numerous physiological adaptations to oligotrophic and extreme temperature conditions. Bulgarian systems broadly reflect global microbial patterns but exhibit additional variability linked to contrasting hydrogeological settings. Many taxa produce thermostable enzymes, antimicrobial compounds, and exopolysaccharides with significant biotechnological potential. The review identifies significant research gaps and emphasises the importance of integrated multi-omics approaches for future exploration of Bulgarian mineral water ecosystems. Full article

Early diagnosis of post-traumatic epilepsy (PTE) is crucial for timely intervention. However, it is hampered by the lack of reliable biomarkers. In this review, we provide a comprehensive summary of current advances in PTE biomarker research, drawing primarily on evidence from human cohort studies, with selective support from experimental animal models where mechanistic insights are required. We cover (i) neuroimaging, including CT, MRI, and EEG/qEEG, which reveal structural and functional alterations associated with epileptogenesis; (ii) molecular biomarkers, including RNAs, proteins, metabolites, and extracellular vesicle (EV)-derived molecules that reflect neuroinflammation, blood–brain barrier (BBB) dysfunction, neuronal injury, and synaptic remodeling; and (iii) artificial intelligence (AI)-assisted approaches, which integrate multimodal datasets to identify complex predictive patterns. While individual modalities offer valuable but incomplete prognostic information, AI-driven analytics hold the greatest promise for early predictive power by combining multimodal data. Future progress will depend on the integration of high-resolution imaging, multi-omics profiling, and rigorous validation to deliver clinically actionable biomarker panels and ultimately reduce the burden of PTE. Full article

Background: Early management of adult burn injuries in resource-constrained environments—such as battlefields and primary care facilities—remains hindered by the absence of standardized, evidence-based protocols. This study aimed to systematically synthesize existing evidence and develop an integrated framework of actionable recommendations to optimize prehospital and early emergency care. Methods: A comprehensive evidence synthesis was conducted across 14 international and domestic bibliographic databases and authoritative repositories. Eligible sources included clinical practice guidelines, expert consensus statements, evidence summaries, and systematic reviews. Literature quality was appraised using validated instruments, and best-practice recommendations were extracted and thematically synthesized across the continuum of early burn care. Results: Fifty-nine high-quality studies yielded 77 recommendations across 13 domains, spanning from scene safety and burn process cessation through airway, breathing, and circulatory management to wound care, infection control, and transfer preparation. An integrated, context-adaptive framework was established to guide resource-calibrated interventions rather than rigid protocol adherence. Conclusions: These findings provide tiered guidance for frontline healthcare providers and inform the development of emergency care standards in resource-limited settings. Future research should prioritize field validation and contextual implementation to address barriers to evidence translation and enhance real-world applicability. Full article

Background/Objectives: Pelvic radiotherapy (RT) for mid–low rectal and anal cancers frequently causes acute and late vaginal toxicity, including dryness, irritation, and dyspareunia, with a substantial impact on quality of life. Evidence supporting targeted interventions for radiation-induced vaginal mucosal changes remains limited. This exploratory retrospective study evaluated the association between daily use of Colpofix^® Ovules and temporal changes in patient-reported vaginal symptoms and Vaginal Health Index (VHI) scores in women undergoing pelvic RT. Methods: Twenty women treated with pelvic RT or chemoradiotherapy between 2024 and 2025 were included. Vaginal symptoms were assessed using a Numerical Rating Scale (NRS 0–10), and mucosal status was evaluated using the VHI (5–25). Assessments were performed at baseline (T0), end of RT (T1), 3 months (T2), and 6 months (T3). Due to the retrospective nature of the dataset, only aggregated summary values were available; analyses were therefore descriptive and aimed at characterizing temporal trends. Results: A clear and progressive reduction in vaginal dryness, irritation, reduced lubrication, and dyspareunia was observed from T0 to T3, with improvements already evident at T1 and further consolidation at T2–T3. VHI scores increased from a mean of 10.8 at baseline to 21.0 at 6 months, reflecting a consistent trend toward mucosal recovery across all domains. In the anal cancer subgroup, the trend toward improvement in dysuria did not meet conventional thresholds for statistical significance (p = 0.073). At T3, 90% of patients reported perceived benefit (55% marked, 35% mild). No adverse events attributable to Colpofix^® were documented. Conclusions: In this small retrospective cohort, daily use of Colpofix^® Ovules was associated with favorable temporal trends in both vaginal symptoms and VHI scores up to 6 months after pelvic RT. These exploratory findings support further prospective controlled studies to better define the potential role of Colpofix^® in managing vaginal mucosal changes during pelvic radiotherapy. Full article

This paper presents a hybrid model that combines linguistic data summarization techniques, algorithms for constructing probabilistic trees, and various generative artificial intelligence models for learning and generating linguistic summaries to aid decision-making. The proposal is validated using methodological triangulation techniques that demonstrate high consistency in the knowledge discovered. The proposal also compares different generative artificial intelligence models; among the evaluated models, Gemini achieved the best performance. However, it is evident that, in certain contexts and tasks, small language models can be effective, yielding results comparable to large language models (LLMs) at a lower computational cost. This study applies the algorithms in a case study analyzing oceanographic data from Northern Chile. In the validation scenario, the combination of linguistic data summarization methods with unsupervised learning techniques effectively models human tolerance for imprecision when processing complex data and generated linguistic summaries easily interpretable by human decision-makers with high levels of confidence. Studies of energy capacities in the studied region and their behavior in both winter and summer are presented. Full article

Large language models (LLMs) are advancing intelligent writing systems from local text continuation and language polishing toward long-form structured text generation. However, directly generating full-length academic paper drafts remains challenging due to unclear research objectives, unstable discourse structures, insufficient long-text coherence, and the lack of explicit quality control mechanisms. To address this long-form structured generation task, we propose MetricDraft, a metric-driven framework for academic paper draft generation. The framework organizes the drafting process as a closed-loop pipeline comprising research ideation clarification, structural anchoring, section-by-section generation, quality assessment, and feedback-driven revision. Its key components include adversarial research ideation clarification, staged structural anchoring, the PRISM structured metric system, progressive context injection with section-type-aware guided generation (PCI+STAGG), and a metric-feedback-driven generation–evaluation co-optimization mechanism. Experimental results demonstrate that MetricDraft achieves higher composite quality scores than one-shot generation, summary-based context passing, and context-accumulation-only baselines, improving MQS over Base1, Base2, and Base3 by +5.5, +7.9, and +7.0 points, respectively, with paired tests reaching statistical significance. To examine whether this advantage is tied to a single LLM backend, we further conduct a cross-model validation on all 15 tasks using Qwen3.7-Max in addition to the original DeepSeek-V4-Pro setting. MetricDraft remains the best-performing strategy under both models. To address citation reliability, an additional citation verification-and-retrieval-based replacement (CVRR) experiment reduces the fabricated citation rate of DeepSeek MetricDraft drafts from 56.0% to 15.0%. Furthermore, PRISM exhibits moderate-to-high positive correlations with expert ratings, providing preliminary evidence that it can serve as an auxiliary evaluation reference for draft quality diagnosis and iterative revision. This work reformulates academic writing as an adjustable, assessable, and iteratively optimizable long-form structured text generation problem, offering methodological insights for human–AI collaborative writing and intelligent text generation system design. Full article

Protein–protein interactions (PPIs) represent one of the major factors determining structure, function and quality in food products, especially in the case of industrial processing. Within complex food matrices, the structural and physical behavior of food components is controlled by PPIs that determine aggregation behavior, network formation, phase stability, and structural integrity and are thus directly related to the stability of the final product and how well a product may perform during a process. Recent developments in analytical techniques have facilitated the elucidation of PPIs and their application in activity-induced structural changes, in particular during thermal, non-thermal, enzymatic, and mechanical processes. In lieu of providing an exhaustive summary, this review synthesizes research evidence and findings related to measuring PPIs from main food systems, namely dairy, meat, cereal and plant-based products. The impact of different processing methods on PPIs and related quality characteristics including structure, stability and functional activity is critically assessed. Knowledge gaps and methodological limitations (in particular concerning laboratory scale industrial processes) are highlighted. By combining mechanistic considerations with practical performance considerations, this review allows us to rationalize the improvement of food processing strategies and to develop protein-based foods with better quality and performance stability. Full article

Physalis alkekengi L. var. franchetii (Mast.) Makino (P. alkekengi) is a well-known traditional Chinese medicine (TCM) with dual edible and medicinal values. Its polysaccharides (PAPs), as core bioactive constituents, have drawn growing research interest amid advances in natural product studies, whereas systematic summaries of existing evidence remain insufficient. This paper collects and analyzes recent progress regarding PAPs extraction, purification, structural characterization, biological activities and targeted applications in diverse food matrices. Current bottlenecks restricting PAPs development are also discussed from multiple research perspectives. Major research outcomes reveal that diverse extraction and purification techniques determine PAPs yield and structural heterogeneity, and characterized structural features are closely associated with their antioxidant, anti-inflammatory, immunomodulatory and other bioactivities. Inadequate mechanistic exploration, incomplete toxicological evaluation and immature industrial extraction systems are critical obstacles limiting further translational application of PAPs. Targeted future research directions are accordingly proposed to address these gaps. This work provides a comprehensive reference and theoretical support for deeper investigation, development and utilization of PAPs as functional food ingredients or herbal bioactive agents. Full article

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling system using monthly data for 2003–2024 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) meteorological covariates, climate controls, and administrative aggregation. Using a rolling-window directed network framework based on Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery, we inferred lagged conditional-dependence networks from covariate-adjusted PM2.5 anomalies and summarized their structure at national and first-order administrative levels. The inferred network structure varies over time but retains measurable continuity across rolling windows. At the country level, cross-border links consistently account for a large share of the directed structure, indicating that PM2.5 variability within the study domain is strongly shaped by transboundary coupling rather than by country-contained dynamics alone. A recurrent backbone of country-level directional coupling corridors emerges, including persistent links among China, Indonesia, Myanmar, and Thailand. At the first administrative level, stable gateways and receptor basins become more evident, especially the bidirectional coupling corridor between Yunnan Province, China, and Shan State, Myanmar, which appears throughout the full window sequence. These results show that subnational structure can reveal transport-relevant coupling patterns that national summaries may conceal. The framework provides an interpretable basis for corridor-oriented monitoring and regime-aware early warning, while the inferred links should be interpreted as directional statistical dependence rather than direct emissions attribution or resolved physical transport pathways. Full article

This study aims to conduct a comparative analysis of the quality and flavor of meat from four categories of Mongolian horses (Wushen, Baicha, Barhu, and Ujimqin). Physicochemical indicators, electronic nose, electronic tongue, and lipidomics were used to characterize meat quality and flavor and to screen for differential markers. Results showed that Wushen Horses had the highest pH_45min, serine, glutamic acid, total free amino acids (∑FAA), total non-essential amino acids (∑NEAA), total amino acids (∑TAA), NEAA/TAA, W2S sensor response, umami and richness values, and had the lowest cooking loss, EAA/TAA, EAA/NEAA, sourness, bitterness and aftertaste B values (p < 0.01). In contrast, Barhu Horses had the highest b*_45min, C20:2 and saltiness values, and had the lowest W5S, W1S and W2W sensor responses (p < 0.01). Lipidomics identified 163 differential lipids (DELs) as potential markers, including LPC (18:2/0:0) and PC (16:0_16:0). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed DELs were significantly enriched in glycerolipid, linoleic acid, arachidonic acid and α-linolenic acid metabolism pathways. Correlation analysis indicated 23 DELs (e.g., carnitine C20:4) correlated positively with umami, W2S and richness, but negatively with shear force and cooking loss. In summary, our data show that among the four categories of Mongolian horses, Wushen Horses exhibited the best meat quality and flavor, while Barhu Horses showed the poorest. The differences in meat quality and flavor were closely associated with changes in lipid composition. This study provides direct molecular evidence from lipids for the variation in meat quality among Mongolian horses. Full article

In large-scale state-owned enterprise audit and compliance tasks, ensuring that similar violations receive consistent disciplinary decisions is essential for procedural fairness and institutional credibility. However, existing retrieval methods face three major challenges: lexical matching methods fail to recognize semantically equivalent violation descriptions, general-purpose semantic encoders lack knowledge of inspection-specific terminology and regulatory distinctions, and retrieved precedents are often not directly transformed into actionable disciplinary references. To address these problems, this paper proposes InspectCL, a domain-enhanced contrastive learning and Retrieval-Augmented Generation framework for similar case retrieval, validated on audit data from a provincial power grid company. First, to provide task-specific supervision that is unavailable in existing benchmarks, we construct InspectCase, a de-identified dataset of 4200 audit and compliance cases across 12 violation categories, with expert-validated positive pairs and hard negative pairs. Second, to overcome the weak domain awareness of generic encoders, we design a domain-enhanced contrastive learning model. Specifically, terminology-masking augmentation improves robustness to specialized inspection expressions, regulatory semantic injection incorporates disciplinary rules to distinguish factually similar but legally different cases, and hierarchical contrastive optimization strengthens both case-level similarity learning and category-level boundary separation. Third, to convert retrieved precedents into practical decision support, the Top-K similar cases are used as evidence for a large language model to generate structured disciplinary recommendation summaries, including violation classification, penalty references, applicable regulations, and rectification measures. Experimental results on InspectCase show that InspectCL substantially outperforms BM25, BERT-base, SimCSE, and Legal-BERT baselines, achieving 56.9% ± 0.7% Recall@5 and an 87.6% ± 0.4% Penalty Consistency Score (PCS). These results demonstrate that the proposed problem-driven modules jointly improve semantic retrieval accuracy and disciplinary decision consistency, offering a practical reference for similar power-grid audit scenarios, with broader applicability to be validated in future cross-domain studies. Full article

Background: Interpretation of pesticide residues in fruits requires tight integration of surveillance evidence, analytical capability, regulatory context, and mitigation data. Methods: This critical integrative review synthesises analytical chemistry, cumulative risk assessment (CRA), regulatory divergence, and mitigation evidence, strengthened by quantitative monitoring summaries and auditable regulatory examples. Routine enforcement continues to rely on validated QuEChERS extraction coupled with targeted LC-MS/MS and GC-MS/MS. High-resolution mass spectrometry (HRMS) adds unique value for metabolites, transformation products (TPs), and incident response, but its routine enforcement role remains constrained by confirmation logic and harmonised validation. Results: Monitoring shows that exposure is typically multi-residue rather than single-compound; the key interpretive challenge therefore shifts toward CRA prioritisation, sensitive-subpopulation assumptions, and transparent distinction between compliance signals and toxicological inference. We provide (i) headline compliance metrics from EU and US programmes, (ii) surveillance-derived high-frequency residue patterns and co-occurrence motifs to guide CRA prioritisation, (iii) an illustrative, traceable comparison of EU/US/Codex MRL divergence for emblematic citrus residues with EU evidence extracts and US/Codex traceability records, and (iv) mitigation evidence statements standardised by study type and transformation-product reporting. Conclusions: Pesticide residues in fruits should be interpreted through a risk-based framework that distinguishes compliance findings from toxicological concern, prioritises relevant multi-residue drivers, and evaluates mitigation according to both residue reduction and transformation-product uncertainty. Full article