Search Results (5,641)

Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle loss, affecting 30–60% of patients with advanced renal cell carcinoma (RCC). It significantly impacts treatment tolerance, quality of life, and prognosis, yet its diagnosis and management remain challenging due to fragmented RCC-specific evidence, particularly in the era of immune checkpoint inhibitor (ICI)-based therapy. The pathogenesis involves persistent systemic inflammation, metabolic reprogramming, and tumor–host interactions. The IL-6/STAT3 and TNF-α/NF-κB pathways are central to muscle catabolism, while tumor-derived mediators such as GDF15 and PTHrP, along with mitochondrial dysfunction, further drive cachexia progression. For prognostic assessment, CT-derived skeletal muscle mass evaluation combined with systemic inflammatory and nutritional biomarkers—including neutrophil-to-lymphocyte ratio (NLR), modified Glasgow Prognostic Score (mGPS), prognostic nutritional index (PNI), and cachexia index (CXI)—has improved risk stratification in advanced RCC. Preclinical and emerging clinical data suggest that targeted therapies may partially attenuate cachexia by modulating inflammatory signaling, while multimodal interventions integrating nutritional support and exercise rehabilitation remain the cornerstone of management. Novel strategies, such as inhibition of the GDF15/GFRAL axis, are under active investigation. Future research should prioritize identification of early biomarkers, standardization of cachexia assessment, and prospective evaluation of cachexia-directed interventions in the immunotherapy era. Integrating cachexia assessment into routine practice may ultimately enable personalized treatment and improve long-term outcomes for patients with advanced RCC. Full article

Background/Objectives: Artificial intelligence (AI) is transforming prosthodontic procedures into data-driven support systems. This scoping review systematically maps the literature using an evidence maturity framework, distinguishing it from recent applications-focused compilations by Aljulayfi, Schwendicke, and others. Methods: A comprehensive search of PubMed, Scopus, and Web of Science (January 2023–March 2026) was conducted in accordance with PRISMA-ScR guidelines, supplemented by targeted manual citation tracking. Fifty studies were included in the review. Results: The evidence base comprises approximately half primary research (computational, in vitro, clinical) and half review articles. Foundational diagnostic CNNs report high technical performance metrics (i.e., sensitivity 98.67%, precision 78.12%), while generative CAD/CAM approaches reduce design time by up to 52% in vitro. However, the evidence synthesis reveals a significant gap between algorithmic efficacy and clinical utility. Most applications currently reside at the “technical validation” stage, while emerging domains such as large language models remain largely experimental. Conclusions: Impressive computational metrics do not automatically equate to patient-centered clinical benefits. Current AI systems must be regarded as adjunctive decision-support instruments rather than autonomous tools. Future research must prioritize prospective multicenter validation, implementation science, explainability, and robust regulatory oversight. Full article

The increasing exposure of informal urban settlements to climate-related hazards highlights the need for reliable tools to assess community resilience and support adaptation planning. This study developed and validated the Climate Resilience Index for Housing and Settlements (IRECLIVA), a multidimensional framework designed to evaluate climate resilience in vulnerable urban communities. The methodology combines objective expert-based assessments with community perception data organized into five dimensions: housing and infrastructure, natural environment, community and governance, economy and insurance, and health and well-being. The index was applied in nine informal settlements in Greater Santo Domingo, Dominican Republic, using field checklists, household surveys (n = 198), and statistical validation procedures, including Cronbach’s alpha and Cohen’s kappa coefficients. The results demonstrated acceptable to excellent internal consistency across dimensions and substantial to almost perfect inter-rater agreement among evaluators. Resilience assessment showed that four settlements exhibited low resilience and five medium resilience, with the most critical deficiencies related to housing quality, infrastructure conditions, environmental exposure, and limited adaptive capacities. These findings indicate that IRECLIVA provides a reliable and practical framework for identifying resilience gaps, prioritizing adaptation measures, and supporting evidence-based urban planning. Further applications in different geographic and socio-economic contexts are recommended to assess the broader transferability and scalability of the index. Full article

Human Digital Twins (HDTs) extend traditional Digital Twin (DT) concepts by modeling both humans and hospital processes to support smarter and more human-centered healthcare. By integrating Industry 4.0 (I4.0) technologies with the human-centric principles of Industry 5.0 (I5.0), HDTs offer new opportunities to improve hospital operations. This study presents a PRISMA-based systematic literature review to examine the role of HDTs in hospital operations. A total of 329 papers were identified through the initial search, and after the screening process, 22 studies were included for in-depth analysis. The review combines bibliometric analysis to examine publication trends, leading authors, contributing countries, and keyword co-occurrence with a content analysis to identify the main research themes. Three major themes emerged: (1) HDT architectures and data integration, (2) human-centric and governance aspects, including explainable artificial intelligence and privacy, and (3) operational and clinical outcomes, including patient flow, resource utilization, and staff support. Based on these findings, the study proposes a four-layer HDT framework for practical implementation in hospital operations. Although the reviewed studies indicate that HDTs have considerable potential to improve operational efficiency and strengthen human involvement, most existing research remains conceptual or simulation-based. Future research should therefore prioritize real-world implementation and validation while incorporating ethical, explainable, and sustainable design principles. Full article

The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from deterministic classifiers and applies a multi-dimensional combinatorial model to calculate the requirements for complete diagnostic coverage. Our analysis demonstrates that comprehensive diagnostic coverage requires between 50,000 and 150,000 distinct, task-specific classifiers under subspecialty-level clinical granularity; conservative aggregated estimates (4500–18,750 binary classifiers) do not reflect the multiplicative expansion introduced by subtype differentiation, severity staging, temporal variants, demographic stratification, and equipment variation, whereas currently cleared devices cover less than one percent of this clinical space. More fundamentally, although population-trained models can generate conditional patient-specific risk estimates when predictors are informative and calibration is adequate, these statistical parameters optimized on population-scale data cannot provide the categorical certainty required for individual diagnostic decisions, which is a gap that clinical judgment must bridge. Because clinical AI tools are inherently statistical and perform reliably only on common, highly represented presentations while failing on rare, atypical cases rare in their training data, attempting to automate routine tasks leaves human clinicians with only the most challenging diagnostics. Furthermore, selective automation of these low-complexity cases introduces severe occupational hazards, including cognitive surrender, diagnostic complacency, and rapid expertise atrophy. Rather than pursuing the computationally and logistically unfeasible goal of complete diagnostic classification, developers should prioritize predictive, prognostic trajectory modeling. This paradigm shift aligns the probabilistic nature of machine learning with clinical utility, reinforcing clinical judgment as the irreplaceable diagnostic integrator. Full article

Ensuring food safety requires the detection of trace-level contaminants such as pesticides, mycotoxins, and heavy metals. Analytical approaches for these analytes should feature high sensitivity, good selectivity, and compatibility with aqueous matrices; surface-enhanced Raman spectroscopy (SERS) satisfies these requirements. Addressing the absence of a unified comparative analytical framework, this critical review surveys recent SERS-enabled sensing strategies for food contaminants. Detection strategies differ substantially across the three contaminant classes: pesticides can be directly detected at ppb levels through substrate engineering and deep learning; mycotoxins rely on affinity-recognition elements to reach pg-mL-level sensitivity; and Raman-inactive heavy metals demand indirect readout via functional probes. Crucially, despite these divergent analytical routes, the field confronts three shared bottlenecks—spectral irreproducibility, severe matrix interference, and the lack of standardized protocols, all of which hinder regulatory adoption. Compared with near-infrared spectroscopy (NIR) and hyperspectral imaging (HSI), SERS delivers outstanding sensitivity for confirmatory trace-level analysis, while its limited throughput may be compensated by multispectral data fusion. Future advances should prioritize portable sensing hardware, explainable Artificial Intelligence (AI), and multiplexed detection to transfer laboratory-scale sensitivity toward practical field-deployable testing tools. Full article

Early detection of pine wilt disease (PWD) before visible crown discoloration is important for field inspection, confirmatory sampling, and timely management. However, because individual trees reach visible discoloration at different times, calendar-based analyses can blur the development of pre-visual spectral responses. Weekly UAV multispectral data from two Pinus massoniana forest sites were therefore aligned to the first visible crown discoloration of each tree. Crown-level band reflectance and vegetation indices (VIs) were examined from eight to one weeks before first visible discoloration, and healthy reference observations at each relative week were used to define 5th–95th percentile reference ranges for detection-rate (DR) analysis. Several VIs showed significant group-level differences from approximately seven weeks before first visible discoloration, whereas mean VI-based DR increased markedly to 0.52 at two weeks and 0.68 at one week before discoloration; single-band DRs were less stable. These results indicate a progressive pre-visual spectral response, with an exploratory early group-level indication emerging approximately seven weeks before discoloration and a stronger, more consistent spectral departure during the final two weeks. This onset-aligned framework provides a practical basis for scheduling repeated UAV surveillance and prioritizing field verification before obvious symptoms emerge. Full article

Torrential flows, a broad category of rapid hydrogeomorphic processes that in the Colombian Andes includes debris flows, mudflows, and hyperconcentrated flows, pose a major hazard in tropical mountain regions. This study used two complementary binary classification models to examine geomorphometric conditioning and antecedent rainfall triggering of torrential flow occurrence. A 12.5 m ALOS PALSAR DEM and 42 years of daily rainfall data (1981–2023) from IDEAM rain gauges and CHIRPS v2 were analyzed in a GIS-based regional framework. Antecedent rainfall variables were aggregated at watershed scale using zonal statistics. The conditioning dataset comprised 642 watersheds (321 with documented events and 321 controls). Gradient boosting ranked first in the preliminary grouped holdout comparison, whereas the uncalibrated random forest achieved the highest mean score under spatial leave-one-province-out validation and was selected as the final conditioning model (mean ROC-AUC = 0.747 ± 0.052). Basin scale and relief were the leading morphometric associations. In the rainfall trigger model, previous day IDEAM mean rainfall and previous day IDEAM maximum rainfall were the two leading permutation importance predictors, followed by monthly CHIRPS rainfall; the 90-day IDEAM maximum accumulation ranked fourth. This ordering indicates that immediate rainfall dominated the fitted model, while longer antecedent wetness retained a secondary contribution. The results support watershed prioritization and regional hazard assessment; because operational rainfall thresholds were not derived, they should not be treated as a ready-to-use early-warning model. Full article

Rabbit monoclonal antibodies (RabMAbs) are valuable for biomedical research and diagnostic applications because of their high affinity, specificity, and broad epitope recognition; here, we established an integrated phenotype-linked workflow for rapid RabMAb discovery using serum-derived polyclonal mouse IgG as a proof-of-concept model antigen. Three Big-Eared White rabbits were immunized in parallel, and the rabbit exhibiting the highest serum endpoint titer was selected for the complete downstream single-B-cell discovery workflow. Approximately 5 × 10⁵ activated B cells were subjected to polydisperse oblate dispersion system (POD)-based screening, yielding 13,266 antigen-positive POD events. Following recovery and 10× Genomics single-cell V(D)J sequencing, 4294 B-cell barcodes yielded valid/interpretable V(D)J data, of which 2099 contained at least one complete, productive, and translatable heavy-chain/light-chain pair, generating 2199 functional VH/VL pairing records. AbFinder™-assisted prioritization generated a computationally recommended pool of 158 candidates, and the five highest-ranked VH/VL pairs within this pool were selected for recombinant expression and validation. All five yielded antigen-reactive RabMAbs with an endpoint ELISA titer of 1:256,000 and BLI-derived apparent K_D values ranging from 4.19 × 10⁻¹⁰ to 9.79 × 10⁻⁹ M. The antibodies showed differential concentration-dependent reactivity toward mouse IgG1, IgG2a, IgG2b, and IgG3 preparations, weak reactivity toward human IgG, and clone-dependent reactivity toward rat IgG. The workflow from spleen collection to functional validation was completed within approximately three weeks. Because only five prioritized candidates from one selected responder rabbit were evaluated, the 5/5 validation outcome should not be interpreted as an overall platform hit rate or definitive validation of the prioritization algorithm. These findings support the feasibility of this workflow for research-grade and diagnostic antibody discovery, while broader evaluation will require larger candidate cohorts, independent biological validation, and additional antigen classes. Full article

Localized phytochemical formulations may provide complementary strategies for controlling mucosal colonization by Streptococcus agalactiae and Streptococcus pyogenes, but computational prioritization requires orthogonal biological validation. This study integrated molecular docking against the redox-sensing transcriptional repressor Rex of S. agalactiae and a protein tyrosine phosphatase target of S. pyogenes (SP-PTP) with in vitro broth microdilution, biofilm, target-bridging, and epithelial-tolerance experiments. Isoflavone showed the most favorable natural-compound interaction with Rex (ΔG = −8.3 kcal/mol), whereas secoisolariciresinol diglucoside (SDG) led the natural-ligand ranking for SP-PTP (ΔG = −5.2 kcal/mol). The complete formulation produced MIC50 values of 0.031% and 0.063% v/v against S. agalactiae and S. pyogenes, respectively; inhibited biofilm formation by 89.2% and 80.1%; and preserved >92% epithelial viability at 1× MIC. Among the three natural candidates with complete matched broth data, target-normalized docking and composite MIC50/MIC90/MBC50 ranks were perfectly concordant in both species; pooled species-stratified Spearman analysis yielded ρ = 1.000 (exact p = 0.0556; n = 6). In contrast, the extended panel including antibacterial comparators showed negligible concordance (ρ = 0.081; p = 0.7826; n = 14). Principal component analysis assigned 74.8% of variance to an in vitro potency axis and 24.6% to a largely orthogonal docking axis. Thus, the experiments confirmed the target-specific ordering of the natural candidates but did not support extrapolation of docking rank across mechanistically heterogeneous antibacterial classes. Intracellular target inhibition, genetic causality, and component synergy remain unestablished. Full article

Informal video platforms support self-directed learning, yet the determinants of learners’ continuance intention to use YouTube for informal learning remain insufficiently integrated. This study addressed two questions: how the hypothesized structural relationships in an extended expectation–confirmation model explain Thai learners’ continuance intention and which determinants are necessary for high continuance intention. A sequential exploratory mixed-methods design was employed. Fuzzy e-Delphi with 23 experts retained 33 measurement items; survey data from 797 Thai learners were analyzed using covariance-based structural equation modeling, bootstrapped specific indirect effects, and Necessary Condition Analysis (NCA). The model explained 74% of the variance in continuance intention. Satisfaction was the strongest direct predictor (β = 0.559), followed by perceived enjoyment and trust. All 12 hypothesized structural relationships and eight indirect effects were statistically significant, revealing interconnected roles for confirmation, content quality, subjective norm, perceived enjoyment, perceived usefulness, satisfaction, and trust. NCA identified satisfaction and content quality as the principal necessary conditions, with smaller supporting roles for perceived usefulness and perceived enjoyment. Theoretically, the findings extend the expectation–confirmation model to voluntary informal video learning and distinguish contributing relationships from necessary constraints. Practically, they support prioritizing high-quality content and satisfactory learning experiences while strengthening perceived usefulness and perceived enjoyment. Full article

Animal-processing by-products contain collagen, myofibrillar proteins, blood proteins, whey proteins, and egg proteins that can be converted into peptide-rich food ingredients. Within a single application-oriented framework, this review integrates source heterogeneity, process control, peptide-profile characterization, tiered functional evidence, food-matrix performance, and regulatory substantiation. Evidence is evaluated for antioxidant, ACE-inhibitory, antimicrobial, DPP-IV-inhibitory, anti-inflammatory, mineral-binding, and taste-modulating functions, while distinguishing chemical assays, cell models, animal studies, human interventions, and tests in real-food matrices. Potential applications include functional foods, dietary supplements, natural preservation, flavor systems, texture modification, active packaging, and oral delivery. Translation remains limited by raw-material heterogeneity, batch variability, sensory defects, processing and gastrointestinal instability, uncertain bioavailability, incomplete safety assessment, and poorly defined regulatory claims. Future work should prioritize source traceability, peptide fingerprints, food-matrix validation, human exposure data, and scalable food-grade production. Compositionally defined peptide mixtures with reproducible functionality may be more practical than single highly purified sequences. Full article

Background/Objectives: Foodborne diseases account for the majority of infectious disease outbreaks in Yogyakarta, Indonesia, yet remain underexplored epidemiologically. This systematic review aimed to characterize the epidemiological patterns and risk factors of foodborne disease outbreaks in this region between 2006 and 2024. Methods: This systematic review searched electronic databases and grey literature published between 1 January 2006 and 31 December 2024. Eligible studies were reported in English or Indonesian and used descriptive or environmental observational designs. Methodological quality was assessed using a modified Joanna Briggs Institute (JBI) checklist. Searching, screening, and data coding were performed independently by the reviewers. The review followed PRISMA reporting guidelines and was registered with PROSPERO (CRD420251060794). Results: Of 841 studies screened, 99 met the inclusion criteria. The most frequently identified pathogens were Bacillus cereus (43.43%) and Staphylococcus aureus (34.34%), associated with 101 and 97 hospitalizations, respectively. Improper post-cooking storage (77.00%) was the leading contributing factor for Bacillus cereus outbreaks, whereas unsafe food processing practices (76.00%) predominated for Staphylococcus aureus outbreaks. Individual unregistered catering services accounted for the largest proportion of outbreaks. Conclusions: Public health strategies should prioritize strengthening food handlers’ capacity during food processing and post-cooking storage, particularly among individual informal catering services, to sharpen foodborne disease control efforts in Yogyakarta. Full article

The Alternative Lengthening of Telomeres (ALT) pathway drives replicative immortality in aggressive malignancies, particularly sarcomas and gliomas. Clinical ALT stratification has relied on screening for structural ATRX and DAXX mutations. However, this genotypic approach fails to capture the dynamic macro-reprogramming required to sustain ALT. Here, we established and validated a 28-gene transcriptomic signature that captures the ALT-associated transcriptomic phenotype of the ALT phenotype. Using multivariate Cox proportional hazards models and time-dependent ROC analyses, we demonstrate that this signature is a robust, independent predictor of poor overall survival in Sarcoma (SARC) and Lower Grade Glioma (LGG) cohorts, outperforming the prognostic value of traditional ATRX/DAXX mutational status. Genomic mapping revealed this transcriptional synchrony is structurally facilitated by non-random focal clustering on Chromosome 8. To deconstruct the machinery driving this lethal phenotype, we employed an integromic approach, synthesizing protein–protein and metabolic flux networks. Topological algorithms prioritized five indispensable hubs: TP53, ATM, ATR, PCNA, and UBE2I. Gene–metabolite profiling identified PCNA as a bottleneck funneling extreme deoxyribonucleotide (dNTP) demand to sustain break-induced telomeric recombination. To translate these vulnerabilities into actionable treatments, we mapped these hubs to a precision pharmacological network. We propose a multi-targeted strategy combining FDA-approved PARP inhibitors to exploit ATR-mediated synthetic lethality, alongside antimetabolites to induce nucleotide starvation. This study redefines ALT risk stratification and provides a data-driven framework to target and treat resistant ALT-positive tumors. Full article

Background/Objectives: Eating disorders are increasingly conceptualized as information-processing disorders characterized by attentional biases toward food-related stimuli. However, whether such biases are already detectable in subclinical, non-treatment-seeking populations remains unclear. This study examined whether subclinical eating pathology, assessed with the Eating Attitudes Test-26 (EAT-26), is associated with distinct patterns of visual attention toward high- and low-calorie food images in university students. Methods: In this cross-sectional observational study featuring an experimental eye-tracking task, 89 university students completed the EAT-26 and a visual task displaying paired high- and low-calorie food images alongside neutral controls. Oculomotor metrics included first fixation duration (FFD), fixation count (FC), and average fixation duration (AFD). Data were evaluated using EAT-26 threshold-based group stratification (lower-risk vs. risky eating behavior) and Spearman rank correlation analyses. Results: Global EAT-26 scores showed a significant correlation with a directional attentional bias index (rho = 0.29, p = 0.006). The Dieting subscale demonstrated no significant relationships with any oculomotor metrics. Conversely, Oral Control scores were significantly negatively correlated with the directional attentional bias index (rho = −0.27, p = 0.009) and positively tracked with low-calorie fixation counts (rho = 0.30, p = 0.004). Bulimia subscale scores showed a moderate positive correlation with the directional attentional bias index (rho = 0.26, p = 0.013), characterized by a significant decrease in low-calorie fixation counts (rho = −0.32, p = 0.002) and a strong increase in high-calorie fixation counts (rho = 0.36, p < 0.001) during sustained viewing. Conclusions: Subclinical eating pathology tendencies are associated with distinct implicit attentional profiles regarding food caloric density during sustained cognitive evaluation. Oral control drives visual prioritization of low-calorie stimuli to maintain inhibitory control, whereas bulimic tendencies reflect prolonged visual preoccupation with high-calorie reward cues. Eye-tracking represents a promising non-invasive research tool for exploring early cognitive correlates of eating-disorder risk. Full article