Search Results (1,429)

Background: Health-related behaviors often cluster during young adulthood, potentially increasing the risk of long-term adverse health outcomes. Understanding how lifestyle risk behaviors co-occur among university students is essential for developing targeted health promotion strategies. Objective: This study aimed to identify lifestyle risk profiles among university students based on the co-occurrence of smoking behavior, alcohol consumption, sedentary behavior, and body weight status. Methods: A cross-sectional study was conducted with 147 university students enrolled in a physical education and sport undergraduate program (mean age: 20.58 ± 2.94 years; 80.3% male). Physical activity and sedentary behavior were assessed using the International Physical Activity Questionnaire–Short Form (IPAQ-SF), while smoking and alcohol consumption were self-reported. Body mass index was used to classify weight status. Lifestyle risk profiles were identified using two-step cluster analysis based on regular smoking, alcohol consumption, sedentary behavior, and overweight/obesity. Differences in cluster distribution according to sex and federated athlete status were examined using chi-square tests. A two-step cluster analysis based on the Bayesian Information Criterion (BIC) and silhouette measure was used to identify lifestyle risk profiles. Results: Overall, 46.9% of participants had experimented with tobacco, 11.6% were current smokers, and 74.8% reported alcohol consumption. Participants accumulated an average of 3772.25 ± 1957.99 MET-min/week of physical activity. Three distinct lifestyle risk profiles were identified. Cluster 1 (46.9%), labeled the alcohol profile, was characterized by alcohol consumption without smoking and no prevalence of being overweight. Cluster 2 (20.4%), the multiple-risk profile, included participants who reported regular smoking, with nearly half presenting sedentary behavior and overweight/obesity. Cluster 3 (32.7%), the overweight profile, was characterized by overweight/obesity combined with alcohol consumption but no smoking. No significant differences were observed in the distribution of lifestyle profiles according to sex (p = 0.111) or federated athlete status (p = 0.087). Conclusions: Lifestyle risk behaviors cluster into distinct profiles among university students, with alcohol consumption appearing across multiple profiles and smoking concentrated in a specific high-risk group. These findings highlight the need for targeted health promotion strategies addressing multiple co-occurring behaviors within university populations. Full article

Background: Untargeted proteomics enables quantitative host cell protein (HCP) determination in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. Methods: A prospective total-error (TE) validation of label-free ddaPASEF proteomics was performed. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20–80 ng) and analyzed in four independent assays (198 injections), supporting one-way random-effects ANOVA with Welch–Satterthwaite adjustment. Peptide-level identification error was evaluated by dual entrapment. Results: Empirical false-discovery proportions were below 1% at q = 0.01. Weighted least-squares regression (R² = 0.993) confirmed stable proportional compression with 81–85% recovery. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision SD ranged from 0.69% to 3.81%. Both 95% β-expectation and 95/95 content tolerance intervals were contained within ±30% at all levels, defining a validated range of 20–80 ng. Abundance-stratified TE profiling revealed concentration-dependent calibration heterogeneity, with stratum-specific intervals within ±35% defining an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe v24.0, CCC = 0.998) and cross-platform acquisition (Astral, CCC = 0.980) remained within ±30% limits. Conclusions: This constitutes the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a transferable statistical framework for high-dimensional analytical methods. Full article

Background/Objectives: Malnutrition and undernutrition are critical health concerns associated with increased mortality and costs. Although these are distinct clinical concepts, they are often used interchangeably in clinical practice and are inconsistent with the diagnostic frameworks. This diagnostic ambiguity may obscure true patient profiles. This study aimed to clarify the real-world diagnostic patterns of malnutrition and undernutrition and identify associated drug prescription trends using a patient estimation database in Japan. Methods: We analyzed the AHI partners database of 2024. Patients were identified using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, code E46. Sex differences were analyzed and stratified according to age group: older adults (≥65 years) and younger adults (15–39 years). Odds ratios (ORs) were used to identify associated drugs. Results: Of 96,673,453 patients, 216,652 were diagnosed with malnutrition and 77,100 with undernutrition. In both categories, older adults accounted for more than half of the patients. Notably, distinct diagnostic labeling patterns were observed by sex. Malnutrition predominated in women (58.8%), whereas undernutrition was more prevalent in men (70.6%). This male predominance of undernutrition was reversed in younger adults, where women showed higher proportions in both categories. Prescription analysis identified 31 drugs frequently prescribed to the study population. Enteral elemental formulas had the highest OR (89.7). Some psychotropic drugs were frequently prescribed to women. Conclusions: Diagnostic patterns varied by sex and age, potentially reflecting distinct practices in diagnostic labeling. These findings highlight the need for standardized frameworks to ensure consistent assessments and effective nutritional interventions. Full article

Saskatoon berry (SB), a traditional food of Indigenous people, has been associated with cardiometabolic benefits in animal models; however, its effects on humans remain unclear. This study investigated the effects of dried SB consumption on cardiometabolic outcomes, gut microbiota, and short-chain fatty acids (SCFAs) profiles in healthy adults. In a 10-week, single-arm, and open-label trial, 20 healthy adults consumed 40 g/day of freeze-dried whole SB. Biochemical measures, physical exams, dietary records, participant feedback, and fecal samples were collected before and after the intervention. Gut microbiota composition and fecal SCFAs were profiled using 16S-rRNA sequencing and gas chromatography–mass spectrometry, respectively. SB intake significantly reduced fasting plasma glucose, total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-c), non-high-density lipoprotein-cholesterol (non-HDL-c), systolic blood pressure, and high-sensitivity C-reactive protein, while increasing dietary fiber intake. Fiber intake was negatively correlated with TC, LDL-c and non-HDL-c (p < 0.05). The relative abundance of fecal Prevotellaceae increased after SB consumption and was positively correlated with multiple fecal SCFAs (p < 0.05–0.0001), while being negatively associated with lipid profiles and blood pressure. No adverse cardiovascular, hepatic, or renal dysfunction were observed; however, the significant increase in sugar intake may pose a risk for elevated blood glucose. Therefore, limiting other high-sugar foods during SB supplementation may be advisable for individuals with glucose intolerance. Overall, SB intake improved glucose and lipid metabolism and lowered blood pressure and inflammatory markers in healthy adults. These cardiometabolic benefits may be mediated by fiber and anthocyanins in SB and through modulation of gut microbiota and SCFA production; however, further confirmation is needed in subsequent randomized controlled trials. Full article

This study presents a representation-centric evaluation of audio foundation models for fine-grained musical instrument analysis, focusing on cymbal classification. A confound-aware comparison of CLAP and MERT embeddings is conducted to examine how each latent space supports recoverability of acoustically and semantically relevant information. To support this analysis, the study introduces a representation-centric, confound-aware multi-stage evaluation framework that separates exploratory geometry, leakage-safe probing, and supporting unsupervised clustering evidence. The methodology is applied to a challenging cymbal dataset characterized by hierarchical labels, class imbalance, and subtle acoustic variation. Results reveal a target-dependent profile of representational strengths rather than a single overall winner. CLAP exhibits stronger variance concentration and more label-consistent local neighborhood organization, and it outperforms MERT on fine-grained, strike-related targets. MERT, however, retains a small but consistent advantage on higher-level cymbal-type classification. Unsupervised analyses show that these advantages reflect local neighborhood structure, not strong global cluster formation, and confound diagnostics indicate that size-related information remains largely type-mediated. Overall, the findings underscore the importance of structured, multi-stage evaluation for disentangling embedding geometry, recoverability, and confound effects while demonstrating the complementary strengths of AFMs in complex audio classification settings. Full article

This review addresses the nutritional composition, health benefits, and claim conditions of aquaculture fish, focusing on gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). Both species provide high-quality proteins, essential amino acids, and favorable lipid profiles, particularly long-chain omega-3 fatty acids, alongside minerals such as phosphorus and selenium, which meet EU criteria for several authorized nutrition and health claims. Evidence demonstrates that regular consumption supports cardiovascular, cognitive, and visual health, reduces inflammation, and contributes to better pregnancy and early childhood outcomes. Consumer skepticism toward aquaculture persists, often driven by perceptions of reduced nutritional quality, despite evidence that farmed fish provide nutritionally valuable proteins and beneficial lipids. Nevertheless, both species consistently meet the requirements for multiple nutrition and health claims, particularly those related to protein, omega-3 fatty acids, and selected minerals, allowing their effective use in labeling and consumer communication. Clear, evidence-based labeling of such claims is crucial to enhance acceptance and promote farmed fish as safe, sustainable, and health-promoting dietary choices. Full article

Sustainability assessment of textile materials has traditionally relied on origin-based classifications and indicator-driven life cycle assessment (LCA), often treating sustainability as an inherent or material-intrinsic property. However, materials sharing similar biological origins or “bio-based” labels frequently exhibit substantially different sustainability outcomes when processing pathways, composite structures, and end-of-life (EoL) compatibility are taken into account. To address this limitation, this study develops a qualitative, multidimensional analytical framework that conceptualizes textile material sustainability as a pathway-dependent and system-mediated outcome rather than an inherent material attribute. The framework integrates four interrelated dimensions—renewability, process sustainability, EoL options, and material source—derived from a structured review of academic, policy, and technical literature. To demonstrate the analytical scope and internal logic of the framework, a selected set of 65 innovative textile materials was systematically analyzed using a three-tier qualitative coding scheme (favorable, conditional, and unfavorable) under conservative data validation criteria. The analysis shows that sustainability performance is primarily shaped by pathway configurations—particularly processing intensity, binder chemistry, and EoL compatibility—rather than material origin alone and that similar bio-based materials can exhibit fundamentally different sustainability profiles depending on these factors. By reframing sustainability from a material-centered perspective to a pathway-oriented and system-based perspective, the proposed framework provides a structured basis for integrating material innovation, process design, and end-of-life planning in sustainability-oriented textile research and development and establishes a conceptual foundation for future empirical and quantitative extensions. Full article

Antimicrobial resistance in microorganisms associated with fermented foods is increasingly recognized, yet rapid methods to characterize antibiotic response dynamics remain limited. This study evaluates antibiotic susceptibility and physiological response patterns of kombucha-associated acetic acid bacteria and motile Escherichia coli using optical nanomotion detection (ONMD), a label-free technique that quantifies single-cell mechanical activity. Two cellulose-producing species (Komagataeibacter xylinus and K. rhaeticus), one non-cellulose-producing species (K. melaceti), and E. coli were exposed to ampicillin, ciprofloxacin, and chloramphenicol. Minimum inhibitory concentrations (MICs) were determined prior to time-resolved ONMD analysis. Susceptible strains exhibited progressive suppression of confined nanomotion consistent with MIC-defined susceptibility, whereas resistant profiles maintained sustained mechanical activity. Chloramphenicol initially induced persistent or increased nanomotion at 120 min; however, extending the observation to 180 min revealed delayed suppression in susceptible strains, demonstrating that bacteriostatic antibiotics require longer observation windows for accurate ONMD classification. In motile E. coli, ONMD revealed both intracellular nanomotion puncta and swimming trajectories, which were progressively attenuated following antibiotic exposure. These findings demonstrate that ONMD complements conventional susceptibility testing by resolving time-dependent suppression of both translational motility and intracellular nanomechanical activity at the single-cell level. Full article

Background/Objectives: This study evaluated the Omicron LP.8.1 variant-adapted BNT162b2 mRNA vaccine (LP.8.1-adapted BNT162b2). Methods: This analysis is part of an ongoing phase 3 open-label study evaluating the immunogenicity, safety, and tolerability of LP.8.1-adapted BNT162b2. Reported here are descriptive 2-week post-vaccination results in 18–64 -year-olds at high risk of severe COVID-19 and in ≥65-year-olds who received the Omicron KP.2-adapted COVID-19 vaccine ≥ 6 months previously. Primary immunogenicity endpoints included neutralizing antibody geometric mean titers (GMTs) against LP.8.1 and KP.2 at 2 weeks after vaccination and geometric mean fold rises from baseline to 2 weeks after vaccination. Results were compared with a historical control group of adults who received KP.2-adapted BNT162b2 in a previous study. Tolerability and safety were also assessed. Results: Overall, 104 participants received LP.8.1-adapted BNT162b2 (18–64-year-olds, n = 51; ≥65-year-olds, n = 53). Baseline neutralizing GMTs were higher in LP.8.1-adapted BNT162b2 recipients than in the historical control group of KP.2-adapted BNT162b2 recipients against both sublineages (248 vs. 157 against LP.8.1; 372 vs. 187 against KP.2). Serum-neutralizing LP.8.1 and KP.2 GMTs increased 2 weeks after vaccination with LP.8.1-adapted BNT162b2 (1752 against LP.8.1; 2104 against KP.2) and historical control groups (1555 and 2395, respectively), and across both age groups. Reactogenicity events with LP.8.1-adapted BNT162b2 were generally mild or moderate and occurred at generally similar frequencies in both age groups. Adverse events were reported in 4.8% of participants (all in 18–64-year-olds); no serious adverse events were reported. Conclusions: After 2 weeks of follow-up, and in a small sample size, LP.8.1-adapted BNT162b2 was immunogenic in ≥65-year-olds and ≥18-year-olds at high risk of severe COVID-19. The safety and tolerability profile for LP.8.1-adapted BNT162b2 was consistent with the current US prescribing information for BNT162b2 and that of other variant-adapted BNT162b2 vaccines (Clinicaltrials.gov Identifier: NCT07069309, registered 16 July 2025). Full article

Surgical precision is critical in oncology, where complete tumor resection while preserving healthy tissue directly influences patient outcomes. Traditional intraoperative diagnostic tools, such as frozen-section analysis, are limited by time constraints, tissue sampling, and interpretative variability. Intraoperative mass spectrometry (MS) has recently emerged as a transformative approach, enabling rapid, label-free molecular profiling of surgical specimens in real time. Several technologies—including Rapid Evaporative Ionization Mass Spectrometry (REIMS, “iKnife”), Desorption Electrospray Ionization (DESI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS) Imaging, Picosecond InfraRed Laser mass spectrometry (PIRL-MS), and novel devices such as the MasSpec Pen—offer unique strategies for intraoperative tumor characterization. Applications have been demonstrated across multiple cancer types, including brain, breast, gastrointestinal, and urogenital malignancies, where MS can improve margin assessment, tumor classification, and surgical guidance. Beyond its clinical promise, intraoperative MS faces technical and translational challenges, including high infrastructure costs, a lack of standardization, and the need for robust multicenter validation. Integration with artificial intelligence, imaging modalities, and multi-omics approaches may further enhance its clinical utility. This review summarizes current technologies, clinical applications, limitations, and future perspectives of intraoperative MS in oncology, highlighting its potential to reshape surgical oncology practice. Full article

Background: Neurodegenerative diseases are a major health problem, and the neuropsychiatric symptoms seen in these diseases adversely impact the lives of patients, families, and caregivers. Inappropriate aggressive behavior is a highly disruptive symptom and a leading cause of institutionalization. There are no approved drugs specifically for the treatment of problematic aggression, and the off-label use of antipsychotics has limited benefit with significant side effects and safety risks. This review discusses dysregulated arginine vasopressin (AVP) signaling in fear–threat circuitry as a key driver of inappropriate aggression. Because the AVP 1a receptor (V1aR) is the dominant subtype in the CNS, the selective antagonism of this receptor represents a well-rationalized target for the treatment of aggression across neurodegenerative, psychiatric, and neurodevelopmental disorders. Objectives: Our goal was to summarize the basis for using V1aR antagonists as a treatment for irritability and aggressive behavior. We describe its discovery, biosynthesis, receptor pharmacology, and CNS distribution, emphasizing V1aR localization in central fear–threat circuits. Translational evidence from animal studies, pharmacological neuroimaging, and lesion network mapping is presented. These data support the suggestion that heightened vasopressinergic tone biases socioemotional information processing toward negative valence, increasing threat sensitivity and the likelihood of inappropriate aggressive responses. Emerging clinical data support this framework. Highly selective, CNS-penetrant V1aR antagonists reduced aggressive behavior and had an excellent safety profile in phase 2 studies in Huntington’s disease and intermittent explosive disorder, with efficacy signals across caregiver-reported, clinician-rated, and incident-based measures. Furthermore, pharmacological neuroimaging showed that V1aR antagonism normalizes AVP-induced alterations in activity within fear–threat circuitry. Conclusions and Future Directions: Preclinical, translational, and clinical findings to date support V1aR antagonism as a promising strategy for treating pathological aggression across disorders. Additional experimental medicine studies and clinical trials are needed to conclusively establish efficacy in various disease populations, and we note the need for improved trial designs and analytical methods as part of the development process. Full article

The growing demand for clean-label food ingredients drives interest in novel marine flavorings. This study evaluated the physicochemical, antioxidant, volatile (GC-MS), and sensory profiles of freeze-dried powders from blue crab roe (Callinectes sapidus), sea urchin roe (Paracentrotus lividus), and beluga caviar (Huso huso) to assess their culinary potential. Results revealed that sensory quality is governed by the synergy between a matrix’s lipid composition and endogenous antioxidant capacity. Sea urchin powder, possessing a low polyunsaturated fatty acid (PUFA) profile and high carotenoid content, exhibited exceptional oxidative stability, yielding a concentrated marine aldehyde signature and top consumer scores. Blue crab roe demonstrated a robust PUFA matrix buffered by high phenolic content, facilitating controlled lipid peroxidation into desirable savory volatiles (ketones and aldehydes). Conversely, the high-fat, monounsaturated-dominant beluga caviar lacked sufficient antioxidants, leading to lipid degradation, oxidized hydrocarbons, earthy off-flavors, and poor texture. Both crab and caviar powders exhibited favorable Atherosclerosis and Thrombogenicity indices. Ultimately, balancing lipid composition and endogenous antioxidants is crucial for flavor stability, highlighting the commercial and environmental potential of transforming underutilized or invasive species like blue crab into stable, nutrient-dense marine flavoring agents. Full article

Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not a new attribution method; rather, it reorganizes existing explanation outputs according to class sensitivity, predictive uncertainty, and asymmetric risk relevance. The empirical analysis uses a single cross-sectional dataset of 954 Indonesia Stock Exchange-listed firms with organizationally provided Low Risk, Medium Risk, and High Risk labels. A stacked ensemble model is used as the explanatory substrate, followed by calibration analysis, uncertainty analysis, and governance-oriented explainability aggregation. On the held-out validation set, the model achieved an accuracy of 0.7487 and a macro ROC-AUC of 0.8630. Repeated stratified validation indicated moderately stable aggregate performance, although class-level reliability remained uneven, with High Risk recall emerging as the weakest and most variable component. The original model showed the most favorable probability reliability among the evaluated variants, whereas temperature scaling and one-vs-rest isotonic regression did not improve calibration. Uncertainty analysis further showed that the most uncertain cases concentrated substantially more misclassifications and High Risk misses; the top 30% most uncertain cases captured 52.1% of all errors and 43.8% of High Risk misses. RW-UCFI produced a materially different feature-priority structure from standard global SHAP ranking, suggesting that explanation outputs may become more decision-relevant for governance-oriented review when contextualized by uncertainty and asymmetric risk conditions in the present setting. Full article

Background/Objectives: Specific Learning Disorders are lifelong neurodevelopmental conditions that persist in adulthood, yet research has traditionally focused on children. In adults, there is significant heterogeneity in cognitive profiles and a lack of consensus on how to operationalize these disorders. This study aims to map the variability in cognitive functioning in university students with Specific Learning Disorders and investigate whether cognitive profiles differ across diagnostic categories and comorbidities. Methods: A retrospective analysis was conducted on the clinical documentation of 166 university students with a diagnosis of Specific Learning Disorders. Participants were categorized into three subgroups: predominant reading disorder, predominant arithmetic disorder, and mixed learning disorder. Cognitive functioning was assessed using Wechsler scales indices. Data were analyzed using linear mixed-effects models and Latent Profile Analysis. Results: Across the sample, reasoning abilities were significantly higher than cognitive efficiency, with working memory consistently emerging as a core weakness. The mixed-disorder group exhibited the lowest cognitive scores and the greatest working memory deficits. Latent Profile Analysis identified two distinct latent subgroups: a “Low Profile” characterized by weaker working memory and a “High Profile” characterized by stronger reasoning and balanced efficiency. Diagnostic labels were only partially aligned with these profiles; while the mixed-disorder group was overrepresented in the “Low Profile,” substantial intra-individual variability existed across all diagnostic categories. Conclusions: The findings suggest that traditional categorical labels for Specific Learning Disorders have limited explanatory power in adulthood, given the high heterogeneity of cognitive functioning. Cognitive weaknesses, particularly in working memory, persist even in high-achieving university students. Clinical and educational support should shift from a label-based approach toward a dimensional, profile-based model to better address the unique strengths and vulnerabilities of adults with Specific Learning Disorders. Full article

C₁₃-Norisoprenoids are important contributors to the aroma of Riesling wine. Their quantification is analytically challenging due to their low concentrations, the lack of commercial standards and their pronounced sensitivity to analytical conditions, reflecting their chemical lability, as well as the dynamic nature of the wine matrix, leading to high reactivity and, consequently, remarkable structural diversity. Here, we developed an assay for the analysis of C₁₃-norisoprenoids in wine using headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME–GC-MS/MS). After evaluating different fiber materials, a statistical design of experiments (DoE) approach was employed to systematically optimize key HS-SPME parameters, including incubation, extraction and desorption conditions. Selected reaction monitoring (SRM) transitions were established for all targeted C₁₃-norisoprenoids, allowing the assay to provide relative quantification of more than 40 compounds using representative labeled and unlabeled standards to generate linear calibration curves. Following method validation, this approach was applied to a young German Riesling wine to investigate the effect of various acidic hydrolysis conditions on the norisoprenoid profile as well as on specific compounds. A central composite design (CCD) was used to systematically study the impact of pH, temperature, and hydrolysis time. Quantitative data were obtained for 22 C₁₃-norisoprenoids demonstrating that hydrolysis conditions strongly affected the norisoprenoid composition. pH and temperature showed a greater influence than reaction time. Response surface models (RSM) indicated that TDN, Vitispirane and TPB in particular are predominantly formed under strongly acidic and high-temperature conditions, whereas others such as Riesling acetal and actinidols are formed under milder conditions. The results indicate that hydrolysis conditions should be tailored to the specific norisoprenoid under investigation and the research question, particularly when simulating conditions of accelerated wine ageing for analytical purposes. Full article