Search Results (857)

Background: Kidney disorders are strongly related to metabolic disturbances, including obesity and type 2 diabetes. Excessive intake of sugar and saturated fats promotes lipid accumulation, cellular energy issues and inflammatory responses. Cannabigerol (CBG), a non-psychotropic phytocannabinoid, has recently gained attention for its metabolic, anti-inflammatory and potential protective properties. Methods: The present study investigated the effect of two weeks of CBG administration (last 14 days of the experiment) on fatty acid (FA) composition, FA metabolic pathways and FA transporters in rats subjected to a high-fat high-sucrose diet (HFHS) for 6 weeks. Male Wistar rats were divided into four groups: Control, CBG, HFHS, and HFHS+CBG. Kidney tissue and urine samples were analyzed by gas–liquid chromatography (GLC) for lipid fractions and FA profiles, while protein expression of FA transporters and metabolic enzymes was assessed by immunoblotting. Polysaccharides and collagen fibers were visualized using Periodic Acid-Schiff (PAS) and AZAN staining, respectively. ELISA and colorimetric kits were used to measure urinary albumin and creatinine contents. Results: HFHS feeding altered renal lipid homeostasis, increasing saturated and monounsaturated fatty acids (SFA and MUFA, respectively) levels and affecting desaturation and elongation ratios. CBG supplementation affected renal lipid metabolism by lowering triacylglycerol (TAG) accumulation, restoring polyunsaturated fatty acids (PUFA) in phospholipid (PL) and altering FA ratios, suggesting an improvement in lipid balance. CBG also increased the expression of carnitine palmitoyltransferase 1 (CPT1) and lipoprotein lipase (LPL) and decreased the expression of stearoyl-CoA desaturase 1 (SCD1) and fatty acid synthase (FAS), suggesting a shift toward enhanced FA oxidation and reduced lipogenesis. Conclusions: Overall, CBG exerted good effects on renal lipid metabolism and may mitigate early lipid-mediated injury associated with metabolic kidney disorders. Full article

Introduction: Aquatic contaminants can alter fish behavior before overt toxicity becomes evident, making neurobehavioral endpoints relevant for ecosystem protection and for hazard prioritization within a One Health framework. We recently developed a high-throughput visual-acoustic zebrafish larval thigmotaxis assay in which edge preference is interpreted as an anxiety-like behavioral endpoint, thereby adding spatial phenotyping beyond conventional locomotion metrics. Objective: To evaluate assay performance in a multisubstance screening challenge and determine whether it can discriminate distinct behavioral fingerprints without prior knowledge of chemical identity. Methodology: Zebrafish larvae were exposed for 1 h at 120 hpf. For each substance, 24 larvae were tested per condition, with six concentrations per substance, plus positive and negative controls. Larvae were challenged using alternating light/dark and tapping/quiet paradigms. The primary endpoint was the percentage of time spent at the edge as a proxy for anxiety-like behavior, while total distance and mean total velocity when moving were used as contextual locomotor metrics; edge distance and edge velocity were used as supportive spatial metrics. Data from 37 substances were analyzed through a standardized automated workflow. Results: Controls performed as expected and supported assay stability across runs. The chemical screening revealed heterogeneous but reproducible behavioral fingerprints. Seven substances produced weak/minimal acute responses, ten showed predominantly suppressive profiles, three predominantly activating profiles, nine showed prominent thigmotaxis-specific anxiety-like signals not explained by locomotion alone, and eight displayed mixed or stimulus-dependent patterns, including non-monotonic responses. Several substances altered edge preference while distance and velocity changed less, differently, or in the opposite direction, indicating behavioral reorganization rather than simple hypo- or hyperactivity. The multi-stimulus design was critical because some effects were evident only under specific sensory contexts. Conclusions: The multisubstance challenge supports the discriminatory capacity, robustness, and added value of the assay for high-throughput neurobehavioral screening. By capturing anxiety-like behavior through thigmotaxis and complementing it with locomotor context, the method improves phenotypic resolution for aquatic pollution assessment and offers a sensitive fish-based NAM to prioritize chemicals of concern for both environmental and human health-oriented testing strategies. Full article

Background/Objectives: Telomeres are critical biomarkers of biological aging, with shortened leukocyte telomere length strongly linked to all-cause mortality and age-related disease risk. Although exercise modulates telomere dynamics, the field’s evolution from descriptive measurements to mechanistic inquiries involving redox biology and epigenetics remains incompletely mapped. This study systematically characterized the global research landscape of telomere–exercise science over 25 years to establish a strategic evidence base for precision exercise prescription. Methods: A bibliometric analysis was conducted on 858 publications from the Web of Science Core Collection (2000–2025). CiteSpace and VOSviewer were used for keyword co-occurrence analysis, strategic thematic mapping, and citation burst detection to visualize global research trends and identify emerging frontiers. Results: Annual publication volume grew from 2 (2000) to 71 (2025), with a compound annual growth rate of 15.4%. China emerged as one of the leading global contributors. Thematic analysis revealed a paradigm shift from descriptive leukocyte telomere length studies toward mechanistic investigations of oxidative stress, mitochondrial homeostasis, and epigenetic clocks. Keyword network analysis confirmed oxidative stress and inflammation as central hubs, mediating telomere protection via redox regulation and non-canonical telomerase functions. Conclusions: Exercise preserves telomere integrity primarily through redox–mitochondrial homeostasis, hormesis-driven antioxidant upregulation, and non-canonical telomerase activation. For aging populations and individuals at metabolic risk, aerobic training and high-intensity interval training (HIIT) are recommended as first-line non-pharmacological interventions for healthspan extension. Leukocyte telomere length and telomerase activity should be integrated as biomarkers in preventive medicine practice. Future large-scale randomized controlled trials incorporating multi-omics approaches and sex-stratified analyses are warranted to establish individualized dose–response guidelines for precision exercise prescription. Full article

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

Background/Objectives: Immunotherapy has emerged as an important field of research in non-small-cell lung cancer (NSCLC) and has demonstrated promising results in clinical practice. In recent years, multiple studies have been conducted, increasing the amount of available data. Therefore, the aim of this systematic review is to assess the combination of perioperative immunotherapy with chemotherapy compared to chemotherapy only in patients with resectable NSCLC in terms of survival, pathological response, and adverse events. Methods: The clinical databases PubMed, Cochrane Library, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) were systematically searched, up to March 2026. A two-step selection process served as the screening for eligibility, in which the assessment was based on pre-defined inclusion and exclusion criteria. This process was visualized via a PRISMA diagram. For each included study, the risk of bias was assessed with the help of the Cochrane Risk of Bias 2.0 tool and the Newcastle Ottawa Scale. A narrative synthesis was performed due to heterogeneity. Data were extracted into tables. Results: A total of 16 studies, involving 4646 patients in total, met the eligibility criteria, and their data on study population, intervention, comparison, and outcome were extracted into tabular form. Survival and pathological response rates are continuously higher in patients treated with immunochemotherapy. Findings on adverse events differed across the individual studies, though the results indicate an increased risk of treatment-related adverse events (TRAEs) in patients undergoing the combined treatment approach. Discussion/Conclusions: Chemoimmunotherapy leads to superior clinical outcomes in terms of survival and pathological response rates, though the trend towards a higher incidence and severity of TRAEs warrants further research. The interpretation of findings is limited by differences in study characteristics, mechanism of conduct, and endpoints between the individual studies. Full article

Vitis vinifera L. × Vitis labrusca L. cv. Black Queen (BQ) is a hybrid cultivar with oenological potential in subtropical climates, yet its sensory structure remains insufficiently systematized. This study aimed to construct an integrated sensory framework by merging two Balanced Complete Block Design (BCBD) datasets into a unified database and developing a structured descriptor reduction workflow to address multicollinearity and redundancy. The resulting “BQ Lexicon v.0” comprised nine Quantitative Descriptive Analysis (QDA) attributes and twelve check-all-that-apply (CATA) descriptors. Based on this optimized dataset, an Artificial Neural Network (ANN) model was developed to predict overall liking (OL), achieving a satisfactory performance (R²(train) = 0.70 and R²(validation) = 0.74). Three-dimensional response surface visualization further illustrated non-linear relationships as a process monitor, indicating sourness as a primary negative driver of acceptance and revealing interactive and synergistic effects between tannin, sweetness, and aroma. These findings demonstrate that integrating structured data management with machine learning can enhance sensory modeling efficiency. Ultimately, the validated BQ Lexicon v.0 and the aligned data framework establish a reliable foundation for future oenological research in Black Queen grape. This structured approach effectively resolves the challenges of integrating distributed sensory datasets, while offering practical insights for targeted winemaking strategies. Full article

Wearable-sensor human activity recognition (HAR) plays a key role in health monitoring, elderly care, and human–computer interaction. Deep learning dominates the field, but two limitations remain. CNNs with fixed kernels cannot capture cross-scale temporal events such as gait cycles and postural transitions in a single layer, and softmax attention on small sensor datasets is often diluted by common-mode background responses across the sequence. We propose DMSCNet, an end-to-end framework with two modules. The Dilated Multi-Scale Branch Block (DMSB) combines a shared bottleneck, parallel dilated convolutions, a pooling bypass, and SE-based channel recalibration to widen the temporal receptive field under a controlled parameter budget. The Contrastive Temporal Attention (CTA) module adopts a dual-path differential design, in which the two paths learn overlapping but non-identical attention patterns and their subtraction suppresses shared low-level responses while preserving the discriminative positions each path locks onto, encoded with opposite signs. DMSB and CTA are cascaded into a DMSC Block and stacked residually. On UCI-HAR, USC-HAD, and RealWorld, DMSCNet reaches F1-scores of 97.65%, 91.80%, and 99.05%, outperforming nine baselines. Ablations confirm that SE acts along the channel axis and CTA along the temporal axis, and visualization reveals a dynamic–static dichotomy together with a signed bipolar encoding pattern produced by the dual-path subtraction. Full article

Regular consumption of excess sugar is linked to nutrition-based diseases, including gut problems, Non-Alcoholic Fatty Liver Disease, and Type 2 Diabetes Mellitus. Increasing sweetness perception is a novel technique to decrease sugar consumption. This experiment compared the sweetness perception of sweetened and unsweetened almond milk in response to different virtual environments with music and visuals. Two music types, the classical song Goldberg Variations, BMV. 998-Variation 13 and a jazz song generated by AI were used. Additionally, fall and spring forest backgrounds were generated by the Blockade Labs 3D image generator. Each participant tasted sweetened and unsweetened almond milk in music-only, background-only, and combination music and background environments. Results revealed significant differences in sweetness ratings for music type (p = 0.015) and between milk types (p < 0.001). Viscosity rating differed significantly between backgrounds (p = 0.04) and by milk type (p < 0.001). Liking ratings varied significantly between backgrounds (p < 0.001) and between music genres (p = 0.011). The results suggest that altering music and background may be a strategy to change sweetness and viscosity perception in unsweetened beverages. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Unprovenanced skeletal remains housed in anatomy laboratories, medical schools, and museums present a persistent ethical challenge when the human skeletal remains potentially represent historically marginalized or legally protected populations. Reliance on a single line of evidence, like epitaphonomic indicators or population affinity estimates, may result in incomplete or misleading assessments. Herein, we present a structured, non-destructive protocol integrating epitaphonomic signatures and an estimation of population affinity (including assessing biocultural indicators of affinity) to support ethical decision-making regarding unprovenanced crania in institutional collections. Our proposed protocol was applied to 16 unprovenanced crania curated in a university anatomy collection. Epitaphonomic signatures associated with anatomical preparation were assessed using categorical scores; craniometric data were analyzed using FORDISC 3.1 following the standard approach therein and using a custom reference dataset imported into the program. Additional exploratory analyses were conducted using a Factor Analysis of Mixed Data (FAMD) to visualize concordance and discordance between epitaphonomic and craniometric variables. Results indicate most individuals exhibit epitaphonomic signatures consistent with anatomical preparation; however, when those data are integrated with craniometric analysis, we identified individuals warranting removal from teaching collections based on elevated ethical concerns. Case examples demonstrate how our protocol supports transparent ethical triage distinguishing between likely anatomically prepared human remains, human remains requiring repatriation, and indeterminate cases requiring further evaluation. This protocol provides a practical, nondestructive framework for the responsible stewardship of unprovenanced skeletal collections. Full article

(1) Background: In the competitive landscape of shopping apps, strategically combining visual elements like color and mascots is crucial for capturing user attention. However, it remains unclear whether their effects are independent, synergistic, or stable, a gap that limits evidence-based design. This study is grounded in the Stimulus-Organism-Response (S-O-R) framework. It investigates how color and mascots, as external stimuli, influence users’ internal attentional state (the organism), which in turn precedes behavioral responses. (2) Methods: Using eye-tracking technology, we conducted a mixed-design experiment with 82 participants. They viewed six sets of authentic app stimuli (icons, launch screens, promotional posters) across colored and non-colored conditions, with or without mascots. Fixation duration and count served as objective measures of attentional engagement. (3) Results: Results revealed significant main effects for both mascots (F(1, 80) = 57.976, p < 0.001) and color (F(1, 80) = 5.010, p = 0.028) on attention. Crucially, their interaction was non-significant (p = 0.450), indicating independent and additive effects. The mascot effect remained robust in both colored and non-colored conditions. (4) Conclusions: The findings, interpreted through the S-O-R lens, suggest that color and mascots operate via distinct pathways in shaping attentional engagement. This supports a hierarchical design strategy: mascots form a stable, foundational attentional attractor, while color provides an independent, enhancing layer. This mechanistic understanding offers a theoretical and practical guide for optimizing visual appeal in app design, particularly for the young, digitally native user demographic that constitutes the core market of the studied platforms. Full article

Steady-state visual evoked potential-based brain–computer interfaces (SSVEP-BCIs) are widely applied in non-invasive brain–computer interaction, yet traditional single-frequency coding suffers from scarce frequency resources and degraded accuracy in multi-target tasks. The partitioned backlight mode (PB-M) supports SSVEP spatial coding, while systematic investigations on its inherent backlight crosstalk are still lacking. This study develops a PB-M-based SSVEP-BCI system to explore crosstalk mechanisms. Each participant completed 90 valid trials with 18 stimuli and five repetitions each. The results verify inter-partition crosstalk, which can reduce recognition accuracy under narrow frequency intervals and non-isolated layouts, and gaze position can modulate non-target SSVEP responses. Classification accuracy was calculated by valid correct trial ratios, and the information transfer rate (ITR) was computed using standard BCI formulas, yielding 87.50% accuracy and 48.75 bits/min ITR. Full exhaustive classification testing across all 18 stimulus targets was not implemented, where core classification validation was performed on partially selected targets. The proposed frequency reuse strategy shows promising potential to improve SSVEP-BCI performance based on empirical experimental data, providing valid references for multi-target BCI design. Full article

Emotion recognition through electroencephalogram (EEG) signals is crucial for brain–computer interfaces (BCIs), yet existing methods often struggle with heterogeneous feature fusion and capturing long-range temporal dependencies. To address these challenges, we propose MAF-TransNet, a novel unified spatiotemporal framework. Specifically, parallel Fully Connected Neural Network (FCNN) modules first non-linearly align heterogeneous differential entropy (DE) and power spectral density (PSD) features. Subsequently, an Adaptive Channel-wise Feature Encoder (ACFE) recalibrates spatial–spectral responses to highlight emotion-relevant cortical activations. Finally, a Transformer encoder dynamically models the global temporal evolution of emotional states. Evaluated on the SEED-IV and DEAP datasets, MAF-TransNet achieves superior subject-dependent (SD) accuracies of 88.80% and 96.58%, respectively, alongside robust subject-independent (SI) performance. Furthermore, Granger causality analysis reveals distinct emotion-dependent prefrontal asymmetry, while t-SNE visualizations confirm the formation of a highly discriminative, linearly separable feature manifold. Ultimately, MAF-TransNet effectively unifies local spatial–spectral extraction with global temporal modeling, providing an accurate and robust approach, while offering preliminary insights into the spatiotemporal dynamics of emotion for future affective BCI applications. Full article

Masonry retaining walls are widely used in mountainous regions but are susceptible to progressive internal damage under environmental and operational loads, which is often difficult to detect through conventional visual inspection. To address this problem, this study proposes a baseline-free vibration-based damage identification method for existing masonry retaining walls. The method combines impulse response function (IRF) estimation with wavelet packet decomposition (WPD) and introduces a scalar damage index, termed the energy ratio standard deviation (ERSD). Unlike conventional WPD energy ratio deviation (ERD) vectors, ERSD condenses multi-band energy redistribution into a single positive scalar for each sensor location, thereby facilitating spatial interpolation and field-level damage localization without modal extraction. The method was validated through four monthly impact hammer tests on a masonry retaining wall in Zhenjiang, China. The results show that non-zero ERD vectors indicate vibration energy redistribution between successive monitoring states, while the spatial peak of ERSD identifies the most likely damage zone. The ERSD maximum occurred at point 5 and was confirmed by post-test visual inspection, which revealed a local crack of approximately 0.8–1.2 mm in the adjacent mortar joint. To avoid overfitting with the limited four-test dataset, the temporal trend of ERSD was evaluated using a linear regression and finite-difference progression rates rather than a high-order polynomial. The proposed method provides a practical preliminary screening tool for field damage localization; however, its quantitative damage severity calibration requires further validation using controlled stiffness-reduction tests and environmental compensation models. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly integrated into dental diagnostics. This study evaluated the ability of ChatGPT-4o and Gemini 3 Flash to visually identify endodontic instruments and assess their explanatory plausibility regarding instrument morphology. Methods: Standardized images of five endodontic file systems (Reciproc R25, Reciproc Blue, WaveOne Gold, MM One Shape, and XP-endo Finisher) were submitted to both models via their free tiers. Each image was evaluated 50 times per model (total n = 500) to assess both classification accuracy and response consistency. Visual recognition performance was measured using recall, precision, and F1-score, while the plausibility of morphological explanations was evaluated using a structured 3-point scale. Results: Gemini 3 Flash demonstrated significantly higher recognition performance compared to ChatGPT-4o (p < 0.001). The overall acceptable response rate was higher for Gemini 3 Flash (94.4%, [95% CI: 91.5–97.3%]) than for ChatGPT-4o (67.2%, [95% CI: 61.4–73.0%]; p < 0.001). Notably, Gemini 3 Flash showed strong performance in identifying complex instrument designs, whereas ChatGPT-4o exhibited marked limitations in recognizing certain non-standard geometries. Reliability analysis indicated higher consistency for Gemini 3 Flash (κ = 0.86, [95% CI: 0.81–0.91]) compared to ChatGPT-4o (κ = 0.51, [95% CI: 0.44–0.58]). Conclusions: Gemini 3 Flash outperformed ChatGPT-4o in both classification accuracy and consistency in this controlled visual identification task. While these findings highlight the potential of multimodal LLMs in endodontic workflows, their current performance variability limits direct, autonomous clinical application. Further validation under clinically realistic conditions is required before such systems can be considered reliable adjunctive tools. Full article