Search Results (593)

Animal behavior recognition is an important research area that provides insights into areas such as neural functions, gene mutations, and drug efficacy, among others. The manual coding of behaviors based on video recordings is labor-intensive and prone to inconsistencies and human error. Machine learning approaches have been used to automate the analysis of animal behavior with promising results. Our work builds on existing developments in animal behavior analysis and state-of-the-art approaches in computer vision to identify rodent social behaviors. Specifically, our proposed approach, called Vision Transformer for Rat Social Interactions (ViT-RSI), leverages the existing Global Context Vision Transformer (GC-ViT) architecture to identify rat social interactions. Experimental results using five behaviors of the publicly available Rat Social Interaction (RatSI) dataset show that the ViT-RatSI approach can accurately identify rat social interaction behaviors. When compared with prior results from the literature, the ViT-RatSI approach achieves best results for four out of five behaviors, specifically for the “Approaching”, “Following”, “Moving away”, and “Solitary” behaviors, with F1 scores of 0.81, 0.81, 0.86, and 0.94, respectively. Full article

Background: Traumatic Brain Injury (TBI) in children is a major cause of morbidity and mortality worldwide. Ocular manifestations are common but often overlooked, despite their potential to cause long-term visual impairment. This study aimed to evaluate the prevalence and characteristics of ocular findings in pediatric TBI patients admitted to the intensive care unit (ICU). Method: We prospectively reviewed records of pediatric patients (≤16 years) with TBI admitted to the Neurosurgery ICU at King Abdullah University Hospital (January 2022–December 2024). TBI was defined using U.S. CDC criteria and confirmed by clinical and radiological findings. Ocular manifestations were identified from ophthalmology consultations, neurosurgical notes, and bedside examinations. Demographics, injury details, and clinical outcomes were recorded. Statistical analyses included Chi-square, Fisher’s exact, and Mann–Whitney U tests, with significance set at p ≤ 0.05. Results: Thirty-eight patients (median age: 8 years; 55.3% male) were included. Ocular findings were present in 20 patients (52.6%). These patients were significantly older (median age 10 vs. 6 years, p = 0.007) and had lower admission GCS scores (11 vs. 14, p = 0.016). Male predominance was higher in the ocular group (75.0% vs. 33.3%, p = 0.030). Ocular findings were significantly associated with surgical intervention (60.0% vs. 22.2%, p = 0.025), orbital fractures (40.0% vs. 5.6%, p = 0.021), basal skull fracture signs (p = 0.036), and extraocular muscle limitation (p = 0.048). On multivariable analysis, orbital fracture remained the only independent predictor of ocular findings (aOR 2.22, 95% CI 1.17–3.57, p = 0.02). Conclusion: Over half of pediatric ICU TBI patients demonstrated ocular manifestations, closely linked to greater injury severity and craniofacial trauma. Routine, comprehensive ophthalmological evaluation should be integrated into the multidisciplinary management of severe pediatric TBI to optimize visual and functional outcomes. Full article

Background: Chronic relapsing colitis involves immune dysregulation and oxidative stress, making monotherapies often insufficient. This study investigates a therapeutic strategy combining doxycycline (Dox), an immunomodulatory antibiotic, with Arthrospira platensis extracts to enhance anti-inflammatory and antioxidant effects, improving remission and controlling relapse. Methods: Ethanolic (ES) and aqueous (AS) extracts of A. platensis were chemically characterized by GC-MS after derivatization. Colitis was induced in mice using two intrarectal DNBS administrations spaced 7 days apart, with oral treatments (Dox, ES, AS, or combinations) given daily between doses. Disease progression was evaluated through clinical monitoring, histological scoring, and biochemical analysis, including MPO and CAT activities, as well as NO, MDA, and GSH levels. Results: GC-MS identified 16 bioactive compounds in each extract. ES contained mainly fatty acids and amino acids, whereas AS was rich in polysaccharides and phytol. Combined doxycycline and A. platensis extracts significantly enhanced recovery in reactivated DNBS colitis compared to monotherapies. Each treatment alone reduced disease severity, but their combination showed synergistic effects, significantly reducing disease activity index (p < 0.001), restoring mucosal integrity, and modulating inflammatory and oxidative markers (p < 0.001). Conclusion: Doxycycline potentiates the anti-colitic effects of A. platensis extracts via complementary mechanisms, offering a promising combination for managing relapsing colitis. Full article

Background: Intensive Care Unit (ICU) readmission and in-hospital mortality are critical indicators of patient outcomes following ICU discharge. Patients readmitted to the ICU often face worse prognosis, higher healthcare costs, and prolonged hospital stays. Identifying high-risk patients is essential for optimizing post-ICU care and resource allocation. Methods: This two-phase study included the following: (1) a retrospective analysis of ICU survivors in a mixed medical–surgical ICU to identify risk factors associated with ICU readmission and in-hospital mortality, and (2) a prospective validation of a newly developed predictive model: the Worse Outcome Score (WOScore). Data collected included demographics, ICU admission characteristics, severity scores (SAPS II, SAPS III, APACHE II, SOFA), interventions, complications and discharge parameters. Results: Among 1.190 ICU survivors, 126 (10.6%) were readmitted to the ICU, and 192 (16.1%) died in hospital after ICU discharge. Key risk factors for ICU readmission included Diabetes Mellitus, SAPS III on admission, and ICU-acquired infections (Ventilator-Associated Pneumonia (VAP) and Catheter-Related Bloodstream Infection, (CRBSI)). Predictors of in-hospital mortality were identified: medical admission, high SAPS III score, high lactate level on ICU admission, tracheostomy, reduced GCS at discharge, blood transfusion, CRBSI, and Acute Kidney Injury (AKI) during ICU stay. The WOScore, developed based on the results above, demonstrated strong predictive ability (AUC: 0.845 derivation, 0.886 validation). A cut-off of 20 distinguished high-risk patients (sensitivity: 88.1%, specificity: 73.0%). Conclusions: ICU readmission and in-hospital mortality are influenced by patient severity, underlying comorbidities, and ICU-related complications. The WOScore provides an effective, easy-to-use risk stratification tool that can guide clinicians in identifying high-risk patients at ICU discharge and guide post-ICU interventions, potentially improving patients’ outcomes and optimizing resource allocation. Further multi-center studies are necessary to validate the model in diverse healthcare settings. Full article

Background/Objectives: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease, but its relationship with gastric cancer (GC) remains unclear. This study aimed to investigate the association between AD and GC using a nationwide Korean database. Methods: Using the Korean National Health Insurance Service-National Sample Cohort, we conducted a nested case–control study including 10,174 GC patients and 40,696 matched controls (1:4 by age, sex, income, and region). Overlap propensity score weighting was used to control for confounders. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated via logistic regression. Results: AD was significantly associated with an increased risk of GC (adjusted OR = 1.08; 95% CI: 1.01–1.15). Subgroup analyses revealed stronger associations among individuals aged ≥ 65 years (OR = 1.12), men (OR = 1.10), rural residents (OR = 1.14), and those without comorbidities (CCI = 0, OR = 1.15). Higher risks were also observed in participants with non-allergic rhinitis (OR = 1.43) or no asthma (OR = 1.12). Conclusions: AD may be associated with an increased risk of GC in the Korean population. These findings may highlight the importance of considering dermatological conditions in the context of systemic cancer risk. Full article

Discovering new yeast species can be crucial for creating new types of beers. In this study, we investigated three new yeast species, Saccharomyces bayanus, Schizosaccharomyces japonicus and Schizosaccharomyces pombe var. malidevorans, which have not been previously used in the brewing industry. Colour, total acidity, bitterness, aroma profile, total phenolic, flavonoid, mineral content and organoleptic characteristics of beers fermented by these strains were analysed to discover their applicability in the brewing industry. They did not significantly affect the nutritional value and colour of the beers, but showed increased acidity compared to the control Saccharomyces cerevisiae. GC-MS (Gas Chromatography-Mass Spectrometry) analysis revealed 33 aroma compounds, some of which were identical and some unique. S. cerevisiae and S. bayanus produced a similar number (19–20) of aroma compounds, while S. japonicus produced the fewest, including some undesirable compounds. Isobutyl alcohol, isoamyl alcohol, acetol, dimethylpyrazine, acetic acid, 4-cyclopentene-1,3-dione, butyrolactone, 2-furanmethanol, phenylethyl alcohol, maltol and pyranone that provide desired aromas in beers could be found in every sample. The new yeasts significantly increased polyphenols and decreased flavonoid content. Based on the results above and the taste scores, the strains S. bayanus and S. pombe var. malidevorans may be suitable for brewing, while S. japonicus is less or only suitable for combined fermentation. Full article

Control strategies for leishmaniasis increasingly target sand fly vectors through sugar feeding approaches containing bioactive compounds. This study investigated the behavioral and toxicological effects of the iridoids plumericin and isoplumericin, isolated from Himatanthus sucuuba, on Lutzomyia longipalpis by integrating computational and experimental approaches focused on gustatory system interactions. The iridoids were purified by column chromatography and characterized by GC-MS. The gustatory receptor A0A1B0CHD5 was structurally characterized through homology modeling, followed by molecular docking and 100 ns molecular dynamics simulations. Behavioral assays evaluated survival, repellency, and feeding preferences using sugar solutions supplemented with an iridoid mixture. Toxicity was assessed in Drosophila melanogaster as a non-target organism model. Molecular docking results revealed comparable binding affinities between sucrose (ChemPLP score 57.96) and the iridoids plumericin (49.08) and isoplumericin (47.75). Molecular dynamics simulations confirmed the stability of the ligand–receptor complexes and revealed distinct conformational changes. The iridoids did not affect L. longipalpis survival, showed no repellency, and did not reduce sugar feeding acceptance. Preference for the control diet was observed only after continuous exposure (48 h), suggesting involvement of post-ingestive sensory processing. No acute toxicity was observed in D. melanogaster (96% survival). These findings demonstrate that iridoids preserve vector feeding behavior and survival while exhibiting low toxicity to non-target organisms, supporting their potential use in gustatory modulation strategies in leishmaniasis vector control without compromising ecological safety. Full article

Background/Objectives: Severe acute pancreatitis (SAP) carries substantial morbidity and resource burden, and early risk stratification remains challenging with conventional scores that require serial observations. The aim of this study was to develop and compare supervised machine-learning (ML) pipelines—integrating feature selection and SHAP-based explainability—for early prediction of SAP at emergency department (ED) presentation. Methods: This retrospective, single-center cohort was conducted in a tertiary-care ED between 1 January 2022 and 1 January 2025. Adult patients with acute pancreatitis were identified from electronic records; SAP was classified per the Revised Atlanta criteria (persistent organ failure ≥ 48 h). Six feature-selection methods (univariate AUROC filter, RFE, mRMR, LASSO, elastic net, Boruta) were paired with six classifiers (kNN, elastic-net logistic regression, MARS, random forest, SVM-RBF, XGBoost) to yield 36 pipelines. Discrimination, calibration, and error metrics were estimated with bootstrapping; SHAP was used for model interpretability. Results: Of 743 patients (non-SAP 676; SAP 67), SAP prevalence was 9.0%. Compared with non-SAP, SAP patients more often had hypertension (38.8% vs. 27.1%) and malignancy (19.4% vs. 7.2%); they presented with lower GCS, higher heart and respiratory rates, lower systolic blood pressure, and more frequent peripancreatic fluid (31.3% vs. 16.9%) and pleural effusion (43.3% vs. 17.5%). Albumin was lower by 4.18 g/L, with broader renal–electrolyte and inflammatory derangements. Across the best-performing models, AUROC spanned 0.750–0.826; the top pipeline (RFE–RF features + kNN) reached 0.826, while random-forest-based pipelines showed favorable calibration. SHAP confirmed clinically plausible contributions from routinely available variables. Conclusions: In this study, integrating feature selection with ML produced accurate and interpretable early prediction of SAP using data available at ED arrival. The approach highlights actionable predictors and may support earlier triage and resource allocation; external validation is warranted. Full article

Background: Shock index (SI) is calculated by dividing heart rate (HR) by systolic blood pressure (sBP) and is a useful tool for predicting the prognosis of trauma patients. This study aimed to determine whether SI is useful in predicting mortality in patients undergoing emergency surgery for trauma. Methods: We analyzed 1657 patients who underwent emergency surgery for trauma. Patients were divided into SI < 1 and SI ≥ 1 groups and the Glasgow Coma Scale (GCS), Injury Severity Score (ISS), revised trauma score (RTS), Korean Triage and Acuity Scale (KTAS), transfusion amount, and mortality were compared. Binary logistic regression analysis was performed to identify factors associated with mortality. Results: There were significant differences in GCS, ISS, RTS, and KTAS in the SI ≥ 1 group compared to the SI < 1 group (all p-values < 0.001). In the SI < 1 cohort, the mortality rate was 11% (144/1283), and in the SI ≥ 1 group the mortality rate was 33% (125/374) (p < 0.001). Age, GCS, ISS, SI ≥ 1, and KTAS were determined to be predictors of mortality by logistic regression analysis. In particular, SI ≥ 1 group members exhibited a high association with elevated mortality (OR, 2.498; 95% CI, 1.708–3.652; p < 0.01). Conclusions: Although SI alone has limitations in predicting the patient’s prognosis, patients with SI ≥ 1 upon arrival at the emergency room are associated with mortality of patients undergoing emergency surgery for trauma, along with already known trauma assessment systems such as GCS, ISS, and KTAS. Full article

The rising popularity of ready-to-eat self-heating sauerkraut fish necessitates a meticulous production process to ensure high-quality products. This study investigated the impact of processing stages on the quality of ready-to-eat tilapia fillets. The results showed that lipid oxidation, protein degradation, pH levels, and TBA concentrations increased during processing. GC-IMS analysis revealed 56 volatile compounds in tilapia fillets, with distinct compositions at different processing stages. The flavor profiles of tilapia fillets underwent significant changes during blanching and rehydration. The levels of aldehydes and alcohols notably increased, with the blanching group exhibiting the highest concentration of aldehydes, particularly saturated linear aldehydes such as hexanal, nonanal, octanal, and benzaldehyde, which play key roles in enhancing fish flavor. Conversely, the proportion of ketones decreased following heat treatment, which is a crucial factor in mitigating undesirable fishy odors. Therefore, the optimal method for preparing ready-to-eat tilapia fillets was salting pretreatment (1.5% salt and 3% propylene glycol) at 4 °C for 1 h, blanching at 100 °C for 1 min, pre-freezing at −40 °C for 12 h, and vacuum freeze-drying at −40 °C under 20 Pa for 18 h. Finally, the dried fish fillets were vacuum-sealed for storage. Principal Component Analysis (PCA) revealed that the combined variance explained by the first two principal components post-dimensionality reduction was 95%, serving as a primary indicator of the volatile flavor profile of the fish. The dried fillets were thoroughly verified using sensory evaluation. This specific formulation garnered the highest scores in sensory evaluations, resulting in superior aroma, color, and texture attributes for the self-heating fish product. The findings of this study offer a foundational framework for developing ready-to-eat tilapia fillets and other convenient food products in the future. Full article

This study examines industrial digitalization, with a particular focus on the transformation from Industry 4.0 to Industry 5.0. The research is based on a database of 1441 Scopus-indexed articles, which forms the basis of a systematic literature review and bibliometric network analysis. The articles were ranked using Global Citation Score (GCS), followed by Co-Coupling Network (CCN) within VosViewer, the method to create arrays. The arrays were analyzed based on the connection strengths of the citations in them. Next, we performed Burst Detection using the CiteSpace app. Finally, the most relevant keywords, determined in the Burst Detection, were used for Co-Occurrence Network (CONK), with which we could create new arrays and analyze them. By connecting the various, fragmented scientific findings, our results highlight that digital twins, artificial intelligence, supply chain resilience and the Internet of Things are the focus of Industry 4.0, i.e., the technological side is dominant. In contrast, Industry 5.0 places employees at the center. It also emphasizes the analysis of human–machine interaction and the importance of green digital sustainability. The results provide a comprehensive picture of how decision-makers, researchers, and professionals can interpret a changing mindset and apply it as practical advice. Full article

Defect detection in lithium-ion battery (LIB) welding presents unique challenges, including scale heterogeneity, subtle texture variations, and severe class imbalance. We propose a multi-scale convolutional framework that integrates EfficientNet-B0 for lightweight representation learning, PANet for cross-scale feature aggregation, and a YOLOv8 detection head augmented with multi-head attention. Parallel dilated convolutions are employed to approximate self-similar receptive fields, enabling simultaneous sensitivity to fine-grained microstructural anomalies and large-scale geometric irregularities. The approach is validated on three datasets including RIAWELC, GC10-DET, and an industrial LIB defects dataset, where it consistently outperforms competitive baselines, achieving 8–10% improvements in recall and F1-score while preserving real-time inference on GPU. Ablation experiments and statistical significance tests isolate the contributions of attention and multi-scale design, confirming their role in reducing false negatives. Attention-based visualizations further enhance interpretability by exposing spatial regions driving predictions. Limitations remain regarding fixed imaging conditions and partial reliance on synthetic augmentation, but the framework establishes a principled direction toward efficient, interpretable, and scalable defect inspection in industrial manufacturing. Full article

Background/Objectives: Most genes involved in the pathogenesis of Metabolic Syndrome (MS) and Type 2 Diabetes Mellitus (T2DM) are regulated by peroxisome proliferator-activated receptors (PPARs), which modulate the production of pro-inflammatory cytokines, with interleukin-6 (IL-6) playing a crucial role. The associations of single-nucleotide polymorphisms (SNPs) with MS and T2DM remain uncertain across populations. Therefore, we aimed to investigate the associations of PPAR-related SNPs in IL-6, LEP, ADIPOQ, ADIPOR1, and ADIPOR2 with MS and T2DM clinical features. Methods: Polymorphism analysis of IL-6, LEP, ADIPOQ, ADIPOR1, and ADIPOR2 genes was performed on isolated DNA from individuals diagnosed with T2DM and from healthy controls using real-time polymerase chain reaction (qPCR). Results: The IL-6-174G/C polymorphism shows that the CC genotype is associated with higher MS risk, whereas the GG genotype appears protective against metabolic disturbances. When the IL6 CC genotype is combined with ADIPOR2 GA or ADIPOR2 219 A/T, hyperglycemia is 1.3 times more frequent than with other IL6/ADIPOR2 genotype combinations. Conclusions: To develop informative genetic risk scores, future studies should include additional polymorphisms in key immune–metabolic pathway genes, such as AP-1, NF-κB, and FFAs. Full article

Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited. Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance. Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS. Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI. Full article

Background/Objectives: Sarcopenia, the age-related decline in skeletal muscle mass and function, is increasingly recognized as an important prognostic factor among elderly patients. This study aimed to evaluate whether computed tomography (CT)-defined sarcopenia independently predicts short-term mortality in elderly Korean trauma patients. Methods: We retrospectively analyzed 722 patients aged ≥65 years admitted to a Korean Level I trauma center between January 2020 and December 2021. Sarcopenia was defined as the lowest sex-specific quartile of skeletal muscle index (SMI) measured at the third lumbar vertebra (L3) within 7 days of admission. Demographics, injury severity, and outcome variables were compared between groups. Kaplan–Meier survival analysis with a 24 h landmark and multivariable Cox regression were applied to identify independent predictors of 30-day mortality. Results: Among 722 patients, 181 (25.1%) were sarcopenic. They were older and had lower body mass index and serum albumin yet showed lower Injury Severity Score (ISS) at presentation. Despite this, in-hospital mortality was higher in sarcopenic patients (15.5% vs. 9.8%, p = 0.036), while 24 h mortality did not differ (4.4% vs. 3.7%, p = 0.663). Landmark analysis starting at 24 h demonstrated significantly worse 30-day survival in the sarcopenia group (log-rank p = 0.028). Multivariable Cox regression confirmed sarcopenia as an independent predictor of 30-day mortality (HR, 2.36; 95% CI, 1.07–5.23; p = 0.034), along with higher ISS and lower Glasgow Coma Scale (GCS) scores. Conclusions: CT-defined sarcopenia at the L3 level independently predicts 30-day mortality in elderly trauma patients and may support early risk stratification. Full article