Search Results (477)

Background/Objectives: Automated insulin delivery (AID) systems have improved the management of type 1 diabetes (T1D), but exercise and nutrition remain challenging because they rapidly alter glucose flux, substrate oxidation, hepatic glucose output, insulin requirements, and fuel availability. This narrative review aimed to synthesize current evidence on the interaction between AID systems, physical activity, and nutritional strategies from a metabolism-oriented perspective. Methods: A narrative bibliographic approach was used to integrate evidence from clinical trials, observational studies, technical studies, consensus statements, and reviews involving people with T1D across different life stages, including pediatric, adolescent, adult, and pregnancy-related contexts, when available. The review focused on AID systems, exercise physiology, nutritional strategies, meal announcement, bolus adjustment, dual-hormone systems, metabolic biomarkers, and emerging metabolomic approaches. Results: AID systems generally improve time in range and reduce hypoglycemia across several user groups, although most exercise- and nutrition-specific evidence comes from adult and pediatric/adolescent cohorts rather than pregnancy-specific exercise studies. Exercise-related glucose responses remain highly dependent on user input, exercise modality, insulin on board, meal timing, and metabolic state. Planned exercise announcement, prandial bolus reduction before postprandial activity, and individualized carbohydrate intake remain key strategies. Biomarkers such as lactate, ketone bodies, non-esterified fatty acids, and counter-regulatory hormones may help explain interindividual variability and support future personalization. Conclusions: Nutrition and exercise management in AID users should be interpreted as a dynamic metabolic interface among exogenous insulin, endogenous counter-regulation, substrate availability, and algorithmic control. Emerging approaches, including activity sensors, adaptive algorithms, dual-hormone systems, digital twins, and metabolomics-informed personalization, may improve safety and reduce user burden, but several remain exploratory and require further validation in diverse free-living conditions. Full article

This study introduces a spatiotemporal framework at the district level in Indonesia to examine and forecast stunting prevalence. The empirical analysis draws on data from 514 districts observed over 2022–2024, with short-term projections extended to 2025–2027 in line with the SDG 2.2 agenda. The modeling methodology is based on a Bayesian spatiotemporal formulation with the SPDE-INLA method. Instead of handling spatial and temporal lags separately, the model simultaneously incorporates them to reflect dependencies that change across both dimensions. This structure facilitates a more flexible representation of underlying risk dynamics. To improve prediction performance, we augment the baseline model with a hybrid component. Specifically, residual variation from the Bayesian specification is further explored using machine learning methods, providing an additional layer of adjustment. Spatial dependence is assessed through three alternative weighting schemes—KNN, Queen contiguity, and distance-based matrices—which are compared prior to selecting the final specification. The empirical specification includes nine key predictors within a semi-parametric framework. Several covariates are allowed to depart from strict linearity by accommodating time-varying effects. Three algorithms were evaluated during the prediction process to determine their abilities to capture the residual structure: XGBoost, Random Forest, and Elastic Net. Spatiotemporal clustering is examined through exceedance probabilities, resulting in the identification of seven unique cluster patterns. The findings consistently indicate that poverty is the main factor influencing stunting dynamics, with evident regional spillovers and temporal variations. Persistent hotspots are primarily located in eastern Indonesia. From a predictive standpoint, the hybrid specification—particularly the variant based on XGBoost—delivers the most stable performance. The forecast results indicate a gradual reduction in stunting prevalence throughout the forecast period. This study establishes persistent geographic inequalities in child nutrition risk and translates them into district-specific intervention priorities, providing decision-support information to further SDG Target 2.2 and its relationships with SDGs 1, 3, 4, and 6. Full article

Inflammatory bowel disease (IBD; Crohn’s disease and ulcerative colitis) arises from convergent dysfunction of the epithelial barrier, mucosal immunity, and gut microbiome on a background of genetic susceptibility and environmental exposures. Diet is among the most modifiable of these exposures, yet much of the diet–microbiome research in IBD remains descriptive and poorly aligned with the molecular pathways linking food to mucosal effects. This comprehensive review reframes the field around functional dysbiosis, in which altered microbial metabolic capacity (rather than taxonomic shifts alone) drives disease-relevant biology. We trace how dietary substrates and additives are converted by gut microbes into bioactive metabolites (short-chain fatty acids, secondary bile acids, tryptophan-derived indoles, sulfur compounds, and polyphenol-derived molecules) and map these to host receptors and signaling pathways governing barrier function, mucus and antimicrobial peptide production, and Treg/Th17 balance. Defined dietary therapies (exclusive enteral nutrition, the Crohn’s disease exclusion diet plus partial enteral nutrition, and Mediterranean-style patterns) are reinterpreted as interventions that reshape microbial metabolic output, and candidate biomarkers for microbiome-informed precision nutrition are evaluated. Microbiota-derived metabolites provide the molecular interface between diet and mucosal immunity in IBD; personalized dietary algorithms remain a research goal, not a validated clinical tool, and diet is best framed as adjunctive to pharmacotherapy and dietitian care. Full article

Background: Malabsorption is a frequent and clinically relevant condition associated with a high risk of disease-related malnutrition across multiple gastrointestinal and systemic disorders. Despite its prevalence, standardized nutritional management algorithms remain limited. Following a previously published Delphi consensus on the use of oligomeric enteral formulas, the present study aimed to develop and validate a practical nutritional management algorithm for patients with malabsorption. Methods: A structured expert questionnaire was conducted among 173 physicians with clinical experience in malabsorption, including specialists in endocrinology, gastroenterology, internal medicine, and oncology. Participants gained experience using the preliminary algorithm by applying it to five real-life cases before completing the questionnaire. The survey addressed symptom type, intensity, and duration required for screening, diagnostic criteria for malnutrition, timing of reassessment, indications for oligomeric oral nutritional supplements (ONSs), and criteria for reintroducing polymeric formulas. Statistical analyses were performed using SAS v9.4. Results: Of the 173 participants, 67.1% were women, with a mean age of 39.6 ± 8.2 years and a mean clinical experience of 10.9 ± 7.9 years. In clinicians’ opinion, diarrhea was the most frequently considered symptom to trigger screening (88.6%), followed by abdominal distension (72.6%), abdominal pain (65.4%), and increased gastric residuals (45.8%). Experts agreed that symptoms should present with at least moderate intensity and persist for more than 3 weeks to justify screening. Most respondents agreed with using the GLIM criteria for malnutrition assessment (97.7%). For patients with poor tolerance to polymeric ONSs or moderate-to-severe symptoms, initiation of oligomeric ONSs was recommended, with diarrhea identified as the main indication (31.1%). Symptom severity prompting oligomeric formulas was rated between 2.8 and 3.3 on a 5-point scale. The mean recommended duration of symptom improvement before transitioning back to polymeric formulas was 6.24 ± 4.45 weeks. Conclusions: This study presents a consensus-based, clinically applicable algorithm for nutritional screening, diagnosis, and intervention in patients with malabsorption. The algorithm provides clear guidance on symptom assessment, use of GLIM criteria, selection of ONS type, and follow-up, potentially improving standardization and quality of nutritional care in this high-risk population. Full article

Frequently, equine diseases necessitate the moistening or soaking of hay for the horses. It was assumed that this treatment would not reduce the willingness of the Polish Konik horses with low nutritional requirements to consume the hay. Furthermore, it was believed that moistening would increase the palatability of this feed. The aim of this study was to determine time and frequency parameters and the amount of unconsumed dry, moist, or soaked hay. The research material comprised 12 Polish Konik horses consuming either 1 kg of dry hay, 2.1 kg of moist hay, or 3.3 kg of soaked hay on three subsequent days of the experiment. The following parameters were determined: the time of hay intake, the duration and frequency of pauses in intake, the time of straw intake, the time and frequency of drinking, and the time spent on other activities during a one-hour period following the provision of hay, after which the feed refusals were weighed. An algorithm for assessing the ‘palatability’ of hay was also developed. The experimental time of hay intake ranged from 1261.67 s (moist hay) to 2663.33 s (soaked hay), and the maximum mass of feed refusals ranged from 10.00 g (dry hay) to 343.33 g (soaked hay). No sex-related differences were noted. The time of straw intake did not exceed 60 s. In general, Polish Konik horses were noted to be selective in their feeding habits, as the ‘palatability’ of soaked hay was low at best, that of dry hay was moderate or high, and that of moist hay was very high. The low ‘palatability’ of soaked hay was more evident in the geldings than in the mares. The reduced willingness to consume hay did not contribute to an increase in the straw intake, which is not a substitute for roughage. It is also important to note that different results may be expected in some horses, as the values for the traits examined indicated significant individual variation. Full article

Background/Objectives: The global aging population has placed escalating demands on long-term care systems, with nursing homes facing persistent challenges including chronic understaffing, high staff turnover, complex resident acuity, and elevated risk of adverse events. Artificial intelligence (AI)—encompassing machine learning, natural language processing, and computer vision—presents a transformative opportunity to address these systemic pressures by enabling proactive, data-driven care delivery. This rapid review aims to systematically map the existing literature on AI applications in nursing facilities, categorize how these technologies contribute to improvements in quality of care, and identify gaps warranting further investigation. Methods: Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we conducted a comprehensive search of academic literature using a predefined Boolean string. The extracted data were organized and analyzed thematically. Results: The synthesized literature (n = 28 studies) revealed seven primary themes: (1) Clinical management, risk prediction, and monitoring; (2) Pressure injuries, wound management, and diagnostics; (3) Objective assessment, mental health, and end-of-life care; (4) Nutrition and personalized daily support; (5) Operational efficiency and staffing; (6) Technical, infrastructure, and economic barriers; and (7) Social, ethical, and demographic considerations. Conclusions: AI holds considerable promise for enhancing the quality of care in nursing homes across clinical, operational, and social domains. However, widespread adoption remains constrained by prohibitive infrastructure costs, data privacy regulations, algorithmic bias, staff resistance, and limited generalizability of findings across diverse populations. Successful integration requires evidence-based implementation frameworks and standardized and interoperable platforms. Full article

Background/Objectives: Regular physical exercise is strongly recommended for individuals with type 1 diabetes mellitus (T1DM) because of its beneficial effects on cardiovascular fitness, insulin sensitivity, metabolic control, and overall health. Nevertheless, participation in physical activity remains limited, largely due to the fear of exercise-induced hypoglycemia and glycemic instability. Glycemic responses to exercise in T1DM are influenced by the interaction between exercise modality, circulating insulin levels, nutritional status, and diabetes technologies. Continuous aerobic exercise, resistance training, high-intensity interval exercise, and mixed intermittent activities elicit distinct metabolic and hormonal responses, resulting in heterogeneous glycemic trajectories. This narrative review aimed to provide a clinically oriented synthesis of the physiological mechanisms underlying exercise-related glycemic fluctuations in T1DM and to discuss integrated insulin- and carbohydrate-based strategies to support safer participation in physical activity in the context of modern diabetes technologies. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, and complementary searches in Google Scholar to identify experimental studies, observational studies, systematic reviews, consensus statements, and clinical guidelines focused on exercise-related glycemic responses in individuals with T1DM. Only articles published in English were considered. Evidence was selected and synthesized according to relevance to exercise modality, insulin therapy strategies, carbohydrate management, and diabetes technologies, including continuous glucose monitoring, continuous subcutaneous insulin infusion, and automated insulin delivery systems. The final narrative synthesis was based on 44 selected studies, reviews, consensus statements, and guidance documents considered most relevant to the objectives of this narrative review. Results: Available evidence indicates that continuous moderate-intensity aerobic exercise is most consistently associated with progressive glucose declines and increased risk of hypoglycemia, particularly when performed in the presence of elevated insulin on board. In contrast, resistance exercise and short-duration high-intensity or anaerobic exercise more frequently induce stable glycemia or transient hyperglycemia through adrenergic stimulation and increased hepatic glucose output. Mixed and intermittent exercise modalities often produce more variable responses depending on exercise sequencing, nutritional status, and insulin exposure. Across studies, integrated adjustment of basal and prandial insulin doses together with individualized carbohydrate supplementation emerged as the most effective strategy to reduce exercise-related glycemic instability. Continuous glucose monitoring and insulin pump technologies improved glucose trend awareness and management flexibility; however, physical exercise remains a challenging condition for current automated insulin delivery algorithms and still requires active user-driven decision-making. Conclusions: Exercise management in T1DM should be based on an individualized interpretation of exercise modality, glucose trends, insulin exposure, and nutritional context rather than on fixed glucose thresholds alone. Combining anticipatory insulin adjustments, tailored carbohydrate strategies, and appropriate use of diabetes technologies may substantially reduce glycemic variability and improve confidence toward physical activity participation. Structured education and individualized clinical guidance remain essential to translate physiological knowledge into effective real-world exercise management. Full article

Soybean protein content is a key indicator of nutritional value and quality grade, and its determination is important for quality evaluation and cultivar selection. To overcome the time-consuming and costly limitations of conventional chemical assays, this study proposed a multiple linear learner ensemble importance-score wavelength selection (MLLEISWS) method to identify informative wavelengths from soybean near-infrared spectra and establish a partial least squares (PLS) model. MLLEISWS was compared with competitive adaptive reweighted sampling, successive projections algorithm, and uninformative variable elimination. Shapley additive exPlanations (SHAP) were applied to the MLLEISWS algorithm to interpret the selected wavelengths. Results showed that the PLS model developed using MLLEISWS achieved the best performance. With only 29 selected wavelengths, the coefficients of determination for the training and test sets reached 0.941 and 0.933, respectively. Root mean square errors were 0.490% and 0.514%, relative root mean square errors were 1.32% and 1.37%, and residual predictive deviation was 3.863, indicating predictive accuracy and stability. SHAP analysis showed that the selected wavelengths were located in protein-related spectral regions and corresponded to overtone and combination bands information from functional groups. MLLEISWS effectively reduced variable dimensionality while maintaining model performance. Full article

This study investigated the fatty acid profile of Muli yak (Bos grunniens) milk and its relationship with compositional parameters across different parities. Milk samples from second-, third-, and fourth-parity yaks were analysed for protein, fat, vitamins, minerals, and 37 fatty acids using gas chromatography. Statistical analyses included ANOVA, correlation analysis, principal component analysis (PCA), machine learning algorithms, and molecular docking. Parity significantly affected 15 components (p < 0.05), with third-parity milk showing the highest eicosapentaenoic acid (EPA, C20:5n3) and arachidonic acid (ARA, C20:4n6) concentrations. Among 134 significant correlations, calcium-ARA and ARA-EPA exhibited strong positive associations (|r| > 0.67). PCA explained 54.2% of the variance through three principal components, differentiating samples by parity. The optimal prediction models were ARA-XGBoost, EPA-Random Forest, ALA-GAM, and LA-SVM, with calcium and protein serving as key predictors. Molecular docking revealed that EPA-FABP2 had the lowest binding energy. These parity-related shifts in functional long-chain polyunsaturated fatty acids are meaningful for the nutritional value of yak milk (e.g., omega-3/omega-6 profile) and may also influence technological properties associated with milk fat composition (e.g., oxidative stability and processing behaviour), supporting parity-oriented quality evaluation and targeted utilisation of yak milk. Full article

Along with the enhancement of people’s public health consciousness and the requirement for individual diet arrangement getting more urgent, the meal recommendation method, which is based on artificial intelligence, has hence become an active research domain in the field of intelligent health. One system that makes practical recommendations must deal with the user’s unclear queries, while at the same time, it must satisfy strict nutrient demands. A great number of existing methods at present either do not take into account verifiable food composition data, or they handle implicit dietary restrictions in a not good way. For solving these problems, we put forward CARE (Constraint-Aware Recipe Engine). Beginning from a mixed Retrieval-Augmented Generation (RAG) basic model (CARE v1.0), we have developed CARE v2.0, which is a suggestion engine that unites intention polish, knowledge graph enlargement, and rule-based checking in a unified working line. Instead of depending on huge black-box models, our framework utilizes an effective language model that possesses 1.5 B parameters. User inquiry content are undergone parsing to become structured nutrition targets; a food knowledge graph links abstract health notions to specific cooking materials; and the obtained candidate results are filtered in accordance with strict diet restrictions, with optional checking carried out by an automatic agent-based reviewer. Under a zero-shot cold-start situation, the system attains a semantic recall@5 of 0.825 on 400 k recipes coming from Recipe1M+ and a newly created fuzzy-query benchmark (CAREBench-150), and it thus has a better performance than dense retrieval baselines (0.550) as well as direct zero-shot prompting. The constraint satisfaction rate is located at 85.0% in fast mode, and it rises to 98.5% when the verification module is in the working state; therefore, it supports the safety of recommendations. These findings indicate that structured food knowledge, which matches a compact algorithmic framework, can therefore connect unclear user intentions and accurate nutrition requirements effectively. Full article

Background and Objectives: The therapeutic role of surgery in Crohn’s disease has evolved in the era of advanced biologic therapies, particularly in patients with complex and treatment-refractory disease. This study aimed to evaluate the relationship between preoperative biologic exposure and surgical outcomes, with a focus on predictors of more extensive surgical procedures, postoperative biological response, and postoperative biologic management. Materials and Methods: We conducted a retrospective cohort study including 60 patients with Crohn’s disease who underwent CD-related surgical interventions between January 2011 and December 2024. Clinical, surgical, and therapeutic data were collected. Combined resection procedures were defined as intestinal resections associated with additional surgical interventions. Postoperative biological response was defined as an exploratory composite endpoint reflecting the simultaneous normalization of hemoglobin, serum albumin, and C-reactive protein at six months. Statistical analyses, including univariable and multivariable methods, were performed. Results: Combined resection procedures were associated with advanced disease, particularly penetrating phenotypes and intra-abdominal sepsis, and with more frequent postoperative biologic intensification (OR 5.56, 95% CI: 1.05–29.57, p = 0.044). Postoperative biologic management included maintenance and intensification strategies (initiation or switching of biologic therapy). At six months, postoperative biological response was achieved in 20.7% of patients (12/58). No significant associations were observed between biological response and preoperative anti-TNF exposure or postoperative biologic intensification. Despite the relatively low rate of complete biological normalization, hemoglobin and albumin normalization were observed in 79.3% and 69.0% of patients, respectively, while the median fecal calprotectin decreased from 820 µg/g preoperatively to 130 µg/g at follow-up. Endoscopic remission was observed in 47.6% of patients with available SES-CD assessment. Conclusions: In patients with complex Crohn’s disease, surgical intervention remains an essential component of multidisciplinary management. While complete postoperative biological normalization was achieved in a limited proportion of patients, surgery was associated with consistent improvements in inflammatory and nutritional parameters. Further prospective studies are needed to better define predictors of postoperative recovery and to clarify the role of surgery within modern treatment algorithms. Full article

Food safety, nutrition, and public health are actual economic and medical problems. Sweetness is an important feature of food technology. Models for the sweetness of special organic compounds used in the food industry are suggested. The models are built using the CORAL software. New statistical coefficients of predictive potential are studied. These are the index of ideality of correlation (IIC) and correlation intensity index (CII). The effectiveness of using the IIC and CII has been tested in simulated sweetness via Monte Carlo optimization of correlation weights for molecular features extracted from Simplified Molecular Input Line Entry System (SMILES) strings. Both factors have been shown to improve the model’s statistical quality on the calibration and validation sets. However, this is accompanied by a decrease in the statistical quality of the training sets. Full article

Background: Gastric cancer survivors frequently encounter a “care gap” after discharge because of complex postgastrectomy syndromes. We evaluated “WECARE,” a bidirectional digital health platform designed to provide real-time symptom monitoring and multidisciplinary support. The primary goal of this study was to assess the efficacy of the platform by measuring the change in the Korean Quality of Life Questionnaire for Gastric Cancer Survivors (KOQUSS-40) total score over a six-month recovery period. Methods: This nationwide, multicenter pilot randomized controlled trial was conducted by the Korean Quality of Life in Stomach Cancer Patients Study Group (KOQUSS) across nine tertiary centers in Korea. A total of 88 patients who underwent curative gastrectomy were enrolled. Following an initial optimization phase involving 22 patients, the remaining 66 patients were randomized at a 1:1 ratio to the WECARE group or the control group. The WECARE group used a platform integrating the KOQUSS-40 algorithm for structured symptom reporting, real-time feedback on nutrition and exercise, and educational content on meal planning, symptom coping, and recovery. Assessments were performed at baseline and at 1, 3, and 6 months after discharge. Results: The WECARE group showed high feasibility and acceptability, with an adherence rate of 86.7% and an 82% satisfaction rate. At 6 months, the KOQUSS-40 total score (primary endpoint) did not differ significantly between the WECARE and control groups (85.3 ± 1.6 vs. 83.8 ± 1.6, p = 0.603). However, the WECARE group showed a numerically favorable recovery trajectory from the acute postoperative phase. Subgroup analysis revealed a positive trend in reflux symptom management in the WECARE group (p = 0.0856). In addition, more than 77% of users reported that the platform improved their self-management capabilities. Conclusions: The WECARE platform is a feasible and acceptable digital intervention for gastric cancer survivors. Although the primary endpoint was not significantly different, the favorable recovery trajectory, high adherence, and patient engagement support further evaluation in larger studies with longer follow-up and broader healthcare settings. Full article

Background: Overweight and obesity are major health problems of the 21st century. As a significant risk factor for numerous noncommunicable diseases, obesity is also strongly associated with the development of abdominal hernias, which significantly impair patients’ quality of life. The review focuses on the pathophysiological mechanisms linking obesity to hernias and the impact of key prehabilitation components. Available research indicates a complex interrelationship between obesity and the development of ventral hernias, driven by pathophysiological mechanisms such as increased intra-abdominal pressure and chronic inflammation, which weakens the collagen matrix of the abdominal wall. Furthermore, both smoking and alcohol consumption significantly increase the risk of abdominal obesity and surgical complications; in turn, physical activity is crucial for reducing visceral fat. Psychological support may reduce pre-operative stress and contribute to improved outcomes. Nutritional intervention and weight loss are other essential components of preoperative management for ventral hernia repair. This review aims to highlight the role of prehabilitation in ventral hernia surgery in obese patients and to propose a structured, evidence-based algorithm (DEPP) for this high-risk population. The algorithm includes: Dietary intervention (D), Elimination of smoking and alcohol consumption (E), Physical activity (P), and Psychological support (P). The algorithm was developed to systematize the clinical approach and determine the steps to be taken in the treatment of patients with obesity and abdominal hernia. Methodology: A literature search was conducted across PubMed, Scopus, and Google Scholar databases for articles published between 2002 and 2026. We included randomized controlled trials, prospective/retrospective cohort studies, systematic reviews, and meta-analyses. Conclusions: Prehabilitation is a multifaceted strategy for optimizing the health of patients with obesity prior to abdominal hernia repair. The proposed prehabilitation algorithm, known as DEPP, is a preliminary approach for managing this group of patients. Full article

To address the challenge of depleting traditional feed resources, this study aimed to biovalorize sweet potato waste (SPW), a major byproduct of the Japanese shochu industry, into a high-value functional animal feed. An innovative two-stage solid-state fermentation (SSF) was employed, featuring an initial aerobic stage with Aspergillus oryzae for substrate degradation, followed by an anaerobic stage with Lactobacillus plantarum for nutritional enhancement. To optimize this complex, multi-variable process, the predictive performance of Artificial Neural Network (ANN) and Random Forest (RF) machine learning models was compared based on an augmented experimental dataset (N = 80). To ensure statistical robustness and prevent data leakage, a repeated k-fold cross-validation strategy was implemented. The RF model demonstrated significantly superior accuracy and reliability than the ANN model, particularly in predicting the primary metric, crude protein (R² = 0.61 ± 0.04 vs. R² = 0.12 ± 0.15). Subsequently, the validated RF model was integrated with a Constrained Differential Evolution (CDE) algorithm for global parameter optimization. The optimized process was predicted to yield a final product with a crude protein content of 25.0%, alongside significant increases of 114.1% in total amino acids and 123.9% in essential amino acids. These projections were experimentally validated in vitro, confirming the model’s accuracy with a relative error of less than 5%. Furthermore, comprehensive biochemical assays demonstrated a massive degradation of anti-nutritional factors and significant enhancements in total phenolic content and antioxidant activity. This study provides a scientifically validated, data-driven framework for the valorization of SPW. It confirms the superior efficacy of ensemble learning methods for optimizing complex bioprocesses with limited data, offering a contribution to the development of a circular bioeconomy and sustainable feed resources. Full article