Search Results (509)

Machine learning (ML) models for hypertension risk prediction have predominantly relied on traditional demographic and clinical risk factors, often overlooking the temporal heterogeneity of environmental exposures. This study developed and validated an interpretable ML framework that systematically integrates multi-window cumulative PM_2.5 exposure features for hypertension risk assessment. We designed a modular analytical pipeline comprising five ML algorithms—Generalized Linear Model (GLM), Lasso regression, Decision Tree, Random Forest (RF), and XGBoost—coupled with a three-layer interpretability module (variable importance, SHAP values, and partial dependence plots). The framework ingests traditional risk factors alongside cumulative PM_2.5 exposure across five temporal windows (0-day, 7-day, 15-day, 30-day, and 60-day). As a validation case, the framework was applied to 2523 participant-visits from the Beijing subsample of the China Health and Nutrition Survey. Analyses were performed at the participant level, systolic and diastolic blood pressure were excluded from the predictors of the hypertension classifiers, and models were validated with person-level and year-based splits. After these corrections the five models showed realistic discrimination, with test AUCs of 0.69–0.77 and Brier scores of 0.17–0.23. Age, body mass index (BMI) and waist circumference were consistently among the most important predictors, and the 60-day PM_2.5 window was the most important exposure feature. Adding the five PM_2.5 window features improved test AUC by ≈0.02–0.03 in the ensemble models (p = 0.03–0.05). Window-specific adjusted analyses showed inverse associations of PM_2.5 with hypertension that were stronger for longer windows. The proposed framework provides a reusable interpretable approach for incorporating multi-window environmental exposure data into cardiovascular risk prediction. Its modular design enables adaptation to other environmental exposures, health outcomes, and population cohorts, supporting both risk screening and personalized intervention strategies. Full article

Sarcopenia is an independent predictor of adverse outcomes in hospitalized patients, associated with increased mortality, prolonged length of stay, and functional decline, yet it remains underdiagnosed owing to inconsistent diagnostic criteria and the practical challenges of assessing acutely ill patients. This narrative review summarizes current evidence on sarcopenia in hospitalized patients, with emphasis on diagnostic approaches, nutritional requirements, and optimal nutritional support strategies. Sarcopenia is particularly prevalent among critically ill patients and is driven by the convergence of systemic inflammation, immobility, and anabolic resistance. Diagnosis remains challenging: no ICU-specific framework exists, and the recent Global Leadership Initiative of Sarcopenia consensus established a unified, setting-independent conceptual definition as a foundation for future operational criteria, with ICU-specific frameworks yet to be developed. Bedside tools, including handgrip strength, bioelectrical impedance analysis, and ultrasound, retain strong prognostic value despite imperfect agreement with reference imaging. Nutritional intervention is a key component of management; however, large-scale trials have demonstrated that higher energy or protein delivery does not universally improve outcomes and may be harmful in selected critically ill patients. A gradual increase in energy and protein delivery according to the phase of illness, with protein targets of ≥1.2–1.5 g/kg/day, is recommended; leucine-enriched supplementation may help counteract anabolic resistance, although direct evidence in non-ICU hospitalized patients remains limited. Early mobilization is an essential complement, as nutrient provision without a mechanical stimulus cannot reverse immobility-induced muscle loss. A practical clinical algorithm integrating screening, nutritional target-setting, route selection, and reassessment is proposed to guide decision-making in this population. Future research should focus on improving the accuracy and feasibility of muscle assessment tools in critically ill patients to enable the timely diagnosis of sarcopenia and nutritional intervention, alongside the development of validated multi-marker panels and adequately powered trials examining the combined effect of nutrition and exercise in this population. Full article

Background: Longitudinal severity assessment may provide more informative risk stratification than reliance on a single admission score in intensive care. This study developed and internally validated an explainable machine learning framework for predicting a subsequent APACHE-II-defined high-severity state using repeated APACHE-II assessments in intensive care unit (ICU) patients receiving total parenteral nutrition (TPN). The endpoint was defined as an APACHE-II score of ≥ 20 at the final assessment (T10), while predictor information was restricted to measurements obtained at T0–T9. Methods: A retrospective institutional cohort of 844 adult ICU patients was analyzed, of whom 214 (25.4%) met the predefined high-severity endpoint. Four principal feature representations were evaluated: admission APACHE-II, longitudinal APACHE-II information combining the raw T0–T9 sequence with derived trajectory descriptors, longitudinal laboratory information, and multimodal combinations including baseline characteristics. Six machine learning algorithms were compared using stratified five-fold cross-validation and independent hold-out testing. Additional analyses included simpler APACHE-II comparators, sequential observation truncation, nested cross-validation, calibration assessment, threshold sensitivity analysis, and SHAP-based model interpretation. Because equivalent longitudinal APACHE-II measurements and an equivalent endpoint were unavailable in eICU, a separate harmonized XGBoost model was used only for exploratory cross-cohort portability assessment. Results: The longitudinal APACHE-II CatBoost model achieved the highest performance, with a cross-validated ROC-AUC of 0.924 and an independent hold-out ROC-AUC of 0.921 (95% CI: 0.878–0.960). Hold-out calibration was good (Brier score = 0.095, calibration intercept = − 0.03, slope = 0.98), and nested cross-validation yielded a pooled out-of-fold ROC-AUC of 0.908 and a mean outer-fold ROC-AUC of 0.920 ± 0.038. The latest APACHE-II assessment alone (T9; ROC-AUC = 0.553), change from baseline (0.576), ordinal slope (0.539), and logistic regression using engineered APACHE-II descriptors (0.633) performed substantially below the full longitudinal CatBoost model. Sequential truncation showed that discrimination was retained after removing later observations, although performance varied non-monotonically across truncated sequences. SHAP analysis showed that multiple APACHE-II observations contributed prominently to model predictions, with trajectory-derived descriptors providing complementary contributions. Adding laboratory trajectories or baseline characteristics did not improve discrimination over the longitudinal APACHE-II representation. Conclusions: Nonlinear modeling of repeated APACHE-II assessments provided strong internal discrimination of a subsequent APACHE-II-defined high-severity state in this TPN-treated ICU cohort and substantially outperformed single-score and simpler longitudinal comparators. However, the predictors and endpoint share the APACHE-II construct, observation indices were sequential rather than standardized clock-time intervals, and the primary model could not undergo conventional external validation in eICU. These findings therefore support the internal predictive value of longitudinal APACHE-II modeling for this specific severity-state task and warrant prospective multicenter evaluation using standardized timing and independent clinical outcomes. Full article

Large language models (LLMs) can answer dietary questions, but their use as substitutes for registered dietitians remains insufficiently quantified. Evaluations have identified inaccurate nutrient targets, incomplete clinical advice, and insufficient personalization, alongside useful performance on some general nutrition tasks. Social media and food-delivery interfaces may also influence dietary choices through food cues, social endorsement, and choice architecture. This conceptual perspective presents ADRIFT (Algorithmic Dietary Reconfiguration through Informational, Framing, and Technological mechanisms) to examine how these influences might interact. Drawing on a targeted, nonsystematic synthesis of nutrition, behavioral, neuroscience, and AI literature, ADRIFT distinguishes three axes: informational errors and unsupported advice; framing through content and interface design; and adaptive personalization and cognitive delegation. Direct evidence supports selected component mechanisms, but their convergence into a feedback loop that reduces nutritional autonomy remains hypothetical. We propose a six-category taxonomy covering hallucinations and related nutrition errors, define nutritional autonomy erosion as declining capacity for informed, self-directed dietary decisions, and outline eight testable hypotheses. The framework concerns unsupervised use and does not predict that language models will replace dietetic professionals. Longitudinal validation, platform experiments, and clinical safety evaluations are needed to determine whether, for whom, and under which conditions the proposed harms occur. Full article

Background: Estimated pulse wave velocity (ePWV) is a non-invasive marker of arterial stiffness with potential relevance for chronic kidney disease (CKD) risk assessment. This study aimed to investigate the association between ePWV and CKD risk in the US and Chinese populations and to evaluate its discriminative performance using machine learning approaches. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES, 2005–2018, weighted n ≈ 100.9 million) and the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015, n = 21,853). In NHANES, CKD was defined according to the 2021 KDIGO criteria as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² or a urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g; in CHARLS, where urinary albumin was not measured, CKD was defined by the eGFR criterion alone, and this difference in case definition was taken into account when interpreting the results. Logistic regression and restricted cubic spline models were used to examine the association between ePWV and CKD, with subgroup analyses stratified by demographic and clinical characteristics. Multiple machine learning models were developed in NHANES and externally validated in CHARLS; model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC), and feature importance was interpreted using SHapley Additive exPlanations (SHAP) values. Results: Higher ePWV was consistently associated with higher odds of CKD in both cohorts (NHANES: odds ratio [OR] = 1.505 per 1 m/s, 95% confidence interval [CI] = 1.459–1.552; CHARLS: OR = 1.434 per 1 m/s, 95% CI = 1.368–1.503; both p < 0.0001), with dose–response relationships observed. Formal interaction tests confirmed significant effect modification by sex (both cohorts) and by BMI and diabetes (NHANES only). Point estimates were higher in men and in NHANES obese and diabetic subgroups, but interactions across smoking and alcohol strata were not statistically significant. Among the machine learning models evaluated, discrimination was moderate and comparable across algorithms (LightGBM: AUROC = 0.804 in internal validation and 0.793 in external validation), and DeLong tests showed no significant difference between LightGBM and XGBoost (p = 0.0655 and 0.0684, respectively). SHAP analysis identified ePWV as the highest-ranking feature, surpassing uric acid, lipid levels, and diabetes history. Using the Youden index, the optimal ePWV cutoff for identifying CKD was 10.15 m/s in NHANES (sensitivity 0.658, specificity 0.695) and 10.568 m/s in CHARLS (sensitivity 0.658, specificity 0.718). Conclusions: Elevated ePWV is significantly associated with eGFR-defined CKD across the US and Chinese populations studied. These findings support ePWV as a potentially useful marker for CKD risk stratification; however, given the cross-sectional design of both cohorts, its predictive value requires confirmation in prospective studies. It is important to emphasize that no non-invasive calculated metric can replace direct measurement of serum creatinine and urinalysis for identifying individuals at risk of CKD in routine clinical practice. Full article

Sustainable agricultural management faces growing challenges from climate change, water scarcity, energy constraints, soil degradation, and the need to ensure both food production and nutritional security. This study proposes an integrated computational framework combining a Global Agricultural Foundation Representation Model (GAFRM), Self-Evolving Explainable Digital Twin (SEDT), Self-Evolving Evolutionary Foundation Optimizer (SEEFO), and an enhanced Green Regenerative Agriculture Sustainability Index (GRASI). The framework operationalizes the food–water–energy–carbon–nutrition (FWEC-N) nexus by explicitly incorporating crop micronutrient density into the agricultural decision architecture and modeling its relationship with regenerative practices such as cover cropping, biochar application, and zero tillage. Using multi-source global datasets, GAFRM learns transferable agricultural representations, SEDT enables adaptive prediction under climate uncertainty, and SEEFO performs five-objective optimization of agricultural productivity, irrigation water use, energy demand, net carbon balance, and overall sustainability, while nutritional quality is evaluated through the MODI outcome indicator. The enhanced GRASI further evaluates nutrient output, soil restoration, carbon storage, and climate resilience within a unified sustainability framework. The framework was evaluated using a global agricultural dataset covering approximately 60 representative countries across six continents and 15 climate zones over the 2000–2026 period. SEDT achieved an RMSE of 3.18, MAE of 2.29, R² of 0.972, and NSE of 0.968, while SEEFO achieved the highest Hypervolume (0.956) and the lowest GD (0.028), IGD (0.039), and Spread (0.162) among the benchmark optimization algorithms. The observed performance differences were statistically significant according to the Wilcoxon signed-rank and Friedman tests (p < 0.05). The findings indicate that integrating nutritional quality with resource efficiency, carbon balance, soil regeneration, and climate resilience provides a more comprehensive basis for evaluating regenerative agricultural strategies. The architecture establishes a fully transparent, explainable decision-support environment through explainable AI (XAI) feature attributions, bridging the gap between digital precision farming, regenerative ecosystem restoration, and sustainable human nutrition under increasing environmental uncertainty. Full article

AI-enabled smart pig farming has evolved from isolated sensing applications to integrated systems for health monitoring, precision feeding, welfare assessment, and environmental control. However, evidence remains fragmented across computer vision, sensor engineering, nutritional management, and thermal monitoring, obscuring mature and commercially viable technological pathways. This review evaluates deployment-relevant AI technologies for commercial pig production through structured evidence mapping and critical thematic synthesis. We analyzed 707 publications from the Web of Science Core Collection (WoSCC; 1991–2025) and evaluated candidate themes based on publication activity, citation patterns, temporal persistence, and thematic convergence. Three technical streams were examined in depth: vision-based pig detection, precision feeding, and AI-assisted infrared body-temperature monitoring. Across these areas, research has progressed from proof-of-concept algorithms to integrated sensing-to-decision systems. Vision-based detection is advancing toward robust, lightweight models; precision feeding toward individualized closed-loop control; and thermal monitoring toward automated region-of-interest (ROI) localization and AI-assisted temperature interpretation. Major gaps remain in dataset representativeness, cross-farm generalizability, methodological consistency, field-scale validation, system reliability, economic feasibility, thermal calibration, surface-to-core temperature inference, ROI localization, and false-alarm control. This review is limited by its reliance on a single bibliographic database, predefined search terms, and potential publication and citation biases. Future progress requires cross-site validation, multimodal sensing, interpretable decision models, cost-effective deployment, adaptive thermal calibration, and reliable alert strategies. Overall, AI-enabled smart pig farming is not only an algorithmic challenge but also a systems-integration task that must translate sensing and prediction into actionable farm management. Full article

Sarcopenia is increasingly recognized as a systemic complication of inflammatory bowel disease (IBD)—more accurately described as an IBD-associated muscle disorder than as a classical extraintestinal manifestation—but it remains inconsistently defined and rarely integrated into routine care. Consensus frameworks require low muscle strength confirmed by low muscle quantity or quality, so studies reporting only computed tomography (CT)-derived muscle area describe low muscle mass rather than consensus-defined sarcopenia; myosteatosis, the fat infiltration of muscle, is a further and partly independent dimension of muscle quality. This narrative review, supported by a structured literature search that was re-run and extended during peer review, synthesized peer-reviewed human evidence published from 1 January 2010 to 5 July 2026 in adult patients. Overall, 152 records were identified through PubMed/MEDLINE and citation tracking; after 19 duplicate or overlapping records were removed, 133 records were screened, 66 full-text reports were assessed, and 54 sources were included: 39 empirical IBD studies, 5 IBD-specific systematic reviews or meta-analyses, 6 consensus or standardization documents and 4 mechanistic or narrative reviews. Sarcopenia in IBD is driven by chronic inflammation, malnutrition, dysbiosis, corticosteroid exposure, inactivity and impaired anabolic signaling. Prevalence is definition- and setting-dependent, from approximately 10% in stable outpatients assessed with functional criteria to more than 40–50% in CT-based or active-disease cohorts. Sarcopenia is consistently associated with—rather than proven to cause—hospitalization, abscess formation, postoperative complications, treatment escalation or failure and impaired function. Muscle ultrasound and automated, artificial intelligence-assisted analysis of opportunistic CT and magnetic resonance imaging (MRI) are emerging as practical routes to routine assessment. We propose a drivers–detection–prognosis–intervention framework, aligned with the sequential European Working Group on Sarcopenia in Older People 2 (EWGSOP2) and Asian Working Group for Sarcopenia (AWGS) 2019 algorithms, to support opportunistic imaging review, strength testing and integrated nutrition–exercise care; this framework is an expert proposal that requires prospective, multicenter validation. Full article

Newborn screening (NBS) for inborn errors of metabolism is challenged by high false-positive rates, which may lead to parental anxiety and increased healthcare costs associated with diagnostic follow-up. False-positive results often arise from changes in metabolite concentrations that mimic metabolic disorders as a consequence of differences in perinatal factors or nutritional status. To address this, Collaborative Laboratory Integrative Reports (CLIR) and the associated post-analytical tools (PATs) using multivariate interpretation and covariate-adjusted reference intervals may be used to improve specificity of NBS algorithms. In the current study, we examined whether CLIR can be applied to optimize the Dutch NBS program by reducing the false-positive rates. We developed and validated a CLIR-based PAT for CPT1 deficiency, GA-I, IVA and MSUD within the Dutch NBS program. Single-condition tools (SCTs) and multivariate approaches, including marker ratios and covariate adjustments, were evaluated for their ability to discriminate true- and false-positive referrals. For CPT1 deficiency, age-adjusted SCT combined with birthweight, location correction, and the C18:1/methionine ratio substantially reduced false positives. For GA-I, C3DC-based ratios improved specificity while preserving true-positive detection, potentially reflecting postnatal renal immaturity in some cases. For IVA, the dual scatter plot fully separated true- and false-positive referrals, highlighting the limitations of single-marker screening. For MSUD, differences between false-positive and true-positive cases were more pronounced, yet a similar number of false positives were still referred; valine-related markers contributed to false positives, while leucine and the Xle/Phe ratio better identified true positives. CLIR-based post-analytical tools enhanced NBS specificity through covariate-aware, multivariate interpretation. This provides important input for decision makers in both the Dutch NBS, as well as the NBS community worldwide. Full article

Background and Objectives: Critically ill surgical patients frequently fail to achieve prescribed enteral nutrition targets because of feeding interruptions and variability in conventional physician-directed feeding practices. Although volume-based feeding (VBF) has been proposed to improve nutritional delivery, concerns remain regarding refeeding syndrome and gastrointestinal intolerance. The aim of this study was to evaluate whether a Volume-Based Plus (VBF+) protocol integrating volume-targeted feeding with mandatory refeeding risk stratification and a standardized semi-elemental enteral formula improves nutritional delivery without increasing gastrointestinal or metabolic complications. Materials and Methods: We conducted an ambispective before-and-after implementation study (retrospective control phase, prospective intervention phase) in the surgical intensive care unit of a tertiary care center between January 2023 and March 2026. Ninety-six patients requiring enteral nutrition for ≥3 days were enrolled (48 control and 48 intervention). Both groups underwent the same refeeding risk stratification to guide feeding strategy, while the intervention group additionally received the standardized VBF+ protocol with risk-stratified caloric advancement and volume-based compensation for feeding interruptions, and a standardized semi-elemental enteral formula. The primary outcomes were median daily caloric delivery (% of target) and mean daily protein delivery (g/kg/day). Multivariable regression analyses adjusted for Nutritional Assessment Form (NAF), APACHE II score, and Charlson Comorbidity Index (CCI) were performed. Results: The VBF+ protocol was independently associated with higher caloric delivery (β = 31.2 percentage points, 95% CI 17.6 to 44.8; p < 0.001) and higher protein delivery (β = 0.168 g/kg/day, 95% CI 0.040–0.297; p = 0.011). Longitudinal analysis showed a significantly faster increase in protein delivery in the intervention group (p < 0.001), while the group-by-time interaction for caloric delivery did not reach statistical significance (p = 0.073). Rates of gastrointestinal intolerance and metabolic complications were comparable between groups; as VBF+ bundled the compensatory algorithm with a semi-elemental formula, this gastrointestinal benefit is hypothesis-generating. Conclusions: In this before-and-after study, the VBF+ bundle protocol was associated with improved caloric and protein delivery and reduced gastrointestinal intolerance; because the algorithm and formula were not evaluated independently, the gastrointestinal benefit remains hypothesis-generating and requires confirmation in randomized trials. Full article

Obesity is a complex, multifactorial chronic disease characterized by excessive and/or aberrant adiposity that requires interventions beyond conventional calorie-restriction models. Current clinical guidelines converge on behavioral modification, encompassing nutritional therapy, physical activity, stress reduction, and sleep improvement, as the cornerstone of obesity management, with psychological therapy, pharmacotherapy, and bariatric procedures as adjunctive or escalating options. While traditional “one-size-fits-all” dietary approaches frequently yield suboptimal long-term outcomes, precision nutrition has emerged as a promising strategy for individualized obesity care. This review critically appraises the evidence underlying each pillar of precision nutrition: genetic profiling shows clear utility in monogenic obesity but variable benefit when diet is matched to common polygenic variants; microbiome-targeted studies reveal that the Bacillota to Bacteroidota ratio shows inconsistent associations with obesity across studies, with genus-level and functional alterations offering more reproducible targets; and metabolomic profiling of postprandial glycemic responses has progressed from foundational predictive algorithms to large-scale clinical validation. It also highlights chrono-nutrition, the alignment of food timing with individual chronotype, as an emerging pillar of personalization. Beyond dietary composition, the review addresses how personalized nutrition can mitigate the adverse effects of incretin-based pharmacotherapy and discusses how artificial intelligence can integrate multi-omics and behavioral data into real-time dietary guidance. Future clinical implementation will require overcoming challenges related to data integration, predictive accuracy, and accessibility, paving the way for more effective, evidence-based obesity management. Full article

Background: Although advances in critical care have improved short-term outcomes, sepsis survivors continue to face substantial chronic morbidity and impaired long-term survival. Conventional threshold-based tools such as the Sequential Organ Failure Assessment (SOFA) and Modified Early Warning Score (MEWS) show moderate and variable discrimination across cohorts. Reported areas under the receiver operating characteristic curve (AUROCs) must therefore be interpreted in relation to the population, prediction horizon, and outcome used in each study rather than as direct head-to-head comparisons. Objectives: This review evaluates how artificial intelligence (AI) could be linked with dynamic plasma metabolites, particularly citrulline and β-hydroxybutyrate (3-HB), to support biologically informed sepsis phenotyping, while critically examining mechanistic evidence, clinical limitations, and translational readiness. Data Synthesis: Machine-learning and natural language processing architectures have shown promising discrimination in many early-detection studies, with pooled AUROCs near 0.87 and reported prediction windows extending to 48 h. However, performance estimates vary with cohort composition, outcome definition, and validation design, and they should not be ranked against unrelated biomarker studies. Human sepsis studies generally associate low or persistently low citrulline with impaired intestinal function and organ injury, but no sepsis-specific decision cutoff has been externally validated. For 3-HB, an AUROC of 0.8429 for septic liver injury was derived from a cohort of 57 patients and has not been shown to add value beyond routine liver tests or illness-severity measures. Murine experiments provide mechanistic hypotheses for ketone-mediated organ protection, but model-specific and sometimes opposing nutritional effects limit direct translation. These metabolites are therefore best considered candidate longitudinal features for multimodal phenotyping rather than stand-alone clinical triggers. Conclusions: Biologically informed algorithmic surveillance is a promising direction, but clinical implementation requires prospective serial sampling, explicit adjustment for renal, hepatic and nutritional confounders, head-to-head comparison with routine markers, and external validation of calibration and clinical utility. Until these requirements are met, citrulline and 3-HB should support research phenotyping rather than direct treatment selection. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

Chinese wolfberry (CW) is a medicinal-food homologous fruit. Key mineral elements are important indicators of the nutritional quality and geographical authenticity of CW. Rapid and non-destructive evaluation of key mineral elements remains challenging because inductively coupled plasma mass spectrometry (ICP-MS) is accurate but destructive and laboratory-dependent. In this study, 120 CW batches from Ningxia, Qinghai, Gansu, and Xinjiang were analyzed by combining ICP-MS reference measurements of Cu, Fe, Mn, and Zn with standardized RGB image features. Visible-color proxy curves were reconstructed from RGB/L*a*b* information and Gaussian fitting, and frequency-domain texture descriptors were extracted from Fourier-derived angle–energy curves. Multivariate analysis showed that mineral profiles, the 570–593 nm visible-color proxy band, and the 0–40° and 63–140° texture-angle ranges contributed to regional differentiation. A Dung Beetle Optimizer-radial basis function (DBO-RBF) regression model was then used for internal prediction of mineral element contents and compared with nine baseline regression algorithms. DBO-RBF achieved test-set R² values of 0.949, 0.965, 0.951, and 0.913 for Cu, Fe, Mn, and Zn, respectively, with corresponding test RPD values of 2.9017, 3.2419, 3.0175, and 2.8416. These results indicate that image-derived visible-color and texture features can provide useful screening information for mineral quality assessment in CW, offering a rapid, low-cost, and non-destructive approach for preliminary batch evaluation. Full article

Chronic kidney disease (CKD) is common in older cats, and nutritional management remains the intervention with the strongest clinical evidence. Interest has expanded from conventional control of phosphorus and uremic signs to the gut–kidney axis, in which renal dysfunction may alter the intestinal environment while microbial metabolism generates solutes that accumulate as kidney function declines. This PRISMA-ScR-guided scoping review and critical narrative synthesis evaluates feline evidence on CKD-associated dysbiosis, gut-derived uremic solutes, bile acid metabolism, and microbiome-directed nutrition. The original search covered January 2021 to April 2026, with inclusion of earlier studies and targeted source verification through July 2026. Eligibility was organized into two evidence strata: core feline evidence and contextual evidence. The final revised evidence map comprised 49 unique sources (28 empirical feline sources and 21 contextual sources spanning comparative, methodological, guideline, regulatory, or safety evidence); non-feline evidence was used only for mechanistic, methodological, safety, or translational interpretation and was not pooled with feline clinical outcomes. Cats with CKD have shown lower fecal microbial richness and diversity and higher circulating indoxyl sulfate in small cross-sectional cohorts, but causality remains unproven. Recent work also identified altered secondary bile acids and lower fecal ursodeoxycholic acid; however, Peptacetobacter hiranonis is principally linked to bai-mediated 7α-dehydroxylation, whereas ursodeoxycholic acid formation requires distinct hydroxysteroid dehydrogenase reactions. Complete therapeutic renal diets improve clinical outcomes, but their benefits reflect multiple simultaneous modifications and cannot yet be decomposed into a microbiome-specific effect. Evidence for isolated prebiotics, probiotics, postbiotics, synbiotics, fecal microbiota transplantation, and precision nutrition algorithms is preliminary or absent in feline CKD. The field is constrained by small cohorts, confounding, reliance on 16S rRNA sequencing, compositional data, and limited interlaboratory reproducibility. Current clinical practice should therefore prioritize a palatable complete renal diet, adequate energy and protein intake, muscle condition monitoring, hydration, and constipation management, while microbiome-directed products remain adjunctive or investigational. Full article