Search Results (1,719)

Background: Childhood malnutrition, manifesting as both underweight and obesity, is a global health concern with potential repercussions on neurodevelopment and metabolic health. Objective: To analyze the relationship between nutritional status, metabolic biomarkers, and cognitive performance in school-aged children. Methods: A cross-sectional study was conducted with 100 children between 6 and 12 years of age from a public elementary school in the municipality of Chiconcuac de Juárez, Mexico. Participants were categorized according to BMI: underweight (UW), normal weight (NW), overweight (OW), and obesity (OB). Anthropometric evaluation, serum biochemical markers, and three surrogate metabolic indices, namely the Triglyceride–Glucose (TyG), Triglyceride/high-density lipoprotein cholesterol (TG/HDL), and TyG-Body Mass Index (TyG-BMI), were calculated. Cognitive performance was assessed using the Wechsler Intelligence Scale for Children (WISC-IV). Results: The OB group children showed significantly higher levels of TG, TC and LDL-C, as well as elevated levels of TyG, TG/HDL and TyG-BMI indices (p < 0.05) and lower HDL-C concentration. While no significant differences were found in Full-Scale IQ (FSIQ), the NW group showed significantly higher performance in the PSI compared to all other groups outside the healthy weight range after FDR correction. Spearman’s correlation showed that surrogate metabolic indices exhibited exclusive negative correlations with the PSI in unadjusted bivariate models. Conclusions: The extremes of the nutritional status spectrum (UW and OB) are concurrently associated with early metabolic alterations and latent cardiovascular risk, while concurrently tracking with lower performance in selective fluid cognitive domains within unadjusted models. Furthermore, surrogate metabolic indices were shown to be valuable tools that co-vary with neurocognitive profiles. Full article

Food contamination by Aflatoxin M1 and Atrazine remains a critical food-safety concern, requiring sensitive detection methods that can operate reliably in complex matrices. Here, we report an AI-assisted antibody-functionalized electrochemical sensing platform for the detection and classification of Aflatoxin M1 and Atrazine across corn, corn flour, and protein matrices. The sensor used analyte-specific antibodies immobilized on an electrochemical electrode surface, where target binding produced measurable changes in the interfacial electrochemical response. Sensor performance was evaluated using cyclic voltammetry, coulometry, and electrochemical impedance spectroscopy (EIS), with EIS providing strong frequency-dependent signatures for concentration-dependent analysis. Spike-and-recovery studies further demonstrated the applicability of the platform in food-matrix conditions. To improve interpretation of complex electrochemical signals, full-spectrum EIS features were integrated with machine learning models for concentration-level classification into low, mid, and high groups. The AI workflow achieved an overall classification accuracy of 93.33%, with 96.67% specificity, 93.44% PPV, 96.66% NPV, and 0.982 AUC for Atrazine, and 96.70% specificity, 93.38% PPV, 96.67% NPV, and 0.987 AUC for Aflatoxin M1. In addition, analyte classification between Aflatoxin M1 and Atrazine reached 97.4% accuracy and 0.994 ROC-AUC. Overall, this work demonstrates a matrix-aware electrochemical immunosensing strategy enhanced by AI-based signal interpretation for food contaminant detection. Full article

The rapid expansion of molecular point-of-care (POC) diagnostics into decentralized settings, including emergency departments, retail pharmacies, and home environments, has shifted the burden of diagnostic performance from laboratory professionals to heterogeneous, often non-expert users. While traditional evaluation frameworks focus on analytical and clinical validity, they often overlook the impact of human-system interactions on real-world reliability. This review introduces the concept of Operational Validity: the ability of a diagnostic system to preserve its intended performance when operated by intended users within the constraints of real-world workflows and environments. To establish a rigorous foundation for this concept, this study provides a critical comparative analysis contrasting Operational Validity against traditional clinical evaluation dimensions (analytical validity, clinical validity, and clinical utility) and post-market metrics. While existing literature outlines isolated usability principles, the significance of this study lies in its synthesis of these fragmented concepts into a formalized, lifecycle-based “Operational Validity” framework that explicitly maps the causal mechanisms connecting initial user interaction directly to downstream clinical outcomes. By synthesizing international standards (IEC 62366-1) alongside the newly finalized May 2026 U.S. Food and Drug Administration (FDA) guidance on the Content of Human Factors Information in Medical Device Marketing Submissions, we examine how human factors engineering (HFE) and usability engineering serve as the methodological foundation for operational validity. We analyze the specific complexities of molecular workflows, identify key parameters of use-related failure modes in pre-analytical and interpretation stages, and detail the mandatory role of iterative formative and final summative usability testing in mitigating these risks. Finally, we propose a lifecycle-based approach to HFE that integrates design, simulated-use validation, and post-market surveillance. Establishing operational validity is essential to ensure that the high analytical sensitivity of molecular POC platforms translates into consistent clinical utility across the full spectrum of decentralized care. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Accurate apple origin identification and non-destructive internal quality evaluation are important for fruit traceability, quality grading, and post-harvest management. Unlike previous studies mainly focusing on origin classification, this study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples. Samples were collected from three representative production regions in China: Alar in Xinjiang, Yantai in Shandong, and Luochuan in Shaanxi. Near-infrared diffuse reflectance spectra were acquired from 375 apples, generating 3000 spectral samples for origin classification and 750 SSC-calibrated samples for sugar content prediction. For classification, six deep learning models were evaluated using standardized full-spectrum input without chemometric spectral preprocessing, and the Transformer achieved the best performance, with a test accuracy of 96.22%. For SSC regression, spectra were preprocessed using standard normal variate and Savitzky–Golay filtering. The DNN model achieved the best prediction performance, with MAE = 0.5958 °Brix, RMSE = 0.7333 °Brix, R² = 0.8646, and Pearson r = 0.9338. These results indicate that near-infrared spectroscopy combined with deep learning can support both Fuji apple origin authentication and non-destructive local tissue SSC assessment. Full article

Introduction: Teprotumumab (Tepezza^®), an insulin-like growth factor-1 receptor (IGF-1R) antagonist, is the first FDA-approved targeted therapy for thyroid eye disease (TED). While effective for reducing proptosis and inflammation, increasing post-marketing evidence has linked teprotumumab to auditory adverse events. IGF-1 signaling is essential for cochlear maintenance and neuroprotection; therefore, systemic IGF-1R inhibition presents a biologically plausible mechanism for ototoxicity. Despite growing recognition of these effects, no standardized approach exists for audiologic assessment or monitoring of patients receiving teprotumumab. This review aimed to (1) summarize proposed mechanisms and the reported spectrum of teprotumumab-related auditory effects, (2) evaluate current methods used to assess and monitor these patients, and (3) identify areas of consensus and ongoing uncertainty. Methods: An updated narrative review of the literature was conducting using PubMed, CINAHL, and Google Scholar using Boolean strings targeting teprotumumab exposure and hearing-related outcomes. Studies from 2022 onward were identified using Boolean search strings targeting teprotumumab exposure and hearing-related outcomes. Peer-reviewed English language studies reporting audiometric findings were eligible for inclusion. Results: Ten studies met inclusion criteria. Reported effects most commonly included bilateral high-frequency SNHL, tinnitus, and aural fullness, typically emerging after three to six infusions. Many cases demonstrated persistent deficits despite drug discontinuation. Baseline audiometric assessment was not uniformly reported across studies, and monitoring protocols varied considerably, with inconsistent incorporation of speech testing and immittance measures. Conclusions: Teprotumumab-associated ototoxicity is increasingly recognized and potentially irreversible. Current evidence is insufficient to guide standardized monitoring. Prospective studies are urgently needed to establish evidence-based audiologic surveillance protocols. Full article

Temperature-dependent luminescence of Pr³⁺-doped materials was investigated using both conventional luminescence intensity ratio (LIR) and principal component analysis (PCA)-based thermometry. Three host matrices with distinct structural properties, LiLaP₄O₁₂, YNbO₄, and Y₂O₃, were selected to evaluate the influence of crystal structure on thermometric performance. Temperature-resolved emission spectra recorded over the 103–523 K (−170 to 250 °C) range were analyzed using both approaches, with the first principal component (PC₁) serving as a thermometric parameter in the PCA. The results show that crystal symmetry and site multiplicity strongly influence the temperature-dependent spectral evolution and, consequently, the thermometric response. LiLaP₄O₁₂ exhibits stable and well-defined spectral evolution, resulting in balanced thermometric accuracy and resolution. YNbO₄ shows enhanced sensitivity to temperature variations due to increased spectral complexity and stronger crystal-field effects, leading to improved resolution but increased calibration uncertainty. In contrast, Y₂O₃ exhibits reduced thermometric performance due to overlapping emissions from multiple crystallographically inequivalent sites with distinct thermal responses. Compared to LIR, PCA provides improved thermometric figures of merit, particularly in systems with complex and strongly overlapping emission bands, demonstrating the potential of full-spectrum analysis in luminescence thermometry. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

Integrating semi-transparent photovoltaics (STPV) into greenhouse structures offers an effective approach to optimizing the Food–Energy Nexus and maximizing sustainable land-use efficiency. However, a knowledge gap remains regarding how specific STPV spectral signatures drive plant morpho-physiological acclimation across multiple cultivation cycles. This study presents a 19-month multi-cycle, proof-of-concept evaluation of the structural growth dynamics and physiological responses of generative (tomato) and vegetative (lettuce) crops under greenhouse prototypes with two distinct thin-film STPV technologies: Cadmium Telluride (CdTe) and amorphous Silicon (a-Si), compared to an unshaded transparent control. Biometric monitoring revealed that morphological acclimation (Shade-Avoidance Syndrome) was highly plastic, driven by the interplay between spectral filtering and seasonal irradiance limits. While structural adaptations, such as foliar expansion and stem elongation under the a-Si spectrum, were pronounced during specific transitional seasons (e.g., early spring), these morphological differences largely homogenized across treatments during periods of extreme high or low natural irradiance. Despite the shading penalty, this morphological acclimation successfully sustained agronomic fresh mass. Systemic efficiency, quantified by the Land Equivalent Ratio (LER) as a relative biophysical synergy index, demonstrated notably crop-specific synergies. Under an extended single fruiting cycle, the CdTe prototype showed potential to improve yield, achieving a maximum LER of 1.66 for the high-light-demanding tomato (Y_crop = 1.40). Conversely, the a-Si module excelled with the shade-tolerant lettuce during early vegetative stages in high-radiation periods, achieving peak LERs up to 1.55. These findings provide a biophysical baseline to help guide future scalability assessments prior to full-scale commercial agrivoltaic (APV) implementation for sustainable food systems. Full article

Background/Objectives: Antimicrobial resistance in pediatric infections presents a worsening global public health challenge, with antimicrobial resistance (AMR) accounting for more than one million deaths annually and disproportionately affecting children younger than 5 years of age. Neonates and critically ill children face heightened risk owing to immature immunity, frequent healthcare exposures, and limited therapeutic options. This review synthesizes evidence on the epidemiology, mechanisms of resistance, clinical outcomes, and management of AMR across the full pediatric age range. Methods: PubMed/MEDLINE and Google Scholar were searched for literature from 2014 to 2026 using terms covering antibiotic resistance, pediatric populations, and key pathogens. Approximately 1840 records were screened; 69 sources met all inclusion criteria. A narrative synthesis approach was used, given heterogeneity across study designs and outcomes. Results: Extended-spectrum β-lactamase (ESBL)-producing Enterobacterales, carbapenem-resistant pathogens, and methicillin-resistant Staphylococcus aureus drive substantial morbidity and mortality in children. Approximately one in five pediatric Gram-negative bloodstream isolates are resistant to third-generation cephalosporins, a phenotype independently associated with a roughly three-fold increase in adjusted mortality. Carbapenem-resistant Klebsiella pneumoniae bacteremia carries a 30-day mortality approaching 40%, and isolates in low- and middle-income countries (LMICs) frequently harbor multiple resistance genes. Pneumococcal conjugate vaccine implementation was associated with absolute reductions of 7–11% in the proportion of pediatric pneumococcal isolates that were penicillin-non-susceptible or penicillin-resistant, largely by preventing infections caused by resistant serotypes and by reducing antibiotic selection pressure, rather than through a direct effect on resistance mechanisms; global AMR mortality in children younger than 5 years of age fell by more than 50% between 1990 and 2021. Conclusions: Pediatric AMR reflects intersecting microbiological, clinical, and health-system challenges. Priority actions include scaling antimicrobial stewardship programs, expanding access to rapid molecular diagnostics, integrating whole-genome sequencing into surveillance, conducting pediatric-inclusive randomized trials, and deploying vaccines as primary prevention tools, with particular emphasis on LMICs where the burden is greatest. Full article

Wild carrot (Daucus carota subsp. carota), the wild relative of cultivated carrot, is globally identified as an invasive weed that threatens hybrid carrot seed production through natural cross-pollination, resulting in compromised genetic purity. Manual identification across the large areas required to ensure genetic purity in carrot seed crops is impractical. Remote sensing offers an alternative; however, morphological similarities among wild carrot, cultivated carrot, and common weeds hinder reliable detection. Early identification, however, remains essential for preventing genetic contamination. This study evaluated leaf-level hyperspectral reflectance spectroscopy (400–2450 nm) with machine learning to discriminate wild carrot from hybrid carrots, parental lines, and 19 co-occurring weed species. Spectral data from 266 wild carrot plants across three New Zealand sites and six weeks (5–10 weeks after emergence) showed negligible spatial effects (R² = 0.034–0.055, pseudo-F = 1.46–2.39, p > 0.05) and moderate temporal variation (R² = 0.136–0.151, pseudo-F = 5.48–6.17, p < 0.001), indicating broadly stable spectral signatures suitable for model generalization. Savitzky–Golay filtering, with min–max normalization outperformed SNV, yielding high full-spectrum accuracies for wild carrot vs. other species (90.35%, κ = 0.80), wild carrot vs. weeds (96.03%, κ = 0.92), and a multi-class model (90.79%, κ = 0.88). After removing atmospheric water-absorption bands to follow airborne sensing, reduced-band models based on airborne-compatible wavelengths maintained strong performance, including 89.40% accuracy (κ = 0.79) for wild carrot vs. weeds using a 20-band Subspace Discriminant model (400–402, 527, 705–720 nm). These findings demonstrate that stable wild carrot spectra and carefully selected visible and red-edge bands can underpin cost-effective UAV/UGV-mounted hyperspectral or multispectral sensors for site-specific wild carrot management. Full article

Background/Objectives: IL23R encodes a pivotal component of the IL-23/Th17 signaling axis and represents a validated genetic susceptibility locus for inflammatory bowel disease (IBD), psoriasis, and ankylosing spondylitis. Despite extensive GWAS data, the functional consequences of the full spectrum of IL23R missense single-nucleotide variants (SNVs) have not been systematically characterized. This study aimed to identify high-risk missense SNVs through a multi-tool in silico pipeline. Methods: A total of 723 missense SNVs from NCBI dbSNP were verified against transcript NM_144701.3/Q5VWK5-1 (629 aa) using Ensembl VEP (GRCh38). Sequential filtering was performed using applied SIFT, PolyPhen-2, PROVEAN, E-SNPs&GO, MutPred2, and ConSurf (grade ≥ 7); AlphaMissense and FATHMM-MKL were used as independent annotation layers. Protein stability was assessed with MuPro and DynaMut2 (AlphaFold2 AF-Q5VWK5-F1-v6; pLDDT = 68.19); structural characterization was performed with Project HOPE, and interaction networks were constructed using STRING and GeneMANIA. Results: Sequential filtering identified 37 high-risk missense variants. MuPro predicted destabilizing effects for 36/37 variants, with concordant DynaMut2 results for 35/37. Project HOPE identified disulfide bond disruption in 11 variants, charge-altering substitutions in 8, and glycine/proline backbone conformational changes in 11. STRING analysis identified IL12RB1 (0.999), IL23A (0.999), JAK2 (0.995), IL12B (0.986), and STAT3 (0.980) as the leading IL23R interactors. The protective variant R381Q was appropriately characterized as neutral by PROVEAN (−1.16) and AlphaMissense (likely_benign), supporting the specificity of the pipeline. Conclusions: Comprehensive in silico analysis identified 37 high-risk IL23R missense candidates with convergent computational evidence of predicted deleteriousness, predominantly involving cysteine bridge disruption, charge alteration, and glycine/proline backbone conformational changes. These variants are presented as prioritized candidates for future functional validation and may inform subsequent investigations of IBD susceptibility and IL-23 pathway pharmacogenomics. Full article

This study proposes a hybrid machine learning model based on full-spectrum pulsed neutron logging data to address the monitoring challenges of Carbon Capture, Utilization, and Storage (CCUS) under complex geological conditions. Traditional interpretation models for sequestered CO₂ saturation (e.g., macroscopic capture cross-section model, characteristic peak count model, and ratio model) heavily rely on prior parameters such as porosity, formation water salinity, and lithology. Acquiring these parameters in real time during practical engineering is often costly and difficult. To reduce the rigid dependence of accurate CO₂ saturation monitoring on complex prior parameters like porosity and salinity under heterogeneous geological settings, this research focuses on the Pearl River Mouth Basin, a core carbon sequestration target area in the Guangdong-Hong Kong-Macao Greater Bay Area, based on the evaluation results of offshore carbon sequestration macro-regions in China. Taking the primary reservoirs of the Enping and Wenchang Formations as typical geological prototypes, a high-fidelity, full-spectrum neutron–gamma response database was constructed using Monte Carlo simulations. Two machine learning strategies are proposed: a direct regression model (NMF+SVR) and a joint model (NMF+SVC/KMeans+SVR). Based on Monte Carlo simulated data, experimental results demonstrate that, compared with traditional petrophysical baseline models and simple machine learning models, the proposed joint learning method effectively reduces the dependence of CO₂ saturation monitoring on lithology and porosity. Furthermore, it is proven that even with a single-detector tool configuration, the method exhibits high prediction accuracy under complex lithological conditions. Notably, the two-step joint model achieves a Root Mean Square Error (RMSE) as low as 4.200%, significantly outperforming traditional physics-based models and single machine learning models such as MLP and RF. This study provides a physically interpretable and accurate technical reference for applying machine learning to pulsed neutron-logging-based CO₂ geological sequestration monitoring. Full article

The escalating global threat of antimicrobial resistance (AMR) has intensified efforts to identify safe, effective, and sustainable alternatives to in-feed antibiotics in livestock production. The bovine gastrointestinal microbiome plays a central role in host immunity, nutrient utilization, and disease resilience, positioning microbiome-modulating interventions as promising candidates for antimicrobial stewardship. Despite growing experimental interest, a systematic synthesis of the available evidence in cattle is lacking. This systematic review aimed to evaluate the efficacy of microbiome-modulating interventions, including probiotics, prebiotics, postbiotics, phytogenic feed additives, essential oils, organic acids, and native rumen microbial supplements, as strategies to reduce antimicrobial use in cattle, and to characterize their effects on gut microbial diversity, fermentation characteristics, and host health and performance outcomes. A systematic search of Scopus, Web of Science, and EBSCOhost (including Academic Search Ultimate, MEDLINE with full text, and CAB Abstracts with Full text) was conducted in accordance with PRISMA guidelines. Studies were eligible if they used cattle (dairy cattle, beef cattle, calves, or mixed production systems), employed a microbiome-modulating intervention, and reported at least one microbiological or host outcome. Seventeen peer-reviewed studies published between 2010 and 2025 were included after full-text screening. Risk of bias was assessed using an adapted SYRCLE tool, which identified moderate overall study quality; the majority of included studies were randomized controlled trials or controlled experiments, though reporting of allocation concealment and blinding was inconsistent across studies. Across the 17 included studies, five broad categories of interventions were evaluated: probiotics (n = 5 studies), prebiotics (n = 2), postbiotics and organic acids (n = 4), phytogenic additives and essential oils (n = 4), and native rumen microbial supplements (n = 2). Animals spanned neonatal dairy calves, weaned Holstein calves, dairy heifers, lactating dairy cows, and Bos indicus feedlot beef cattle. Probiotics and organic acids most consistently improved growth performance: benzoic acid supplementation increased average daily gain by 8.4% (p < 0.05) and fructo-oligosaccharide prebiotics elevated body weight at weaning by 6.7% (p < 0.01). Native rumen microbial supplements improved energy-corrected milk yield by up to 3.1% without increasing dry matter intake. Polyphenols and bile acids demonstrated the strongest immunological and disease-preventive effects, reducing calf mortality by approximately 40% and disease severity by approximately 35%, respectively. Microbiome analyses revealed intervention-dependent increases in microbial diversity and shifts toward taxa associated with improved fermentation efficiency, including enrichment of propionate-producing Prevotellaceae, butyrate-associated Ruminococcus, and hindgut Bifidobacterium. Rumen fermentation outcomes included reductions in the acetate:propionate ratio and ammonia-N concentrations and improvements in fiber digestibility of 3.6–4.4 percentage units in dairy cows. Phytogenic additives preserved microbial diversity without inducing broad-spectrum suppression, functioning primarily as microbiome stabilizers rather than direct antimicrobial replacements. This systematic review provides evidence that gut microbiome modulation may enhance growth performance, improve fermentation efficiency, and reduce disease susceptibility in cattle, thereby supporting antimicrobial use reduction across dairy, beef, and mixed production systems. Effect magnitudes varied substantially across intervention categories and production contexts, and study quality was moderate, underscoring the need for larger, pre-registered trials with standardized outcome reporting and direct antibiotic comparator arms. Probiotics, prebiotics, and bile acid metabolites showed the greatest potential as components of integrated antimicrobial stewardship strategies in cattle production. Full article

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency. Full article