Search Results (557)

The rhizosheath is a dynamic plant–soil interface in which root traits, microbial activity, and soil physical properties jointly regulate plant adaptation to drought and nutrient limitation. Despite the growing interest in this field, it remains conceptually fragmented. This study mapped the development, structure, and emerging directions of rhizosheath research at the intersection of microbiome interactions, stress adaptation, and root-trait genetics. Bibliometric and science-mapping analyses were performed on 136 publications (2015–2026) retrieved from the Web of Science Core Collection. Using the Bibliometrix framework, we examined publication dynamics, collaboration networks, citation patterns, keyword co-occurrence, and thematic structures. The dataset comprised 136 publications, 6366 cited references, and 723 authors, with 54.41% international co-authorship. Logistic modeling described the accumulation of publications through 2025, identifying a growth inflection at 2022.54; a reliable saturation level could not be estimated because the 2026 data cover only a partial year. Document coupling resolved nine clusters dominated by soil–root interface processes (n = 51; 1358 citations) and plant–microbe interactions (n = 18; 895 citations). Thematic analysis positioned soil and rhizosheath as central domains and identified water stress as a key motor theme, whereas mucilage, hydraulic functioning, and microbiome assembly emerged as recent trends. Rhizosheath research is transitioning from descriptive characterization toward more integrated, mechanistic perspectives linking root traits, soil processes, and microbial dynamics. Progress will depend on resolving genotype × soil × microbiome interactions and advancing field-based cross-scale phenotyping to support climate-resilient cropping systems. Full article

Conventional design paradigms for vehicle underbody armor face an inherent trade-off: enhancing blast protection invariably incurs a prohibitive weight penalty. Here, we investigate a mechanical pre-stressing strategy for high-strength steel V-shaped vehicle underbody structures. A conventional V-shaped baseline structure was first subjected to a 6 kg TNT blast test, and the measured response was used to validate the numerical model. Based on the validated numerical model, four mass-equivalent (100 kg) configurations were subsequently compared numerically under escalating threats (2~8 kg TNT): pre-stressed steel, homogeneous steel, and all-metallic honeycomb sandwich panels (comprising high-strength steel face sheets and an aluminum alloy core) with both positive and negative Poisson’s ratios. The numerical results predict that the pre-stressed steel configuration exhibits the smallest maximum permanent floor deformations among the four configurations, with values of 22 mm, 46 mm, 131 mm, and 208 mm under 2, 4, 6, and 8 kg loads, respectively. Mechanistically, we reveal that for V-shaped geometries, residual-stress-induced stiffening and geometric arching are profoundly more effective than core crushing in controlling global bending, while the auxetic steel-faced aluminum honeycomb offers only marginal improvements over its conventional counterpart. This study offers a potential pathway for overcoming the weight–protection trade-off in underbody armor design. While the numerical predictions are encouraging, direct experimental validation of the pre-stressed configuration remains necessary prior to practical application. Full article

GLP-1 receptor (GLP-1R) agonists are increasingly investigated in epilepsy, but antiseizure activity, neuroprotection, and disease modification are distinct therapeutic claims. This critical narrative review with structured evidence mapping separates these claims across 21 preclinical primary publications and eight human studies identified through 6 August 2026. Selected GLP-1R-related interventions show antiseizure and anti-kindling signals, but effects vary across compounds, models, treatment timing, and seizure types; null and pro-seizure findings in absence epilepsy preclude a uniform class-wide antiseizure effect, and concurrent anti-kindling does not establish antiepileptogenesis. Neuroprotective evidence is broader, although direct neuronal or tissue preservation is demonstrated only in selected studies; many findings remain biomarker-based, and seizure reduction may itself lessen downstream injury. Evidence for durable disease modification remains suggestive rather than established. Causal support is strongest at the receptor level, whereas most downstream synaptic, inflammatory, glial, oxidative, and mitochondrial evidence remains associative. Semaglutide has high translational relevance but remains directly under-tested in epilepsy, and human evidence is predominantly observational or safety-oriented and does not establish therapeutic epilepsy efficacy. Progress requires chronic epilepsy studies with longitudinal EEG/video-EEG and baseline seizure burden, post-insult designs controlling initial-insult severity and assessing persistence after withdrawal, and linked pharmacokinetic, target-engagement, and causal mechanistic testing. Full article

Cardiovascular risk assessment remains a central challenge in both population-based prevention and high-risk clinical settings. We benchmarked a previously developed mechanistic model of cardiovascular ageing against machine-learning baselines across two distinct cohorts and evaluated both discrimination and probability calibration. The mechanistic model was based on an ordinary differential equation (ODE) framework and adapted to the observable interaction structure of two independent public datasets: the Framingham Heart Study cardiovascular risk dataset, with 10-year coronary heart disease as the outcome, and the Heart Failure Clinical Records dataset, with mortality as the outcome. ElasticNet logistic regression and XGBoost were evaluated as cohort-specific machine-learning benchmarks. Predictive performance was assessed using repeated stratified five-fold cross-validation with three repeats. In the Framingham cohort (n = 4240; 644 events), ElasticNet achieved ROC-AUC = 0.727 (95% CI 0.705–0.747), PR-AUC = 0.343, and Brier score = 0.116, while XGBoost achieved ROC-AUC = 0.715 (95% CI 0.694–0.737), PR-AUC = 0.325, and Brier score = 0.118. The observable mechanistic score showed weaker discrimination, with ROC-AUC = 0.547 and PR-AUC = 0.202. In the Heart Failure cohort (n = 299; 96 deaths), ElasticNet and XGBoost achieved ROC-AUC values of 0.770 and 0.771, respectively, whereas the mechanistic score achieved ROC-AUC = 0.534. Post hoc calibration improved probability scaling of the mechanistic score but did not restore discriminative performance. Overall, cohort-specific machine-learning models demonstrated stronger discrimination, while the external applicability of the mechanistic framework depended on the alignment of available predictors and clinical endpoints in the target cohort. Full article

The sensitivity of classical electrochemical biosensors is constrained by noise processes at the electrode–electrolyte interface: low-frequency 1/f noise, thermal noise and capacitance fluctuations degrade the signal-to-noise ratio in ways that circuit-level mitigation reduces but does not remove. Chirality-Induced Spin Selectivity (CISS) has been proposed as a route past that limit, by shifting transduction from the scalar quantity of charge to the vector property of electron spin. Spin polarizations of up to approximately 60% have been reported at room temperature for double-stranded DNA monolayers in spin-resolved photoemission, while spin-dependent electrochemistry on smaller chiral adsorbates typically yields values in the range of about 5–30%; the reported magnitude is therefore system-, geometry-, technique- and analysis-dependent rather than a universal property of biological helices. Analyte binding modulates this efficiency through changes in helical pitch, dipole and structural integrity. This review unites the physics of CISS with the surface chemistry of spin-selective sensor layers, compares the competing mechanistic accounts of the effect, and then examines a persistent quantitative gap: the polarizations obtained from first-principles transport calculations on isolated chiral molecules remain well below the measured values. Non-relativistic, spin-restricted calculations on closed-shell helices in vacuum yield no polarization by construction, and although spin-polarized and relativistic implementations that treat spin–orbit coupling explicitly are available, they typically still underestimate experiments by orders of magnitude. We argue that a substantial part of this deficit is attributable to the widespread use of static, vacuum-based or implicitly solvated models, and that multiscale quantum mechanics/molecular mechanics (QM/MM) frameworks with explicit solvents are one necessary—though probably not sufficient—correction. Full article

Hepatic ischemia–reperfusion injury (IRI) is a major clinical challenge in liver surgery and transplantation, contributing to postoperative complications and graft dysfunction. The pathogenesis of hepatic IRI is complex and multifactorial, involving ischemia-induced metabolic consequences, oxidative stress, inflammatory responses, endothelial dysfunction, and the activation of immune pathways upon reperfusion. Despite extensive research efforts, the translation of preclinical findings into effective clinical interventions remains limited. This review provides a critical overview of the principal models used to investigate hepatic IRI. Conventional two-dimensional in vitro systems, including monoculture and co-culture models, offer controlled environments for mechanistic studies and high-throughput screening, but fail to fully reproduce the structural and cellular complexity of the liver microenvironment. Animal models, particularly those based on mice, rats, and pigs, remain essential for studying the systemic and multicellular aspects of hepatic IRI. Nevertheless, species-specific physiological differences, ethical concerns, high costs, and limited translational predictability represent significant limitations. In this context, three-dimensional liver models have emerged as promising alternatives capable of bridging the gap between in vitro systems and animal experimentation. By more accurately recapitulating tissue architecture, cell–cell interactions, and functional heterogeneity, 3D platforms offer enhanced physiological relevance and translational potential. We discuss the strengths and limitations of each experimental approach and highlight the role of advanced 3D models as complementary tools that may enable more accurate investigations of hepatic IRI and accelerate the development of effective therapeutic strategies. Full article

Methane production from water-bearing coal reservoirs is governed not only by methane adsorption capacity but also by the accessibility of adsorption domains and the efficiency of pore-scale transport during pressure depletion. However, the interfacial mechanism by which coal wettability and water occurrence regulate methane adsorption–desorption reversibility remains insufficiently understood. In this study, three representative Ordos Basin coal samples with different pore structures and surface polarities, denoted as OBC-L, OBC-M, and OBC-H, were investigated to explore the pore-scale mechanisms governing water-mediated methane storage and release rather than to establish basin-wide statistical relationships. A combined experimental workflow involving N₂ adsorption–desorption, FTIR and XPS analyses, contact angle and Zeta potential measurements, low-field NMR, high-pressure methane adsorption–desorption tests, kinetic modeling, hysteresis evaluation, and Pearson correlation analysis was used to clarify the coupling among pore structure, coal–water interfacial properties, water occurrence, methane storage, and methane release. The results show that OBC-H possesses the strongest dry-state methane storage potential, with the BET surface area increasing from 5.82 m²/g for OBC-L to 12.94 m²/g for OBC-H and the fitted Langmuir volume (V_L) reaching 22.3 cm³/g. Nevertheless, OBC-H also shows stronger water affinity, as reflected by an increase in the XPS-derived O/C atomic ratio from 0.118 to 0.186, a decrease in contact angle from 82.6° to 51.8°, and an increase in bound water fraction from 46.3% to 69.4%. With the transition from dry to saturated conditions, the fitted V_L of OBC-H decreases from 22.3 to 15.2 cm³/g, while the Langmuir pressure (P_L) increases from 1.38 to 3.00 MPa, indicating a simultaneous reduction in the model-estimated maximum methane adsorption capacity and apparent methane affinity. More importantly, the desorption results demonstrate that high adsorption capacity does not necessarily correspond to high methane deliverability. For OBC-H, the final desorption efficiency decreases from 79.6% to 54.2%, the effective diffusion coefficient decreases from 2.74 × 10⁻¹¹ to 0.86 × 10⁻¹¹ m²/s, and the hysteresis index increases from 12.8% to 36.4% under saturated water conditions. Correlation analysis further confirms that bound water fraction is positively associated with adsorption–desorption hysteresis but negatively associated with desorption efficiency, desorption rate constant, and effective diffusion coefficient. These findings are consistent with two distinct water-mediated constraints: adsorbed/bound interfacial water contributes to surface-site shielding, whereas capillary and saturated water occupation contributes to pore-throat transport restriction; together, these effects reduce methane release efficiency and enhancing desorption irreversibility. This study provides an interfacial interpretation of methane deliverability based on representative water-bearing coal samples and offers a mechanistic basis for understanding wettability- and water-retention-related transport constraints; broader applicability across the Ordos Basin requires validation using a larger number of samples from different coal seams and reservoir settings. Full article

Objective: Modern high-intensity warfare, such as the Russo-Ukrainian conflict, generates a high incidence of multisystem polytrauma, blast-induced traumatic brain injury, and limb amputations often complicated by post-traumatic stress disorder and chronic pain. Traditional specialised rehabilitation protocols are structurally inadequate for these co-occurring symptoms and rely heavily on medical treatments, increasing the risk of opioid dependency. The primary aim of this review is to conceptualise a multidimensional integrative framework for war-related polytrauma; a secondary aim is to evaluate and summarise the evidence underpinning its key rehabilitation strategies for limited-resource, post-conflict environments. Methods: This narrative review synthesises evidence identified through targeted database searches and purposive selection guided by clinical relevance, methodological quality, and applicability to war-related polytrauma across physical, technological, and non-pharmacological rehabilitation domains. Evidence was evaluated using a four-tier (A–D) hierarchy, comprising Tier A (systematic reviews and RCTs), Tier B (prospective cohort studies and non-randomised controlled trials), Tier C (descriptive, case-series, and retrospective studies), and Tier D (exploratory, mechanism-based, or expert opinion evidence). No systematic inclusion/exclusion protocol was applied, and no claims of comprehensive search coverage are made. Results: Concurrent physical and trauma-focused psychological rehabilitation, evidenced by reduced pain and psychological symptom burden alongside improved functional independence, is supported by high-quality evidence (Tier A) for managing comorbid polytrauma. Robotic exoskeletons and AI-supported tele-rehabilitation demonstrate dose-dependent motor benefits in selected rehabilitation populations (Tier A–B), but their scalability in wartime settings is contingent on electricity supply, connectivity, equipment maintenance, and trained personnel. Acupuncture shows Tier A evidence for neuropathic pain and Tier B cost-effectiveness; evidence for phantom limb pain and PTSD is preliminary (Tier C–D) and requires dedicated RCTs in veteran populations. Ayurvedic herbal interventions are exploratory (Tier C–D) with no war-specific clinical trials; mechanistic plausibility is discussed as hypothesis-generating only. Conclusions: Addressing the war-related rehabilitation gap requires integration of biomedical care, technology-assisted rehabilitation, and evidence-graded non-pharmacological adjuncts within a structured biopsychosocial framework. Future priorities include pragmatic, adapted trial designs (e.g., stepped-wedge or cohort-embedded designs, which are more feasible than classical RCTs under wartime conditions) for acupuncture in phantom limb pain, feasibility trials for robotic rehabilitation in frontline-adjacent facilities, and health-economic modelling adapted to the Ukrainian context. Full article

Rock bolt reinforcement governs underground excavation stability through the combined effects of embedment depth, installation pattern, and interaction with the evolving stress and structural environment. Despite substantial advances across mechanistic, empirical, numerical, discontinuum, dynamic, and field-based research traditions, no unified framework currently integrates these approaches across geological and stress regimes. Current practice, therefore, relies on design methods calibrated within specific contexts, producing optimisation outcomes that are model-dependent, metric-sensitive, and not reliably transferable across site conditions. This review critically synthesises evidence from 30 peer-reviewed studies organised into six analytical categories: mechanistic confinement frameworks, empirical classification systems, numerical parametric investigations, discontinuum- and discrete fracture network (DFN)-based optimisation studies, high-stress and dynamic performance analyses, and field-based performance evaluations. The synthesis establishes three principal findings. First, optimal bolt embedment is stress-regime-dependent; plastic-radius-based design logic is appropriate under moderate static conditions but becomes insufficient under high stress or dynamic loading, where energy absorption capacity and controlled yielding govern performance. Second, in discontinuous rock masses, joint geometry and spacing dominate reinforcement effectiveness, shifting optimisation from uniform length selection toward pattern-specific alignment and multi-length configurations that outperform equal-length grids under DFN-controlled conditions. Third, numerical optimisation outcomes are sensitive to the choice of objective metric and modelling paradigm, such that bolt length and spacing recommendations cannot be transferred across analytical frameworks without explicit mechanism comparison. To integrate these findings, a unified conceptual framework is proposed based on regime classification using three dimensionless indicators: the bolt penetration ratio (Π₁ = L/r_p), which relates embedment to plastic zone radius; the structural interception ratio (Π₂ = S/S_j), which relates bolt spacing to dominant joint spacing; and the stress intensity ratio (Π₃ = σ_in situ/σ_cm), which relates in situ stress to rock mass compressive strength. These indicators identify whether confinement-dominated, structure-dominated, or stress-dominated behaviour governs stability, and direct design logic accordingly. The framework does not prescribe universal geometric thresholds; rather, it provides a structured classification pathway that integrates mechanistic and empirical evidence into a coherent and transferable design logic. Probabilistic validation incorporating geological variability, stochastic fracture network modelling, and iterative field calibration is identified as the necessary development path toward a statistically robust optimisation methodology. Full article

Data silos and topological incompatibility between building information modeling (BIM) geometric models and finite element method (FEM) analysis models in transportation infrastructure engineering represent critical bottlenecks that impede real-time digital twin analysis and the intelligent transformation of the industry. Based on a critical review of existing BIM-to-FEM conversion methods and their limitations, this study proposes a “BIM-FEM” seamless conversion and dynamic twin mapping method that integrates parametric modeling with finite element meshing, with modeling and repair time reduced from 16 h to 3 h, and the maximum element aspect ratio improved from 84.78 to 16.59. In terms of geometric topology, we propose a collaborative construction method in which finite element hexahedral meshing rules drive BIM parametric modeling in reverse. By regularizing the decomposition of axis lines and cross-sectional feature points of linear transportation structures and optimizing their topology, we achieve fully automated hexahedral meshing without topological errors. In terms of mechanical analysis, an “offline pre-solution, online superposition” computational order-reduction model is proposed. This reduces the high-dimensional full-range finite element solution of dynamic traffic loads to a dot product operation between the influence line matrix and real-time load vectors, enabling sub-second computational response under high-concurrency dynamic traffic conditions—specifically, single-point mapping takes less than 0.27 ms, incremental updates are controlled within 0.2 s. In terms of spatiotemporal mapping and system applications, a high-fidelity “FEM-BIM” mapping mechanism based on inverse isoparametric transformation and AABB (Axis-Aligned Bounding Box) spatial indexing has been established, supporting real-time rendering of 3D cloud maps on the web and digital twin applications in engineering. Applications of this method in real-world bridge engineering digital twin systems have demonstrated its ability to perform automatic structural safety assessments and health condition predictions with an overall computation time reduction of approximately 73% compared to conventional approaches. This addresses the shortcoming of traditional structural health monitoring—which emphasizes sensor-based identification over mechanistic evaluation—and provides a viable path for intelligent, precise management and maintenance of transportation infrastructure throughout its entire life cycle. Full article

Background: Sleep disorders, particularly chronic insomnia (Nidranasha), represent an escalating global public health challenge associated with extensive neuropsychological and metabolic morbidities. While conventional pharmacotherapy provides immediate symptomatic relief, concerns regarding dependency, tolerance, and altered sleep architecture necessitate the evaluation of evidence-based complementary interventions. Ayurveda offers a comprehensive multi-modality framework for sleep health through internal adaptogenic herbs (Abhyantar Chikitsa), external oil-based therapies (Bahir Parimarjana), and structured lifestyle regimens (Dinacharya/Ratricharya). This narrative literature review evaluates the current clinical and mechanistic evidence supporting Ayurvedic interventions for sleep disorders. Methods: A focused narrative review was conducted across PubMed; Scopus; Cochrane Central Register of Controlled Trials (CENTRAL); Google Scholar; and Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homoeopathy (AYUSH) Research Portal for literature published between 05/01/2016 and 05/01/2026. Randomized trials, open-label and single-arm clinical studies, mechanistic and phytochemical work, and toxicological and herb–drug interaction studies were eligible. Results: Standardized Withania somnifera root extract is supported by the most rigorous data, including double-blind placebo-controlled trials with polysomnographic or actigraphic corroboration reporting reductions in sleep-onset latency and improvements in sleep efficiency. Evidence for Bacopa monnieri and Valeriana wallichii is more limited and derives largely from studies in which sleep was a secondary outcome. External therapies, notably Shirodhara and Abhyanga, are associated with favorable autonomic and electroencephalographic changes, but the available trials are small, short, and difficult to blind, and non-specific effects of warmth, touch, and therapist attention cannot be separated from any specific treatment effect. Mechanistic work implicates modulation of gamma-aminobutyric acid (GABA) signaling, hypothalamic–pituitary–adrenal signaling, and parasympathetic activation, but these findings are hypothesis-generating and derive largely from in vitro and animal models at concentrations of uncertain human relevance. Conclusions: Ayurvedic interventions show preliminary signals of benefit in insomnia, most consistently for standardized Withania somnifera, but overall certainty remains low because of risk of bias, indirect evidence, poor standardization, and an absence of long-term safety data. Current evidence supports further rigorous investigation and carefully monitored adjunctive use in selected patients rather than general integration into routine care or substitution for established treatment. Full article

Background/Objectives: Dietary nitrate, long framed through food-safety concerns about N-nitroso compound formation, is now also recognised as a substrate of the nitrate–nitrite–nitric oxide pathway. This review aims to define the mechanistic, dietary and host conditions under which nitrate bioactivation becomes functionally relevant, with particular attention to its microbial determinants and to the level of inference the evidence actually supports. Methods: We conducted a narrative review supported by a structured literature search (PubMed, Scopus and Web of Science; 1 January 1976 to 14 February 2026; full-text, peer-reviewed, English-language, human-relevant sources; 148 sources retained, of which 93 contributed to the evidence synthesis), with narrative synthesis of mechanistic, interventional, observational and regulatory sources addressing dietary source and food matrix, enterosalivary metabolism, oral and gut microbial function, and health-related outcomes. A PRISMA-style flow diagram summarises the documented screening and inclusion process, and the complete database-specific search strategies are provided in Supplementary Table S1; no meta-analysis was performed because of substantial heterogeneity in designs and outcomes. Results: Within the canonical enterosalivary pathway, nitrate-to-nitrite bioactivation is predominantly microbiota-dependent and downstream conversion is chemically conditional: within the enterosalivary cycle, nitrate-reducing bacteria on the tongue dorsum generate the nitrite required for downstream nitric oxide formation, and its conversion in the stomach depends on pH and on matrix constituents. Dietary source and food matrix therefore govern both the delivered dose and the chemistry that follows, so vegetables, beetroot products, inorganic salts, drinking water and processed meat are not interchangeable exposure models. The oral microbiota is the principal microbial determinant of the response, whereas the gut microbiota acts as a context-dependent modifier of intestinal redox tone, barrier function and microbial ecology, supported by markedly weaker human evidence. Nitrate-rich sources reproducibly raise nitrate and nitrite biomarkers, with variable effects on blood pressure, vascular function and exercise efficiency, limited or inconsistent effects on cognition, cerebral blood flow and metabolic endpoints, and a safety profile whose interpretation depends on food matrix, dose, exposure pattern and host context rather than concentration alone. Conclusions: We propose the Source–Matrix–Microbiota–Host (SMMH) framework, in which biological impact depends on the interaction between dietary source and dose, food matrix, microbial nitrate-reducing capacity and host susceptibility, rather than on nitrate dose alone, and in which pathway-level, physiological and clinical evidence are kept explicitly distinct. The evidence base is mechanistically robust for the oral microbiota, considerably less defined for the gut microbiota, and variable at the level of validated clinical endpoints; it does not yet support source-independent guidelines or population-level recommendations. Full article

Transport infrastructure constitutes an essential component of the built environment, supporting urban accessibility, economic activity, and the long-term functionality of cities and developed areas. The quality of newly constructed pavements is traditionally assessed through compliance with construction specifications, such as layer thickness, material properties, and compaction requirements. However, these parameters do not directly quantify the influence of construction deviations on long-term pavement structural performance. This paper presents a structural capacity-based framework for pavement construction quality assessment that evaluates construction quality according to its expected impact on pavement service life. The proposed methodology integrates ground-penetrating radar measurements, core sampling, laboratory testing, and mechanistic structural analysis to determine the actual structural capacity of the as-built pavement expressed as the allowable number of Design Axle Loads. Based on these results, the Pavement Construction Quality Index (PCQI) is introduced to quantify the combined effects of systematic and localized construction deficiencies. To prevent severe localized defects from being masked by area-weighted averaging, the Critical Local Defect Indicator (CLDI) is proposed as an independent acceptance criterion. The sensitivity parameters of the PCQI formulation were calibrated using mechanistic analysis and HDM-4 deterioration modelling. The proposed framework was demonstrated through a real pavement reconstruction case study and further examined using Monte Carlo simulation to investigate its numerical behaviour over a broad range of construction non-compliance scenarios. The results demonstrate that the proposed methodology provides a continuous and technically consistent evaluation of pavement construction quality while enabling practical engineering interpretation of different quality levels. The framework offers a structured quantitative decision-support tool for pavement acceptance based on structural capacity rather than solely on compliance with construction tolerances, thereby supporting more reliable management of transport infrastructure as part of the wider built environment. Full article

Confronted with severe global challenges, including water scarcity, excessive use of chemical fertilizers and pesticides, and heavy metal contamination in soils, conventional agricultural technologies exhibit marked limitations in integrated water–fertilizer management and non-point source pollution control. Leveraging their excellent water retention capacity, intelligent sustained-release properties, and environmental responsiveness, hydrogels offer innovative solutions to advance sustainable agricultural development. This review comprehensively outlines the fundamental types, crosslinking mechanisms, and key functional properties of hydrogels, with a focused discussion on their agricultural deployment as high-efficiency soil conditioners, fertilizer vectors, and pesticide carriers; it deciphers the microscopic water-holding mechanisms under the tristate water model, delineates the divergent water-uptake and retention behaviors between ionic and non-ionic hydrogels, and clarifies the cyclic water-holding and release mechanisms of hydrogels during soil amelioration. Thise paper further synthesizes hydrogel-enabled environmental remediation applications, in which heavy metals and pesticide residues in soils and aquatic systems are removed via functional-group coordination adsorption or photocatalytic degradation; concurrently, hydrogels have been shown to activate plant systemic immunity through calcium-signaling pathways, thereby inducing broad-spectrum antiviral defense responses. Moreover, hydrogels can be integrated into precision agriculture frameworks to enable real-time monitoring of crop physiological status and to support targeted irrigation and fertilization management. This work also evaluates the role of hydrogels in promoting seed germination, root system development, crop metabolic regulation, and stress resilience, while introducing tailored application strategies across distinct plant growth stages. Their documented economic advantages include water conservation, enhanced crop yields, reduced dependence on synthetic fertilizers, and lower labor costs. Nevertheless, the large-scale implementation of hydrogels continues to face multifaceted challenges—particularly poor degradability and latent ecological risks, as conventional polyacrylamide (PAM)-based gels resist soil mineralization and retain potentially neurotoxic monomers, leaving a critical gap in multi-annual field data concerning their non-target interference with native soil aggregate evolution, pore distribution, and rhizospheric carbon–nitrogen footprints. Mechanistically, many hydrogels with tensile strengths below 1 MPa are highly susceptible to three-dimensional network collapse under high-salinity osmotic shock and tillage mechanical stress, exhibiting a precipitous drop in water retention after more than three wet–dry cycles due to deficient long-term structural stability. Compounding these technical gaps are elevated production costs and low farmer adoption, driven by the absence of texture-specific performance thresholds—such as an available water increment ≥ 40% for sandy soils—and the lack of established life-cycle cost models and farmer incentive mechanisms for bio-based hydrogels. Moving forward, hydrogel technology should pivot toward materials innovation and cost-reduction engineering to broaden its applicability, employ ≥3-year, multi-habitat regional trials to delineate ecological benefit–risk boundaries, and ultimately position hydrogels as pivotal enablers of sustainable, green agricultural paradigms. Full article

Human milk (HM) represents the physiological reference for infant digestion, whereas infant formulas (IFs) differ in protein composition and lipid architecture. This study compared the gastric and intestinal digestion of HM, five commercial IFs, whole milk powder (WMP), and protein ingredients using a standardized infant in vitro model. Protein hydrolysis was assessed through digestibility and residual subunit patterns, and lipid digestion was evaluated through free fatty acid release and fatty acid profiling. Gastric digestion revealed pronounced differences driven by intrinsic structure and processing, whereas intestinal digestion reduced these disparities, although distinct subunit patterns persisted. Lipid analyses showed clear source-dependent separation before digestion and partial convergence during intestinal lipolysis. Several IFs containing structured lipid and vegetable-oil systems showed intestinal fatty acid or free fatty acid profiles closer to HM than WMP, whereas samples with stronger bovine-fat signatures remained more distinct. Overall, the results demonstrate that digestion-product patterns provide mechanistic insight into structure-driven digestion behavior in complex dairy-based matrices, beyond compositional comparison alone. Full article