Search Results (2,870)

The pancreas is an organ with two functions: endocrine and exocrine. The proper functioning of the pancreas depends on many factors. One of these is trace elements—precise control of trace element homeostasis is important for both the endocrine and exocrine parts. This review provides a comprehensive summary of current knowledge regarding the role of trace elements: iron (Fe), copper (Cu), cobalt (Co), iodine (I), manganese (Mn), zinc (Zn), silver (Ag), cadmium (Cd), mercury (Hg), lead (Pb), and selenium (Se) in pancreatic physiology and their influence on the pathogenesis of key diseases of this organ, such as diabetes (DM), acute (AP) and chronic pancreatitis (CP), autoimmune pancreatitis (AIP), and pancreatic cancer (PC). Trace elements, including Fe, Cu, Zn, Se, and Mn, play a fundamental role in maintaining endocrine and exocrine homeostasis, participating in insulin synthesis, stabilizing digestive enzymes, and the functioning of antioxidant systems. It has been demonstrated that disturbances in their concentrations lead to the activation of pathological molecular pathways, including oxidative stress, chronic inflammation, and beta-cell apoptosis. In the context of diabetes, excess Fe promotes ferroptosis, whilst exposure to heavy metals such as Cd, Pb, and Hg induces insulin resistance and pancreatic islet dysfunction. In the course of pancreatitis, elements such as Zn and Se exhibit protective potential by stabilizing tissue barriers, whereas toxic metals impair ion transport, exacerbating fibrotic processes. Furthermore, analysis of available data indicates a significant association between heavy metal accumulation and pancreatic carcinogenesis, driven by DNA damage and oncogene modulation. Understanding pancreatic metallomics opens new prospects for early diagnosis, environmental prevention, and the development of targeted therapeutic strategies that restore the body’s micronutrient balance. Full article

With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, an integrated simulation platform coupled with environmental loads, hull dynamics, propeller characteristics and a high-fidelity thermodynamic engine model was constructed to explore the response characteristics of the propulsion system. The model integrates a zero-dimensional multi-zone combustion method, turbocharger dynamic characteristics and an incremental PID governor, and has been verified based on the bench test data of TBD234V12 diesel engine and the 20 m Wigley standard ship. The simulation results under the sea conditions from level 7 to 9 show that the transient load has a nonlinear amplification effect. Specifically, from sea state 7 to sea state 9, the engine load fluctuation range expands by 2.0 times, while the main peak amplitude of speed fluctuation increases by 3.7 times. Furthermore, the peak exhaust pressure rises by 1.8 times, and the exhaust temperature fluctuation amplitude broadens by 35%. Frequency domain analysis further identified the low-frequency energy concentration phenomenon in the exhaust pressure spectrum and the precursor characteristics of compressor surge. The research results quantify the deterioration law of thermodynamic stability and mechanical stress under wave disturbance, and provide an important reference for the formulation of an engine robust control strategy and fatigue life assessment under high sea conditions. Full article

This translational synthesis highlights the potential role of obesity-induced low-grade chronic inflammation in modulating clinical outcomes among patients with spondyloarthritis (SpA). Obesity transforms adipose tissue into a pro-inflammatory endocrine organ, where hypertrophic adipocytes release adipokines such as leptin alongside cytokines including TNF-α and IL-6, potentially contributing to macrophage polarization toward an M1 phenotype and activating NF-κB signaling pathways. This systemic immunometabolic priming may lower activation thresholds at the enthesis—the primary pathological site in SpA—potentially amplifying IL-23/IL-17 axis activity via Th17 bias, innate-like lymphocyte responses, and stromal–immune crosstalk under mechanical stress. Clinically, patients with SpA and obesity have been reported to demonstrate heightened disease activity (BASDAI, ASDAS), impaired function (BASFI), accelerated radiographic progression (syndesmophytes, enthesophytes), and diminished biologic response rates, potentially attributable to pharmacokinetic alterations (e.g., subtherapeutic TNF inhibitor levels) and pharmacodynamic resistance. Multisystem comorbidities, including non-alcoholic fatty liver disease, cardiovascular events, metabolic syndrome, sleep disturbances, and depression, further exacerbate morbidity and diminish quality of life. Therapeutic implications emphasize obesity as a modifiable disease modifier. Weight loss interventions, including hypocaloric diets, anti-inflammatory regimens (e.g., Mediterranean diet), multicomponent exercise, GLP-1 receptor agonists, and bariatric surgery, have been associated with reductions in inflammatory biomarkers, improved remission rates (MDA, DAPSA), and prolonged drug survival by restoring adipokine balance and disrupting mechano-inflammatory loops. Future randomized controlled trials should prioritize long-term evaluations of integrated multidisciplinary strategies that combine metabolic optimization with immunomodulatory therapies, addressing adherence challenges through psychological support and patient-tailored protocols, while elucidating dose–response relationships for GLP-1RAs and exercise in diverse SpA subtypes to establish precision management paradigms that mitigate cardiometabolic burden and improve holistic outcomes. Full article

Multiple Sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system characterized by leukocyte infiltration, inflammation, demyelination, and progressive neurodegeneration. Susceptibility to MS is influenced by genetic factors, including variants within the human leukocyte antigen (HLA) region, (notably HLA-DR15), and multiple single nucleotide polymorphisms that modulate T cell function and immune regulation. Clinically, early manifestations such as visual disturbances, sensory deficits, fatigue, and impaired coordination often precede more advanced features, including cognitive decline and bladder or bowel dysfunction. Although experimental and genetic models of neuroinflammation have facilitated the development of therapies that reduce relapse rates and slow disease progression, the underlying pathological mechanisms remain incompletely understood. Emerging evidence points to the importance of cytoskeletal organization and membrane-associated signaling platforms in maintaining neuronal and immune cell function. Disruption of these systems may contribute to demyelination and neuroinflammatory cascades. Within this context, a systems biology perspective is particularly valuable, as it emphasizes the integration of multiple, interdependent pathways rather than isolated mechanisms. Caveolin-1 (Cav-1), an integral membrane protein of caveolae, has gained attention as a potential central regulator due to its role in coordinating signaling processes across diverse cellular compartments. In this review, we examine the potential genetic and functional contributions of Cav-1 to MS pathophysiology, with a focus on its involvement in oxidative stress, inflammation, blood–brain barrier integrity, and autophagy. By framing these processes as components of an interconnected network, we highlight Cav-1 as a context-dependent modulator that may influence both disease progression and severity. However, despite its mechanistic relevance, the translational potential of Cav-1 remains uncertain, and further studies are required to clarify its precise role and evaluate its suitability as a therapeutic target in MS. Full article

Tropical estuaries are highly productive yet increasingly threatened by natural and anthropogenic pressures, necessitating robust ecological assessments for sustainable management. This study assesses the spatio-seasonal distribution of macrobenthic assemblages and evaluates the ecological health of the Sangu River estuary based on their bioindicator potential. Sediment samples for macrobenthos analysis were collected during three seasons (pre-monsoon, monsoon, and post-monsoon) from nine stations across three estuarine zones influenced by sedimentation, aquaculture, and terrestrial runoff. We employed microbenthic diversity indices, multivariate analyses, the AZTI’s Marine Biotic Index (AMBI), and multivariate-AMBI (M-AMBI) to evaluate the ecological health status of the study area. Our study recorded 13 taxa, dominated by Nereididae (40.90%), Mysidae (14.29%), and Capitellidae (10.20%). Macrobenthos diversity (Shannon diversity) ranged from 0.80 to 1.22, and abundance showed negative correlations with salinity (r = −0.29) and silt (r = −0.22), and a positive correlation with dissolved oxygen (r = 0.29). Analysis of Similarities (ANOSIM) indicated that seasonal variation was the primary driver of community structure (p < 0.001). AMBI classified most stations as having good to moderate ecological status, while M-AMBI indicated moderate disturbance across seasons, with elevated proportions of opportunistic taxa (EG V: 14.4–32%) reflecting persistent anthropogenic stress. This study provides the first empirical ecological baseline for the Sangu River estuary and highlights the applicability of family-level AMBI assessments in data-limited tropical estuarine systems. Full article

Reservoir drawdown zones are repeatedly affected by water-level fluctuations and anthropogenic regulation, making vegetation recovery an important issue for ecological restoration and sustainable reservoir management. This study focused on Cynodon dactylon, a dominant herbaceous species in the drawdown zones of five reservoirs in the Jinsha River Basin, southwestern China. Drawing on the existing concept of stress memory, which emphasizes the retained effects of previous environmental stress exposure on subsequent plant responses, we established an integrated assessment framework based on species dominance, functional traits, landscape pattern indices, and the soil seed bank. This framework was used to evaluate variation in the stress memory of C. dactylon across different successional stages and inundation gradients. The results showed that the overall stress memory of C. dactylon increased with successional progression in both the upper and lower zones, indicating continuous adaptive accumulation under long-term hydrological disturbance. The memory reflected by individual component indicators also generally increased, although their accumulation patterns varied among indicators. These findings suggest that dominance, functional traits, landscape pattern, and the soil seed bank can jointly characterize the adaptive responses of C. dactylon during vegetation recovery. Overall, the stress memory framework provides a systematic approach for identifying stage-specific vegetation changes, evaluating restoration potential, and informing ecological restoration and sustainable management in reservoir drawdown zones. Full article

Autonomous vehicles must enforce safety constraints even when their state estimates are corrupted by sensor faults and disturbances. This paper develops a separation-based robust safety-control framework that couples a fault-tolerant observer with a control barrier function (CBF) safety filter through an explicit estimation-error envelope. First, a uniformly ultimately bounded observer-error estimate is derived. This bound is then injected into an estimated-state robust CBF condition, yielding safety margins that account for both observation error and bounded disturbances. The construction is further extended to time-varying safe sets induced by moving obstacles. For implementation, the resulting condition is realized as a quadratic-program safety filter with high-order obstacle and lane constraints. Simulations on a nonlinear 3-DOF bicycle model evaluate bias faults, gust-like disturbances, dense traffic, and tightened stress tests. Compared with a standard CBF baseline and observer/safety-filter ablations, the proposed method preserves nonnegative safety margins while keeping slack activation negligible. Additional sensitivity experiments quantify the trade-off among safety margin, slack usage, observer accuracy, control conservatism, and QP computation time. The results support the proposed architecture as a practical bridge between bounded state estimation and fault-aware safety filtering. Full article

Congenital heart disease (CHD) is the most common anatomical malformation occurring in live-born infants and an increasing cause of morbidity and mortality all over the world. Population-based observations have described associations between maternal cardiometabolic disorders and the risk of CHD in offspring. The present article is a narrative review. The aim of this study was to review the epidemiological evidence and clinical observations relating maternal obesity and diabetes mellitus to the risk of CHD in offspring, with particular attention paid to first trimester disturbances of fetal cardiac development and the influence of genetic, epigenetic and environmental factors. Studies have shown that maternal diabetes is a risk factor associated with nearly all subtypes of CHDs in offspring, while obesity and overweight are associated with increased risk for complex defects and outflow tract obstruction and decreased risk for ventricular septal defects. Diabetes and obesity share several phenotypes, which could be transmissible from mother to fetus via the placenta. This means that an increase in maternal glucose could be responsible for the prevalence of CHD in newborns of obese women. On the other hand, maternal diabetes may induce epigenetic modifications in the developing fetus. DNA methylation changes can impact gene expression patterns relevant to heart development. The abovementioned studies are heterogenous, express different opinions and are often difficult to compare. Therefore, the results from these meta-analyses must be interpreted with caution. Optimal diabetes control is responsible for the prevention of oxidative stress in diabetic pregnancies, and a deeper understanding of maternal risk factors holds the potential to improve both prenatal detection of CHDs by identifying at-risk pregnancies and primary prevention of diseases by improving preconception management. Full article

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but its strong parameter dependence is a key factor limiting the development of MPC. Therefore, a model-free predictive control (MFPC) method combining an ultra-local model with model predictive control is proposed to solve the problem of strong dependence of traditional MPC on system model parameters. Firstly, establish the ultra-local mathematical model of the DAB converter. The system’s lumped disturbances are identified using the residual prediction method and substituted into the discrete model of the system at the next time step to achieve model-free prediction. Secondly, a minimum back-flow power constraint is added to the cost function to improve the steady-state performance of the converter. Thirdly, in the extended phase shift modulation, the Lagrange multiplier method (LMM) is proposed to reduce the current stress, ultimately achieving the collaborative optimization of the comprehensive performance of the DAB. Finally, a simulation model is built using MATLAB/Simulink, and compared with traditional control methods, the voltage ripple has been reduced by 51.3%, 89.1%, and 85.1%, respectively; the current stress significantly decreases both when the output voltage reference value changes and when the load resistance changes abruptly, and both can basically achieve zero back-flow power operation. The validity and superiority of the proposed strategy have been verified. Full article

Herbicides considered selective to rice are generally evaluated based on their direct crop safety and weed suppression effects, yet it remains unclear whether they may also trigger indirect or context-dependent effects on rice under rice–barnyardgrass co-cultivation. To address this question, we compared rice performance and associated microbial dynamics under six conditions: rice–barnyardgrass co-cultivation and rice monoculture, each treated with a water spray control or sublethal doses of propanil (Pro, 66.7 mg a.i. L⁻¹) or cyhalofop-butyl (Cyh, 5.86 mg a.i. L⁻¹). Barnyardgrass exhibited visible injury and stronger leaf-level oxidative stress responses, whereas rice displayed no discernible phytotoxic symptoms. Nevertheless, under co-cultivation, herbicide treatment significantly suppressed rice growth, with up to 17.8% lower root lengths and 24.8% lower shoot fresh weights, with reductions varying by herbicide and trait. By contrast, comparable suppression was not observed under herbicide exposure or co-cultivation alone, identifying this response as an emergent, context-dependent negative effect. Microbiota reassembly emerged as an early and stage-specific component of the herbicide-associated response under co-cultivation, with the most pronounced changes detected on day 5 and occurring primarily in bacterial communities. Moreover, bacterial community variation was negatively correlated with root length (ρ = −0.664), and urease activity declined under herbicide treatment. Together, these findings indicate that in paddy fields, herbicides act not only on individual plants but also as an external disturbance to the coupled rice–barnyardgrass system, for which microbiota reorganization represents a key component of the ecological response. Our results suggest that herbicide selectivity should be interpreted within a crop–weed–microbiome context, rather than being inferred solely from their direct crop safety and weed suppression effects. Full article

Background: To investigate the effect of angiotensin-(1-7) [Ang-(1-7)] on serum metabolomics in obese type 2 diabetic (T2DM) mice. Methods: Four-week-old male C57BL/6 mice were fed a high-fat diet and intraperitoneally injected with streptozotocin (35 mg/kg) to establish an obese T2DM model. Mice were randomized into control, T2DM and T2DM+Ang-(1-7) groups (n = 6). Body weight and blood glucose were recorded weekly. At 10 weeks, blood glucose, serum inflammatory factors, lipid profiles, and pancreatic β-cell insulin secretion were detected; serum metabolite alterations were analyzed via untargeted metabolomics. Results: 1. Ang-(1-7) intervention decreased blood glucose (p < 0.05) and CRP levels (p < 0.01), and alleviated dyslipidemia (p < 0.05 or p < 0.01), as well as β-cell morphology and insulin expression in obese T2DM mice. 2. Non-targeted metabolomics analysis suggested that Ang-(1-7) may alleviate abnormal amino acid metabolic pathways by regulating levels of metabolites such as L-valine, L-proline, L-histidine, and glutamic acid. This intervention also tended to reduce multiple lipid metabolites, including Omega-3 Arachidonic Acid Ethyl Ester, phosphatidylcholine, and glycerophosphocholine, thereby participating in the modulation of lipid metabolism balance. KEGG enrichment analysis further indicated that Ang-(1-7) was involved in the regulation of protein digestion and the absorption pathway, as well as the HIF-1 signaling pathway related to oxidative stress, bile acid metabolism pathway, and other signaling pathways, and improving the insulin secretion pathway, pyrimidine metabolism, and TCA cycle energy metabolism pathway. Conclusions: Ang-(1-7) may partially improve metabolic disturbances in obese T2DM mice, which is potentially associated with the modulation of multiple metabolic processes, including amino acid metabolism, lipid metabolism, insulin secretion, and TCA cycle energy metabolism. Full article

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution. Full article

Urban trees are increasingly exposed to persistent anthropogenic drivers that extend beyond climatic forcing and fundamentally alter the conditions of secondary growth. While climatic controls of cambial phenology and xylogenesis are well established, the mechanisms by which non-climatic drivers regulate cambial activity and wood formation remain fragmented and are often inferred only indirectly. Here, we develop a cambium-centred framework to synthesise current evidence on how anthropogenic drivers shape wood formation in urban and peri-urban trees. To our knowledge, this is among the first syntheses explicitly linking anthropogenic drivers to distinct stages of xylogenesis. Anthropogenic drivers are typically chronic, spatially heterogeneous, and temporally decoupled from seasonal climatic rhythms, and may alter cambial kinetics and generate anatomical signatures not captured by ring width alone. We evaluate major driver domains, including root-zone constraints, altered hydrology, urban microclimate, pollution, salinity, and mechanical disturbance, while also considering emerging drivers such as artificial light at night and microplastics. Evidence is stratified into three levels: direct observations, indirect physiological evidence, and mechanistic plausibility. Across driver classes, three recurrent anatomical patterns emerge: reduced conduit size under hydraulic or osmotic stress; anomalies in wall deposition under carbon limitation or oxidative stress; and pronounced circumferential heterogeneity under spatially localised forcing. Integrative approaches combining xylogenesis monitoring, quantitative wood anatomy, dendrometer observations and spatially explicit sampling are essential to disentangle anthropogenic from climatic effects and improve assessment of tree resilience. Full article

Although secondary control of direct current (DC) microgrids has been widely studied, traditional static current sharing may still cause severe voltage sag under large-signal constant power load (CPL) steps, and many distributed schemes rely on global topology information while showing limited transient disturbance rejection. To address these issues, this paper proposes an observer-assisted, stability-margin-driven prescribed-time distributed secondary control strategy for islanded DC microgrids. A dynamic CPL risk evaluation function updates current-sharing ratios according to converter operating margins, while a distributed prescribed-time observer estimates disturbance envelopes and alleviates high-frequency chattering. Local adaptive gains remove the explicit dependence of controller tuning on global Laplacian eigenvalue information. MATLAB R2024a-based numerical studies show that, under a 6000 W CPL stress scenario, the proposed method limits the maximum voltage drop to 3.37 V, compared with 24.60 V for the conventional virtual current derivative (VCD) method. Under heterogeneous line impedances and a non-ideal digital benchmark, the proposed method yields a normalized current-sharing error of 0.72%, whereas the VCD method exhibits milder voltage transients. These results support the algorithmic effectiveness and numerical robustness of the proposed strategy within the adopted validation environment. Full article

Background: Gestational diabetes mellitus (GDM) is an increasingly prevalent metabolic disorder of pregnancy. Beyond its well-established metabolic consequences, growing evidence suggests that exposure to maternal hyperglycemia during fetal life may influence immune system development and increase the risk of allergic diseases in offspring. Objective: This study aimed to systematically review the available evidence on the association between gestational diabetes mellitus and the development of allergic diseases in children, with particular emphasis on immunological mechanisms and the role of early-life gut microbiota. Methods: A systematic review was conducted using the PubMed and Scopus databases. Original human and animal studies, including cohort, case–control, cross-sectional, and clinical studies, were eligible for inclusion. Study selection followed PRISMA guidelines and was performed independently by three reviewers. Methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) Critical Appraisal Tools. Results: The included studies suggest that children born to mothers with GDM may have an increased risk of developing allergic diseases, particularly atopic dermatitis, food allergy, allergic rhinitis, and urticaria. Associations with childhood asthma were less consistent and appeared to depend on maternal body mass index, glycemic control, and duration of follow-up. Evidence suggests that maternal hyperglycemia may disrupt fetal immune programming through chronic low-grade inflammation, oxidative stress, altered cytokine profiles, and impaired regulatory T-cell development. Additionally, GDM has been associated with early alterations in neonatal gut microbiota composition and metabolic pathways, which may further contribute to immune dysregulation and increased susceptibility to allergic diseases. Importantly, effective metabolic control during pregnancy was associated with a lower risk of adverse allergic outcomes in offspring. Conclusions: GDM may represent an important prenatal exposure associated with altered immune maturation and a higher risk of allergic diseases in offspring. Early metabolic disturbances, immune dysregulation, and alterations in gut microbiota appear to be key mechanisms underlying this association. Optimizing glycemic control during pregnancy and implementing early-life preventive strategies may reduce the long-term burden of allergic diseases. Further well-designed longitudinal and mechanistic studies are required to clarify causal pathways and identify effective preventive interventions. Full article