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Spodoptera frugiperda (J. E. Smith) is a major invasive pest of maize in Asia, with migration pathways extending annually in July into North China, a key maize-producing region. However, the potential utility of Bt maize for controlling this pest remains unclear. In this study, we evaluated the insecticidal activity and field control efficacy of Bt-(Cry1Ab + Cry1F) maize against the insect in North China. Bt insecticidal protein levels in Bt-(Cry1Ab + Cry1F) maize tissues followed the order: V6-8 leaves > V12 leaves > VT tassels, R4 kernels > R1 silks. Correspondingly, larval mortality was 100 ± 0% after leaf feeding for 7 days and 84.49 ± 1.71%, 75.65 ± 1.50%, and 82.57 ± 1.08% after feeding on VT tassels, R1 silks, and R4 kernels, respectively. V5 leaf feeding significantly reduced pupal weight, pupation and emergence rates, and fecundity of 1st- and 3rd-instar S. frugiperda, while prolonging larval development. Field infestation experiments showed significantly lower larval density and leaf and plant damage rates of S. frugiperda in Bt-(Cry1Ab + Cry1F) maize than those in conventional maize fields, with larval control efficacy of 61.14-100%. Field surveys in 2023-2025 revealed no S. frugiperda larvae in Bt-(Cry1Ab + Cry1F) maize fields throughout the entire growth period, with plants exhibiting no damage. In contrast, conventional maize suffered continuous damage during the reproductive growth stage, with a peak larval population of 9.33-12.67 per 100 plants and a plant damage rate of 64-74%. This study demonstrates that Bt-(Cry1Ab + Cry1F) maize can effectively control this pest in the region and provide a green tool for area-wide management of S. frugiperda. © 2026 Society of Chemical Industry.

Introduction: Detection of in-stent restenosis by cardiac CT is challenging due to blooming artifacts. The technological progress of CT scanners and especially the recent introduction of photon-counting detectors (PCDs) has led to an improvement in image quality. Several studies have analyzed the lumen visibility of coronary stents, but most studies used models which did not simulate cardiac movement. In this study we use a pulsatile heart model to simulate a heartbeat to analyze the effects of cardiac motion on image quality. Methods: Seventeen different coronary stents with an outer diameter of 3.0 mm were implanted into polyolefin tubes. The tubes were then filled with diluted contrast medium and attached to the pulsatile heart model. The stents were scanned in a third-generation dual-source CT with an energy-integrating detector (EID) and a first-generation PCD CT. Results: In motion, the mean visible stent lumen was reduced from 64.4% to 59.4% in EID CT, from 61.4% to 56.0% in PCD CT using the Bv60 kernel, and from 72.9% to 62.9% in PCD CT using the Bv72 kernel, each in standard resolution mode. Employing the ultra-high-resolution mode (UHR), stent lumen visibility was reduced from 61.3% to 57.9% with the Bv60 kernel and from 71.7% to 61.8% with the Bv72 kernel. The difference between static imaging and motion was significant in each instance (p < 0.001). Conclusions: While PCD CT and the use of sharper kernels improves the image quality in comparison with EID CT and smoother kernels, the impact of cardiac motion on the reduction in stent lumen visibility is substantial. Hence, the best image quality is achieved in patients with a normal and regular heart rate. If this is not possible to achieve, a retrospective acquisition mode should be considered.

Almond (Prunus dulcis) is a highly nutritious nut, rich in unsaturated fatty acids, fibre, and bioactive compounds such as tocopherols, phenolic compounds, and carotenoids. Processing methods commonly applied in the almond industry, including blanching and roasting, may modify the nutritional composition and bioactive profile of the kernels. Therefore, the aim of this study was to evaluate the effect of blanching and roasting on the nutritional composition and bioactive compound content of the 'Largueta' almond variety. Three forms were analysed: raw almonds with skin, blanched (peeled) raw almonds, and roasted almonds, with their chemical composition, lipid profile and bioactive compound content being examined. The data obtained indicated that raw almonds with skin showed higher levels of fibre (12.16 g/100 g), phenolic compounds (66.35 mg gallic acid/100 g), and &#x3b2;-carotene (65.88 &#xb5;g/100 g). Roasted almonds contained lower amounts of phenolic compounds (42.87 mg gallic acid/100 g), tocopherols (7.64 mg &#x3b1;-tocopherol/100 g and 1.99 mg &#x3b3;-tocopherol/100 g) and essential amino acids such as tryptophan (1.23 g/100 g protein) and lysine (3.22 g/100 protein). Blanching, by removing the skin, significantly reduces fibre (7.52 g/100 g) and carotenes (26.60 &#xb5;g &#x3b2;-carotene/100 g). With regard to fatty acids, the main components of nuts, oleic acid predominated in all samples (>65%), with no significant changes due to processing. Thermal treatments modify the composition of almonds. Roasting concentrates some nutrients but reduces antioxidants, while blanching mainly affects fibre content. Therefore, the consumption of raw sweet almonds with skin is recommended to preserve their nutritional and antioxidant benefits, or to subject them to moderate heat treatment.

Single-lead electrocardiogram (ECG) is widely used in wearable devices for atrial fibrillation (AF) screening. Nevertheless, subtle pathological characteristics like P-waves and f-waves in practical signals are vulnerable to noise contamination. Meanwhile, the scarcity of high-quality annotated abnormal data instances leads to severe class imbalance. To mitigate these issues, we present an end-to-end framework designed for arrhythmia diagnosis using single-lead ECG signals, which integrates quality-aware data augmentation with a Peak-Enhanced attention mechanism. First, to mitigate the problem of data imbalance, a Quality-Aware Generative Adversarial Network (QA-GAN) is designed. This network integrates a signal quality evaluation module based on signal kurtosis, together with a dynamic soft-label training scheme, guiding the generator to prioritize learning high-quality morphological features, thereby synthesizing high-fidelity minority class samples. Second, to accurately capture subtle pathological features in electrocardiograms, a Peak-Enhanced Attention Convolutional Network (PEAC-Net) classification model is proposed. This model incorporates a Peak-Enhanced Attention (PE-Att) module, which employs learnable derivative convolutional kernels to precisely identify the transition points in the ECG signal. Furthermore, by integrating one-dimensional multi-scale dilated convolution (DSGC1D) with bidirectional LSTM, the model achieves effective capturing of both fine-grained local morphological features and long-range global rhythm patterns. Experimental results on the PhysioNet 2017 dataset indicate that the presented model attains an accuracy of 0.902 and a macro-F1 score of 0.880, respectively, outperforming other state-of-the-art models and also exhibiting robust data adaptability on the MIT-BIH dataset.

This study aimed to characterize the stress relaxation of wheat kernels of different hardness and varying moisture contents using generalized Maxwell models with three, five, and seven elements. Single-kernel stress relaxation tests were conducted under controlled axial compression to obtain force decay data, which were fitted using non-linear regression to estimate model constants. Positive correlations between kernel moisture content and Maxwell constants were observed, indicating greater magnitude of force decay and higher relaxation rates at higher moisture levels, while the residual force decreased. Significant differences in viscoelastic parameters were also found between hard and soft wheat varieties.

Low back pain affects hundreds of millions of people and is associated with degenerative changes in the lumbar spine. Trabecular bone microarchitecture change is a reasonable contributor to low back pain, but it remains largely invisible on routine clinical CT because typical voxel sizes (500 to 1000 &#x3bc;m) are insufficient to resolve trabeculae (about 100-200 &#x3bc;m). We present LumbarSR, a paired and registered dataset of 30 human lumbar vertebral specimens scanned with a photon-counting micro-CT (Micro-PCCT) reference at 105 &#x3bc;m isotropic resolution and with a standard clinical CT system under eight acquisition configurations formed by the factorial combination of two in-plane resolutions (195 and 586 &#x3bc;m), two slice thicknesses (500 &#x3bc;m and 1000 &#x3bc;m), and two reconstruction kernels (bone and soft tissue). Clinical CT volumes are rigidly registered to the Micro-PCCT reference using ANTs-based alignment and resampled to a common voxel grid, enabling voxel-wise evaluation and supervised learning. LumbarSR provides data in both original DICOM and registered NIfTI volumes with a consistent directory structure and specimen identifiers. We provide baseline evaluations using whole-image and masked image-quality metrics, trabecular morphometry against the Micro-PCCT reference, and super-resolution benchmarks based on interpolation and deep learning methods. LumbarSR is intended to support the development and evaluation of super-resolution methods for lumbar vertebra CT and related analyses of trabecular level structure. Because specimens were de-identified dry teaching specimens without available clinical histories or demographic metadata, LumbarSR should be interpreted as a paired imaging and benchmarking resource rather than a clinically labeled cohort.

A Gaussian Process Regression (GPR) model was developed to predict the shear capacity of exterior reinforced concrete (RC) beam-column joints subjected to seismic loading. The model accounts for key parameters, including beam and column geometry, reinforcement detailing, axial column load, and concrete compressive strength. A database of 273 experimentally tested specimens was used, with emphasis on horizontal joint shear strength. Three kernel structures within the GPR framework-Primary, Rational Second-Order, and Combined kernels-were examined. Model predictions were evaluated against existing shear-strength formulations using deterministic metrics (MAE, RMSE, and R2) and probabilistic measures (NLPD and MSLL). The results show that kernel effectiveness depends on model formulation; however, the Combined kernel exhibited more stable predictions and improved uncertainty calibration for models with higher-dimensional input sets. Sensitivity analysis identified concrete compressive strength, beam depth, and joint transverse reinforcement as dominant variables, followed by column height, axial load ratio, and reinforcement configuration. Overall, the proposed framework enables consistent shear-strength prediction and quantifies the relative influence of geometric and material parameters, contributing to more informed assessment and design of RC beam-column joints.

The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such materials is required for optimizing mix designs and ensuring structural performance. In this context, the present study is the first to systematically investigate and compare three different support vector regression (SVR) kernels, namely, linear SVR (L-SVR), polynomial SVR (Poly-SVR), and radial basis function SVR (RBF-SVR), for predicting the compressive strength of normal, high, and ultra-high strength 3D-printed fiber-reinforced concrete (3DPFRC). A model was developed and validated using a dataset of 278 samples collected from the literature. In addition to SVR models, Artificial Neural Networks (ANN) and Gradient Boosting Machines (GBM) were developed for benchmarking purposes. Results from statistical evaluation revealed that the ANN model achieved the highest predictive accuracy overall, outperforming the L-SVR, Poly-SVR, and GBM models. Among the SVR-based models, the RBF-SVR demonstrated superior performance, showing competitive accuracy with the ANN while outperforming both the other SVR variants (L-SVR and Poly-SVR) as well as the GBM model in terms of coefficient of determination (R&#xb2;) value and error metrics. All models achieved R&#xb2; scores ranging between 0.94 and 0.86 for the training datasets and 0.93 and 0.72 in the testing datasets. The RBF-SVR model, in particular, offered an excellent balance between accuracy, training speed, and reliability, making it a strong and efficient alternative to more complex models. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the water-to-cement ratio, silica fume content, and cement volume as the most influential parameters affecting compressive strength.

(-)-Epigallocatechin has multiple bioactivities. ANR catalyzes its biosynthesis, with unclear structure and mechanism restricting enzyme modification. Here, we first screened ANRs from 21 plant species and identified Torreya grandis ANR (TgANR) as the most efficient candidate based on molecular docking and in vitro assays. Through integrated alanine scanning and computational prediction, we constructed a combinatorial mutant N15S/F106E/P182S, which exhibited a 3.88-fold higher EGC synthesis activity than the wild-type enzyme. The interaction mechanism between TgANR and the substrate delphinidin as well as the structure-activity relationship in the catalytic production of EGC were studied on the basis of protein engineering modification, thus clarifying the catalytic mechanism of TgANR. Furthermore, transient overexpression of the mutant gene in T. grandis kernels significantly increased EGC accumulation. This study not only elucidates the structure activity relationship of TgANR but also provides an efficient enzyme variant and a protein engineering framework for boosting EGC biosynthesis in plants.

Perchlorate, thiocyanate, and nitrate are ubiquitous environmental contaminants that disrupt thyroid homeostasis primarily through inhibition of iodide uptake into the thyroid. Evidence linking prenatal exposure to these chemicals with maternal thyroid function remains limited and inconsistent. We evaluated these associations in 192 pregnant women from the MARBLES cohort (2006-2020) who contributed repeated urine and serum samples. Urinary perchlorate, thiocyanate, nitrate, and iodide concentrations were quantified, and serum total triiodothyronine (TT3), total thyroxine (TT4), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) levels were assessed. The thyroid feedback quantile-based index (TFQI) was calculated from FT4 and TSH values. Single-pollutant associations were estimated using generalized estimating equation models, and mixture effects were evaluated using Bayesian kernel machine regression. Analyses were stratified by maternal urinary iodine concentration (UIC) group. In the overall population, higher urinary thiocyanate concentrations were associated with lower FT4 levels (&#x3b2;= -0.17; 95% CI: -0.30, -0.04), with stronger effects observed at lower FT4 levels. Higher nitrate concentrations were associated with higher TT4 levels. Associations were generally stronger among women with UIC <150 &#x3bc;g/L. In this subgroup, higher thiocyanate was additionally associated with lower TFQI, while higher nitrate was associated with lower TSH levels. Mixture exposure was also associated with a decreasing trend in TFQI among women with UIC <150 &#x3bc;g/L and an increasing trend among women with UIC &#x2265;150 &#x3bc;g/L. These findings suggest that prenatal individual and mixture exposure to perchlorate, thiocyanate, and nitrate may alter maternal thyroid function, particularly among women with UIC <150 &#x3bc;g/L.

Coconut kernel fiber (CKF) is a by-product of coconut oil processing; it is rich in protein and serves as a potential source of bioactive peptides. In this study, from the enzymatic hydrolysis products of CKF (CKFH), a low-molecular-weight CKFH component (LW-CKFH, 1-3 kDa), exhibiting 74.49% &#x3b1;-glucosidase inhibition and restoring glucose metabolism in IR-HepG2 cells to 71.37% of normal levels. In a type 2 diabetes (T2DM) mouse model, LW-CKFH alleviated insulin resistance and enhanced insulin sensitivity by repairing liver damage, thereby improving glucose and lipid metabolism and reducing inflammation; its effects on improving insulin resistance and sensitivity reached 75.43% and 75.47% of the efficacy of metformin, respectively. Molecular docking analysis identified FDLPAR, LPFPRPAGPR, and ANVFNPR as key active peptides responsible for inhibiting &#x3b1;-glucosidase activity. Furthermore, LW-CKFH exhibited good gastrointestinal digestibility and processing stability, while significantly reducing the glucose release rate from bread (>50%), indicating its suitability for the development of hypoglycemic or low-GI functional foods. LW-CKFH was particularly suitable as a functional ingredient for fruits, vegetables, grains, and dairy products to develop low-GI or hypoglycemic foods. This study provides new insights into the high-value utilization of the coconut processing by-product CKF.

(1) Background: Slaughterhouses receive livestock from different sources, enabling the collection of health-related surveillance information. Few surveillance frameworks integrate slaughterhouse distribution with livestock population density for prioritizing surveillance areas. (2) Methods: The surveillance priority of livestock in Thailand was determined on a district basis. Registered slaughterhouses were mapped using kernel density estimation. Livestock were mapped using census data. The z-scores were combined to determine the composite surveillance prioritization index for each district. The 90th percentile was used to determine district-level surveillance priority. Spatial analyses using Global Moran's I and LISA statistics were performed. Sensitivity analyses assessed robustness. (3) Results: The composite district-level priority index ranged from -0.728 to 9.071 (I = 0.642, p < 0.001). LISA identified 120 High-High (HH) priority districts. Of these, 42 priority districts were selected, concentrated in the northeastern and west-central regions. (4) Conclusions: This model is replicable and can be applied to other regions that rear livestock and use slaughterhouses for surveillance.

Predicting the human burden of vector-borne diseases from limited surveillance data remains a major challenge, particularly in the presence of nonlinear transmission dynamics and delayed effects arising from vector ecology and human behavior. We develop a data-driven framework based on an extension of Sparse Identification of Nonlinear Dynamics to systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data. Using severe fever with thrombocytopenia syndrome as a case study, we show that this approach can uncover key features of tick-borne disease dynamics using only human incidence and local temperature data, without imposing predefined assumptions on human case reporting. We further ascertain the robustness of the recovered incidence-temperature model by integrating it with mechanistically derived tick-host covariates, showing that the forecasting ability does not improve. This suggests that the proposed core data-driven model already delivers strong predictions. The framework also allows for systematic sensitivity analysis of memory kernels and behavioral parameters. Although the approach prioritizes predictive accuracy over mechanistic transparency, it yields sparse, interpretable integral representations suitable for epidemiological forecasting. This methodology provides a scalable strategy for forecasting vector-borne disease risk and informing public health decision-making under data limitations.

Engineered cementitious composite (ECC) is a high-performance strain-hardening material widely used in durable infrastructure, yet its complex multi-parameter interactions make accurate mixture design and performance prediction challenging. This study aims to establish an EDFrame, which is an integrated prediction framework for engineered cementitious composite (ECC). First, two original datasets of ECC's tensile stress and strain are collected from the comprehensive and authoritative literature, comprising 18 features and 10 categories of single or hybrid fibers. Data augmentation is then performed using a constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN), with two traditional methods for comparison. A One-Dimensional Convolutional Neural Network with a residual module (1D-Residual CNN) is developed to predict tensile stress and strain, and its performance was compared against five popular machine learning models. The interpretability of the proposed model has been achieved through Partial Dependence Plot (PDP) and Kernel SHAP analyses. The results demonstrate that Tuned-CTGAN effectively generates reliable synthetic data, significantly improving the R2 of 1D-Residual CNN from 0.8658 to 0.9128 for tensile stress and from 0.8433 to 0.9378 for tensile strain, outperforming all compared models. PDP analysis identifies optimal fiber content (1.5-2%) and fiber length (12-20 mm) ranges for enhanced tensile performance, while SHAP analysis reveals fiber length and diameter as the most critical features influencing tensile stress and strain, respectively. The proposed EDFrame provides a robust and interpretable solution for ECC performance prediction, supporting efficient and accurate mixture design in engineering practice.

The deep integration of breakthrough technological innovation and healthcare has given rise to new quality productive forces in healthcare, profoundly reshaping the spatial logic of medical resource allocation. However, existing studies lack an evaluation framework tailored to this emerging concept, and its spatiotemporal evolution and regional coordination remain insufficiently explored, hindering targeted policy-making for balanced healthcare development. We measured the level of new quality productive forces in healthcare across 31 Chinese provinces during 2010-2023 using the CRITIC-entropy combined weighting method and TOPSIS method. Dagum Gini coefficient and kernel density analysis were applied to explore its spatial differentiation and temporal evolution characteristics. &#x3c3; convergence, absolute &#x3b2; and conditional &#x3b2; convergence models were adopted to clarify its convergence pattern. China's new quality productive forces in healthcare showed an "initial decline then sustained growth" trend, peaking in 2023. New quality medical laborers exhibited the strongest performance, while new quality medical objects of labor were the weakest. Spatially, the Eastern Region had the highest level in 2023 and the Western Region recorded the lowest level; regional gaps narrowed steadily. Inter-group disparities dominated overall disparities during 2011-2019, while transvariation was the main source in 2010 and post-2020. Absolute &#x3b2; and conditional &#x3b2; convergence were observed, but no &#x3c3; convergence existed. China's new quality productive forces in healthcare have shown dynamic growth, yet they continue to face structural constraints characterized by persistently underdeveloped new quality medical objects of labor and narrowing yet non-negligible regional disparities. Our findings highlight the need for an integrated policy approach that involves implementing differentiated strategies aligned with the heterogeneous development levels and convergence characteristics of healthcare new quality productive forces across regions, while enhancing patient digital engagement and expanding the accessibility of high-quality online healthcare services.

Environmental heavy metal exposure poses significant endocrine-disrupting risks, yet evidence regarding the impact of metal mixtures on sex hormones in susceptible populations remains limited. We conducted a cross-sectional study involving 434 participants (167 males, 267 postmenopausal females) from the Dongdagou Xinglong cohort, a well-established cohort residing in a historically heavy metal-polluted region in Northwest China. Concentrations of eight metals (aluminum, chromium, copper, zinc, arsenic, selenium, cadmium, lead) in whole blood and a panel of six sex hormones (including androgens and estrogens) in serum were measured. Multiple linear regression analysis revealed significant positive associations between blood cadmium and dehydroepiandrosterone sulfate (&#x3b2; = 0.468), androstenedione (&#x3b2; = 0.571), testosterone (&#x3b2; = 0.680), and estradiol (&#x3b2; = 0.528) in males (all P&#x202f;<&#x202f;0.05). Conversely, in females, blood cadmium was negatively associated with dehydroepiandrosterone sulfate (&#x3b2; = -0.221), androstenedione (&#x3b2; = -0.375), testosterone (&#x3b2; = -0.256), progesterone (&#x3b2; = -0.258), estradiol (&#x3b2; = -0.437), and estrone (&#x3b2; = -0.432) (all P&#x202f;<&#x202f;0.05). Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile-based g-computation (Qgcomp) regression models consistently demonstrated that heavy metal mixture exposure elevated androstenedione and testosterone levels in males while reducing estradiol levels in females. Cadmium was consistently identified as the primary risk factor in both males and postmenopausal females. Furthermore, animal experiments corroborated above findings: Cadmium-exposed rats exhibited dose-dependent cadmium accumulation, disruption of blood metals homeostasis, elevated serum estradiol, and reduced testosterone levels (all P&#x202f;<&#x202f;0.05). Collectively, our analyses of a metal-exposed cohort highlight that heavy metal mixtures are associated with sex-specific dysregulation of sex hormones, wherein cadmium appears to be a primary driver. This study offers insights for assessing reproductive health risks in similar metal-polluted contexts.

Exposure to organochlorine pesticides (OCPs) may disrupt adolescent development; however, their precise impacts remain unclear. Using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2016, we examined associations between OCPs and adolescent body composition indicators, including body mass index (BMI) z_score, appendicular lean mass (ALM), trunk fat (TRF), total fat (TOF), total lean mass (TLM), and total percent fat (TPF). We fitted several statistical models including linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR). Mediation analysis evaluated the effect of serum albumin, while network toxicology and molecular docking identified key targets and pathways. Linear regression showed that OCPs were negatively correlated with BMI z_score, ALM, TRF, TOF, TLM, and TPF in adolescents, particularly in males. The WQS and BKMR revealed a negative relationship between OCPs mixtures and BMI z_score, TRF, TOF, and TPF, with hexachlorobenzene (HCB) as the major contributor. Albumin mediated the negative effects of HCB on all body composition indicators. Preliminary bioinformatics analyses suggested that HCB may influence body composition through inflammation, metabolic regulation, and apoptosis involving the MAPK, PI3K-Akt, and Ras signaling pathways. These findings suggest that HCB exposure may adversely affect adolescent growth and nutritional health, particularly among males.

This study systematically investigates the hot deformation behavior of TC18 alloy under the conditions of deformation temperatures of 720-840 &#xb0;C and strain rates of 0.001-1 s-1. Based on the stress-strain data obtained under the aforementioned process parameters, a support vector regression (SVR) model was established and further optimized by using a Stacking algorithm to enhance predictive accuracy. Although SVR and Stacking techniques have been applied previously in material constitutive modeling, this paper presents a systematic optimization framework specifically for TC18, integrating comprehensive experimental data, kernel selection, hyperparameter tuning, and Stacking-based model fusion. The polynomial kernel function was identified as optimal, and hyperparameters were tuned via grid search combined with five-fold cross-validation, which is determined as {C = 1000, coef0 = 1, d = 5, &#x3b5; = 1, &#x3b3; = 1}. The Stacking-SVR model exhibits significantly improved fitting and generalization performance compared to Poly-SVR, Arrhenius, XGBoost and MLP, with RMSE, MAPE, and R2 metrics of 2.7882, 0.0110, and 0.9973 on the training set, and 2.7956, 0.0169, and 0.9982 on the test set, respectively. Additionally, the proportion of samples with relative errors within 5% reaches 98.7% for the training set and 94.83% for the test set. These results indicate that the proposed framework not only possesses extremely high predictive accuracy, but also ensures strong generalization ability and interpretability in practical applications.

Lithium (Li), nickel (Ni), cobalt (Co), and manganese (Mn) are widely distributed environmental metals and are also important constituent elements of Li-ion batteries. However, the potential health effects of mixed exposure to these metals in older adults remain unclear. This preliminary cross-sectional study investigated the associations between serum Li, Ni, Co, and Mn concentrations and CKD risk, assessed Li/Ni/Co/Mn (LNCM) mixture effects, assessed the effects of Li/Ni/Co/Mn (LNCM) mixture exposure, explored potential pathways, and evaluated the moderating role of healthy lifestyle behaviors. In a cross-sectional study of 844 community-dwelling older adults in Wuhan, China, serum Li, Ni, Co, and Mn were measured using ICP-MS. Logistic regression, weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and restricted cubic splines (RCS) were applied to assess single and mixed metal effects on CKD risk, while network toxicology and mediation/moderated mediation analyses were used to explore mechanisms and lifestyle interactions. Higher serum Li, Ni, and Mn concentrations and LNCM mixture exposure were positively associated with CKD risk, with Li contributing most strongly (weight&#xa0;=&#xa0;0.563). Network toxicology suggested lipid metabolism as a potential pathway, and mediation analysis indicated that triglycerides (TG) mediated 7.76%-11.00% of the metal-CKD associations. Healthy lifestyle behaviors attenuated TG elevation induced by LNCM exposure. These findings suggested that mixed exposure to Li, Ni, Co, and Mn were associated with increased CKD risk in older adults, with TG partially accounting for these associations, and that healthy lifestyle behaviors might attenuate these observed relationships.