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Several studies have explored the relationship between various aspects of work and the onset of depressive symptoms. However, there is a lack of research focusing on the association between job types and depressive symptoms. This study aims to investigate the impact of agricultural work on depressive symptoms and whether life satisfaction mediates this relationship. Data were obtained from the 2015 China Health and Retirement Longitudinal Study (CHARLS) (n = 6856). Participants were categorized based on whether they were engaged in agricultural or non-agricultural work and further classified as self-employed or employed. Depressive symptoms and life satisfaction were assessed using the CES-D and SWLS scales. Logistic regression analysis was used to examine associations, and Baron and Kenny's mediation test and the Sobel test were used to assess the mediating effect of life satisfaction. Engaging in agricultural work was positively associated with increased depressive symptoms scores (B = 3.437, p < 0.001), indicating that agricultural work exacerbates depressive symptoms. This effect was partially mediated by life satisfaction. Self-employed agricultural workers are a high-risk group for depressive symptoms. Additionally, life satisfaction plays a mediating role between type of job and depressive symptoms. Public health recommendations aimed at improving or mitigating depressive symptoms among agricultural workers could focus on enhancing life satisfaction to promote healthier psychological status.

Chronic diseases are highly prevalent among older adults and may be associated with their ability to achieve successful aging, which encompasses five key components: absence of major chronic diseases, freedom from disability, high cognitive function, no depressive symptoms, and active social participation. However, many disease conditions are excluded from conventional definitions of successful aging. This study aims to quantify the predictive effects of multiple chronic diseases on overall successful aging and its five core components, thus providing new evidence to refine aging frameworks. Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS) covering the years 2011, 2013, and 2015. Six common chronic diseases not traditionally included in the successful aging definition were selected: hypertension, dyslipidemia, arthritis, kidney disease, liver disease, and digestive disease. Six machine learning models were applied to construct prediction frameworks of successful aging and chronic diseases. The SHapley Additive Explanations (SHAP) and ALE (Accumulated Local Effects) analyses were used to quantify the impact of each chronic disease on the prediction of successful aging, providing both global and individual-level interpretability. Logistic regression was conducted to examine the associations between key diseases identified by SHAP and the five components of successful aging. A total of 4,385 participants were included initially in this study, and a total of 1,104 participants were selected after propensity score matching for subsequent analysis. After hyperparameter tuning with Optuna, the XGBoost model was chosen for model interpretation and prediction (F1 = 0.781, F2 = 0.891, AUC = 0.707, AUPRC = 0.724). SHAP analysis indicated that hypertension, kidney disease, and arthritis were the most influential predictors of successful aging. Additionally, SHAP results highlighted that sleep duration was also among the most important features. Subsequent logistic regression further revealed that, beyond their associations with the disease component, kidney disease and arthritis were significantly linked to depressive symptoms and cognitive function, while hypertension was strongly associated with physical functioning. Our findings highlight that several chronic diseases not traditionally included in successful aging criteria are significantly associated with aging outcomes. Extending the disease spectrum within the definitions of successful aging may enhance individual-level assessment and provide insights for future research on targeted health interventions for the older adults. Furthermore, these findings may help raise awareness of health factors associated with successful aging.

Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( ) and sleeve friction ( ) as input variables. Pearson correlation and sensitivity analysis confirmed that these variables are highly correlated with the classification results. To enhance classification performance, 25 optimization algorithms were applied, and the models were validated against an independent dataset of 208 CPT records. The results revealed that 23 of the algorithms successfully improved the SVM classification accuracy. Among these, 18 algorithms achieved higher accuracy than the current engineering standard, the “Code for in-situ Measurement of Railway Engineering Geology.” Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. In conclusion, integrating evolutionary algorithms with SVM for soil classification offers a promising approach to enhancing the efficiency and accuracy of soil analysis in engineering applications.

Background Arthritis, a common condition in the U.S., is caused by autoimmune reactions, osteoarticular injuries, and uric acid deposition. It affects around 25% of adults and is strongly linked to cardiovascular disease. The American Heart Association’s Life’s Essential 8 (LE8) composite, which considers cardiovascular health factors, has been studied for its predictive capacity in assessing arthritis risk. The study aims to provide tailored prevention recommendations and monitor factors associated with arthritis within the LE8 framework specifically for arthritic individuals. Methods This population-based study used data from the U.S. NHANES (2017–2020) for adults aged 20 + . Multivariate logistic and restricted cubic spline modeling explored the relationship between LE8 and arthritis, revealing significant nonlinearity ( P  < 0.001). Gradient Boosting Decision Trees were employed to conduct a predictive model of Arthritis risk. Results Through study, we found that idealer LE8 scores correlated with poorer arthritis risk. Subgroup analyses ideallighted poorer scores for smoking ( P  < 0.002, poor_socre and intermediate_score) and physical activity( P  = 0.001, poor_score) as significant risk factors. Gradient Boosting Decision Trees predicted disease risk, with age, HDL cholesterol, and blood pressure identified as the three most significant predictive factors. Conclusion The LE8 score in U.S. adults shows a negative association with arthritis outcomes, with arthritis prevalence decreasing as the score increases. poorer Smoke and Physical Exercise Time components and intermediate Blood Glucose scores may be arthritis risk factors. The LE8 may help identify arthritis risk early and reduce its burden.

Alkalinity production is one of the most typical and widespread salinization hazards on the Loess Plateau. Based on the characterization of typical flooding sites and the results of salt monitoring, this study investigates the deterioration mechanism of salinization on Zhouqiao site. The orthogonal test was used to simulate the effects of different concentrations of MgSO 4 , NaCl and CaCl 2 under natural conditions on the quality change, salt analysis out location, surface phenomenon, strength and electrical conductivity of the soil at the Zhouqiao site, and to make a preliminary analysis on the mechanism of saline deterioration of the site soil. The results show that the soil column mass increased significantly under the action of salt, and the rate of salt absorption in the soil column decreased when the critical value was reached, and the critical values were different under the action of different kinds of salts. The rate of salt analysis is also influenced by the salt concentration and the number of cycles, which gradually increases with the increase of salt concentration and the number of cycles. The nominal strength of the soil column with the number of cycles, but occasionally increases. The conductivity increases with the number of cycles, and the magnitude distribution of the conductivity of the soil column under the action of different salts is not exactly the same.

Archeological evidence from loess sediments from Shangchen on the southeastern Chinese Loess Plateau indicates a suspension of hominin occupation around the time of the early mid‐Pleistocene climate transition, prompting a re‐assessment of climate‐vegetation‐hominin interactions. Loess deposits with in situ lithic records cover the period of hominin occupation and reveal four distinct climate‐vegetation periods (2.1–1.8, 1.8–1.26, 1.26–0.9, and 0.9–0.6 Ma). Major oscillations in climate superimposed upon an aridification trend and an expansion of C4 herbaceous vegetation from about 1.26 Ma may have driven early humans to move to more hospitable locations in the region. Comparison with the record at Nihewan indicates that large‐scale climate oscillations induced disparate hominin responses due to distinctive local environmental conditions. Plain Language Summary A combination of several lines of evidence suggests that changes in climate and their impacts on the availability of food and shelter from around 1.26 million years ago may have driven the dispersal of early humans from the southeastern Chinese Loess Plateau to more hospitable environments elsewhere in the region. Key Points Pleistocene temperature, rainfall, and C4 plants ratios are quantitatively reconstructed from the Shangchen loess deposits with artifacts High climate variability, C4 plants expansion and aridification suspended hominin occupation at Shangchen during the early mid‐Pleistocene climate transition Hominins responded differently to large‐scale climate oscillations depending on distinctive local environmental conditions

Background Abemaciclib, an oral kinase inhibitor, is used to treat hormone receptor–positive and HER2-negative breast cancer patients. However, there has been a decrease in studies reporting adverse reactions to abemaciclib-related kidney injuries. Thus, this study was aimed at assessing its safety profile using a large-scale pharmacovigilance database. Methods Abemaciclib-related adverse drug reaction reports from the Food and Drug Administration Adverse Event Reporting System were obtained and scrutinized, and adverse drug reactions were selected using reporting odds ratio, the proportional reporting ratio methods, empirical Bayes geometric mean and UK Medicines and Healthcare products Regulatory Agency methods. Results We selected 10,757 matched reports associated with abemaciclib, among which we found eight adverse reactions about kidney injuries correlated with abeamciclib, such as increased blood creatinine, renal disorder, decreased glomerular filtration rate, increased blood urea, hydronephrosis, abnormal renal function test, increased creatinine renal clearance and increased cystatin C. A demographic analysis of reported cases of abemaciclib-associated renal injury revealed that the majority were female, aged ≥46 years and had taken the drug ≥30 days. Conclusion This study highlights the characteristics of adverse reactions with abemaciclib and those associated with renal damage, which are crucial for safety studies on the clinical use of this drug. Plain language summary Objective Abemaciclib is an oral kinase inhibitor commonly used to treat hormone receptor–positive and HER2-negative breast cancer. Although it has shown efficacy in treating breast cancer, there have been concerns about its potential to cause kidney injuries. Despite this, studies addressing the adverse effects of abemaciclib on kidney function have been limited. This study aimed to assess the safety profile of abemaciclib, focusing on its association with kidney-related adverse events. Methods We conducted a retrospective analysis of adverse drug reactions (ADRs) related to abemaciclib using the Food and Drug Administration Adverse Event Reporting System (FAERS). The study focused on ADRs associated with kidney injuries, and we employed several disproportionality analysis methods to identify potential signals of kidney-related adverse reactions. These methods included the reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM), and UK Medicines and Healthcare products Regulatory Agency (MHRA) methods. Results Our analysis identified a total of 10,757 reports associated with abemaciclib. Among these, we found eight distinct kidney-related adverse reactions, including increased blood creatinine, renal disorders, decreased glomerular filtration rate, increased blood urea, and abnormal renal function tests. A demographic analysis revealed that the majority of the affected patients were female, over 46 years of age, and had been taking abemaciclib for more than 30 days. Conclusion This study provides valuable insights into the kidney-related adverse effects of abemaciclib. The findings suggest that kidney injuries may be an underreported side effect of this medication, particularly among women and those on prolonged treatment regimens. Further research is needed to better understand the renal safety of abemaciclib and to develop guidelines for managing potential kidney-related risks in patients undergoing treatment.

The coupling of the microfluidic chip to mass spectrometry (MS) has attracted considerable attention in the area of chemical and biological analysis. The most commonly used ionization technique in the chip–MS system is electrospray ionization (ESI). Traditional chip-based ESI devices mainly employ direct electrical contact between the electrode and the spray solvent. In this study, a microchip ESI source based on a novel polarization-splitting approach was developed. Specifically, the droplet in the microchannel is first polarized by the electric field and then split into two sub-droplets. In this process, the charge generated by polarization is retained in the liquid, resulting in the generation of two charged droplets with opposite polarities. Finally, when these charged droplets reach the emitter, the electrospray process is initiated and both positive and negative ions are formed from the same solution. Preliminary experimental results indicate that the coupling of this polarization-splitting ESI (PS-ESI) chip with a mass spectrometer enables conventional ESI-MS analysis of various analytes.

Rolled Zn-0.8Li-0.2Ag(wt%) alloy as candidates for biodegradable materials. The biodegradable behavior of Zn-0.8Li-0.2Ag alloy in different solutions (Ringer's, DMEM, SBF and DMEMp) was investigated. The cytotoxicity of Zn-0.8Li-0.2Ag alloy and its antibacterial properties against staphylococcus aureus, enterobacter faecalis and candida albicans were evaluated. The results showed that Zn-0.8Li-0.2Ag alloy consists of zinc matrix and a LiZn4 secondary phase. The presence of Cl− causes locally corroded of Zn-0.8Li-0.2Ag alloy in Ringer's solution, and its corrosion resistance is lower than that of the alloy which is uniformly corroded in other solutions containing CO32− and PO43−. Zn-0.8Li-0.2Ag alloy is non-toxic and exhibits better antibacterial properties than the experimental reference group without silver.

Generally, brick buildings are in the open-air environment year round, and damage to them is aggravated by the effect of repeated freezing and thawing cycles. In order to determine freeze–thaw damage and deterioration mechanism, the initial moisture content of gray brick specimens was set as 20%, 40%, 60%, 80%, 100%. The effects of moisture content and the number of freeze–thaw cycles on the quality, mechanical properties and microstructure of gray brick were investigated by uniaxial compression tests and scanning electron microscopy (SEM) tests. Numerical simulations were applied to model the freezing and thawing process. The results showed that: as the number of freeze–thaw cycles increased, the mass loss rate and peak strength reduction rate of gray brick both increased. The initial moisture content had a greater impact on damage to gray brick due to freeze–thaw; ω = 80% was defined as the limit moisture content of gray brick. Under the repeated action of freeze–thaw cycles, the areas affected by thermal stress were mainly concentrated in the center of the outer surface and the center of the side of gray bricks. The maximum thermal stress after 55 freeze–thaw cycles was 1.522 × 10−2 MPa. This research results provide a theoretical basis for the prevention and protection of frost damage of brick buildings in a freeze–thaw environment.