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Food safety is a major concern for the Chinese public. This study collected 465 published papers on heavy metal pollution rates (the ratio of the samples exceeding the Grade II limits for Chinese soils, the Soil Environmental Quality Standard-1995) in farmland soil throughout China. The results showed that Cd had the highest pollution rate of 7.75%, followed by Hg, Cu, Ni and Zn, Pb and Cr had the lowest pollution rates at lower than 1%. The total pollution rate in Chinese farmland soil was 10.18%, mainly from Cd, Hg, Cu, and Ni. The human activities of mining and smelting, industry, irrigation by sewage, urban development, and fertilizer application released certain amounts of heavy metals into soil, which resulted in the farmland soil being polluted. Considering the spatial variations of grain production, about 13.86% of grain production was affected due to the heavy metal pollution in farmland soil. These results many provide valuable information for agricultural soil management and protection in China.

Drought has been one of the most important limiting factors for crop production, which deleteriously affects food security worldwide. The main objective of the present study was to quantitatively assess the effect of drought on the agronomic traits (e.g., plant height, biomass, yield, and yield components) of rice and wheat in combination with several moderators (e.g., drought stress intensity, rooting environment, and growth stage) using a meta-analysis study. The database was created from 55 published studies on rice and 60 published studies on wheat. The results demonstrated that drought decreased the agronomic traits differently between rice and wheat among varying growth stages. Wheat and rice yields decreased by 27.5% and 25.4%, respectively. Wheat grown in pots showed greater decreases in agronomic traits than those grown in the field. Rice showed opposite growing patterns when compared to wheat in rooting environments. The effect of drought on rice increased with plant growth and drought had larger detrimental influences during the reproductive phase (e.g., blooming stage, filling stage, and maturity). However, an exception was found in wheat, which had similar decreased performance during the complete growth cycle. Based on these results, future droughts could produce lower yields of rice and wheat when compared to the current drought.

To explore the relationship of gut microbiota with the development of type 2 diabetes (T2DM), we analyzed 121 subjects who were divided into 3 groups based on their glucose intolerance status: normal glucose tolerance (NGT; n = 44), prediabetes (Pre-DM; n = 64), or newly diagnosed T2DM (n = 13). Gut microbiota characterizations were determined with 16S rDNA-based high-throughput sequencing. T2DM-related dysbiosis was observed, including the separation of microbial communities and a change of alpha diversity between the different glucose intolerance statuses. To assess the correlation between metabolic parameters and microbiota diversity, clinical characteristics were also measured and a significant association between metabolic parameters (FPG, CRP) and gut microbiota was found. In addition, a total of 28 operational taxonomic units (OTUs) were found to be related to T2DM status by the Kruskal-Wallis H test, most of which were enriched in the T2DM group. Butyrate-producing bacteria (e.g. Akkermansia muciniphila ATCCBAA-835, and Faecalibacterium prausnitzii L2-6) had a higher abundance in the NGT group than in the pre-DM group. At genus level, the abundance of Bacteroides in the T2DM group was only half that of the NGT and Pre-DM groups. Previously reported T2DM-related markers were also compared with the data in this study, and some inconsistencies were noted. We found that Verrucomicrobiae may be a potential marker of T2DM as it had a significantly lower abundance in both the pre-DM and T2DM groups. In conclusion, this research provides further evidence of the structural modulation of gut microbiota in the pathogenesis of diabetes.

Objective To detect the expression of CEA-related cell adhesion molecule 5 (CEACAM5) in non-small-cell lung cancer (NSCLC) and explore its function in the progression and development of NSCLC. Methods qRT-PCR and immunohistochemistry were performed to detect CEACAM5 expression in human NSCLC tissues and cell lines. The correlation between CEACAM5 expression and the clinicopathological features of patients with NSCLC was also investigated. MTT, colony formation, wound healing, and immunoblot assays were performed to detect the functions of CEACAM5 in NSCLC cells in vitro, and immunoblotting was used to detect the effects of CEACAM5 on p38–Smad2/3 signaling. Results CEACAM5 expression was elevated in human NSCLC tissues and cells. We further found that CEACAM expression was correlated with clinicopathological features including T division, lymph invasion, and histological grade in patients with NSCLC. The in vitro assays confirmed that CEACAM5 depletion inhibited the proliferation and migration of NSCLC cells by activating p38–Smad2/3 signaling. We verified the involvement of CEACAM5 in the suppression of NSCLC tumor growth in mice. Conclusion CEACAM5 stimulated the progression of NSCLC by promoting cell proliferation and migration in vitro and in vivo. CEACAM5 may serve as a potential therapeutic target for the treatment of NSCLC.

Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design of current commercial Li-ion batteries. However, its practical utility for multivalent metal-ion batteries (MIBs), the most promising future solution of large-scale energy storage, is limited due to scarce MIB data availability and poor DL model interpretability. Here, we develop an interpretable DL model as an effective and accurate method for learning electrode voltages of multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at small dataset limits (150–500). Using the experimental results as validation, our model is much more accurate than machine-learning models, which usually are better than DL in the small dataset regime. Besides the high accuracy, our feature-engineering-free DL model is explainable, which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network. The presented model potentially accelerates the design and optimization of multivalent MIB materials with fewer data and less domain-knowledge restriction and is implemented into a publicly available online tool kit in http://batteries.2dmatpedia.org/ for the battery community.

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.

Background Leptin is a peptide hormone secreted by adipose tissue and is an important determinant of obesity and its complications. The purpose of this study was to establish sex- and body mass index (BMI)-specific reference intervals for serum leptin in a Chinese population and investigate the factors influencing leptin concentrations. Methods Fasting serum leptin levels were assayed in 469 men and 773 women from randomly sampled Chinese residents. Blood glucose, insulin, hemoglobin A1c (HbA1c), liver enzymes, blood lipid profiles, creatinine, and uric acid (UA) levels were measured. Pearson’s correlation coefficient and multiple linear regression analyses were used to estimate the relationship between serum leptin level and other variables. The reference intervals were determined by the 2.5th and 97.5th percentiles. Results The mean ± standard deviation serum leptin level was much higher in women (20.92 ± 12.96 ng/mL) than in men (6.45 ± 5.53 ng/mL). The reference interval of serum leptin was 0.33–19.85 ng/mL in men and 3.60–54.86 ng/mL in women. The specific reference intervals of serum leptin in men with BMI of 20 to < 25 and 25 to < 27.5 kg/m 2 were 0.42–12.32 and 2.17–20.22 ng/ml, respectively. The specific reference intervals of serum leptin in women with BMI of 20 to < 25 and 25 to < 27.5 kg/m 2 were 4.11–38.09 and 8.27–48.66 ng/ml, respectively. BMI was significantly correlated with Ln (leptin) both in men (r = 0.698, P  < 0.001) and women (r = 0.626, P  < 0.001). In multivariate linear regression analysis, serum leptin was correlated with BMI, homeostasis model assessment of insulin resistance (HOMA-IR), UA in women, and plus triglyceride (TG) in men. The variance in serum leptin levels could be partially explained by these variables in both women (adjusted R 2  = 0.447) and men (adjusted R 2  = 0.552). In participants with leptin levels higher than the reference intervals, significantly higher levels of HOMA-IR, low-density lipoprotein cholesterol (LDL-C), UA, a higher proportion of central obesity (waist circumference [WC] > 90 cm), and metabolic syndrome were found in men, and significantly higher levels of HOMA-IR, UA and a higher proportion of central obesity (WC > 85 cm) were found in women. Conclusion This is the first study to establish sex- and BMI-specific reference intervals of leptin for both sexes in a large Chinese population. Serum concentration of leptin was predicted by BMI, HOMA-IR, UA in women, and TG in men.

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and super large twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax’s reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Structure relaxation is an iterative process particularly demanding in large-scale material discovery campaigns. Here, the authors realize a deep generative model able to relax material structures in a single step while estimating its accuracy.

The aim of this study was to determine the effect of exposure to different antithyroid drugs during pregnancy on the incidence of neonatal congenital malformations. A meta-analysis was performed to compare the incidence of neonatal congenital malformations after exposure to different antithyroid drugs during pregnancy. Twelve studies that met the inclusion criteria were included in this meta-analysis. PubMed, Embase, and CENTRAL databases were searched from inception until January 2017. Study designs included case-control studies, prospective cohort studies, and retrospective cohort studies. Twelve studies involving 8028 participants with exposure to different antithyroid drugs during pregnancy were included in this study; however, only 10 studies involving 5059 participants involved exposure to different antithyroid drugs exactly during pregnancy. Our results indicated that exposure to methimazole (MMI)/carbimazole (CMZ) only during pregnancy significantly increased the risk of neonatal congenital malformations compared to no antithyroid drug exposure (OR 1.88; 95%CI 1.33 to 2.65; P = 0.0004). No differences were observed between propylthiouracil (PTU) exposure and no antithyroid drug exposure only during pregnancy (OR 0.81; 95%CI 0.58 to 1.15; P = 0.24). Exposure to MMI/CMZ only during pregnancy significantly increased the risk of neonatal congenital malformations compared to that associated with exposure to PTU (OR 1.90; 95%CI 1.30 to 2.78; P = 0.001). For pregnant women with hyperthyroidism, exposure to MMI/CMZ significantly increased the incidence of neonatal congenital malformations compared to exposure to PTU and no antithyroid drug exposure; however, no differences were observed between PTU exposure and no antithyroid drug exposure.

Background. Cerebral ischemia–reperfusion injury is a common complication of ischemic stroke that affects the prognosis of patients with ischemic stroke. The lipid-soluble diterpene Tanshinone IIA, which was isolated from Salvia miltiorrhiza, has been indicated to reduce cerebral ischemic injury. In this study, we investigated the molecular mechanism of Tanshinone IIA in alleviating reperfusion-induced brain injury. Methods. Middle cerebral artery occlusion animal models were established, and neurological scores, tetrazolium chloride staining, brain volume quantification, wet and dry brain water content measurement, Nissl staining, enzyme-linked immunosorbent assay, flow cytometry, western blotting, and reverse transcription–quantitative polymerase chain reaction were performed. The viability of cells was measured by 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide assays, while cell damage was measured by lactate dehydrogenase release in the in vitro oxygen glucose deprivation model. In addition, enzyme-linked immunosorbent assay, flow cytometry, western blotting, and reverse transcription–quantitative polymerase chain reaction were used to evaluate the therapeutic effect of Tanshinone IIA on ischemia/reperfusion (I/R) induced brain injury, as well as its effects on the inflammatory response and neuronal apoptosis, in vivo and in vitro. Furthermore, this study validated the targeting relationship between miR-124-5p and FoxO1 using a dual luciferase assay. Finally, we examined the role of Tanshinone IIA in brain injury from a molecular perspective by inhibiting miR-124-5p or increasing FoxO1 levels. Results. After treatment with Tanshinone IIA in middle cerebral artery occlusion–reperfusion (MCAO/R) rats, the volume of cerebral infarction was reduced, the water content of the brain was decreased, the nerve function of the rats was significantly improved, and the cell damage was significantly reduced. In addition, Tanshinone IIA effectively inhibited the I/R-induced inflammatory response and neuronal apoptosis, that is, it inhibited the expression of inflammatory cytokines IL-1β, IL-6, TNF-α, decreased the expression of apoptotic protein Bax and Cleaved-caspase-3, and promoted the expression of antiapoptotic protein Bcl-2. In vitro oxygen-glucose deprivation/reoxygenation (OGD/R) cell model, Tanshinone IIA also inhibited the expression of inflammatory factors in neuronal cells and inhibited the occurrence of neuronal apoptosis. In addition, Tanshinone IIA promoted the expression of miR-124-5p. Transfection of miR-124-5p mimic has the same therapeutic effect as Tanshinone IIA and positive therapeutic effect on OGD cells, while transfection of miR-124-5p inhibitor has the opposite effect. The targeting of miR-124-5p negatively regulates FoxO1 expression. Inhibition of miR-124-5p or overexpression of FoxO1 can weaken the inhibitory effect of Tanshinone IIA on brain injury induced by I/R, while inhibition of miR-124-5p and overexpression of FoxO1 can further weaken the effect of Tanshinone IIA. Conclusion. Tanshinone IIA alleviates ischemic–reperfusion brain injury by inhibiting neuroinflammation through the miR-124-5p/FoxO1 axis. This finding provides a theoretical basis for mechanistic research on cerebral ischemia–reperfusion injury.