Background Gestational weight gain is closely related to maternal and infant health outcomes. Pregnant women are simultaneously exposed to four metals—lithium (Li), vanadium (V), uranium (U), and bismuth (Bi)—through inhalation of fine particulate matter and consumption of contaminated food and water. Existing studies suggest that exposure to these metals may be associated with gestational weight gain. However, no study has yet explored the complex relationships between exposure to mixtures of these four metals and weight gain at different stages of pregnancy. Objective To investigate the associations between mixed exposure to Li, V, U, and Bi in early pregnancy and the average weekly gestational weight gain during both early pregnancy and mid-to-late pregnancy. Methods This prospective study recruited eligible women in early pregnancy from an obstetrics clinic of a tertiary hospital in Jinan, China, between September 2021 and July 2023. Pre-pregnancy weight, current weight (at 11⁺⁰ to 13⁺⁶ weeks of gestation), and spot urine samples (≥5.0 mL) were collected at enrollment. Urinary concentrations of Li, V, Bi, and U were determined using inductively coupled plasma mass spectrometry. Participants were followed up in late pregnancy (≥28 weeks of gestation) to collect information on physical activity via questionnaire; weight measurements at the last antenatal visit (35⁺⁰ to 37⁺⁶ weeks of gestation) were obtained from the hospital information system. After adjusting for covariates, multiple linear regression and generalized additive models were used to assess the associations of individual metals with weekly weight gain in early pregnancy and in mid-to-late pregnancy. Bayesian kernel machine regression (BKMR) and quantile-based g-computation (Qgcomp) were applied to evaluate the joint effects of the metal mixture exposure on weekly weight gain at the two gestational stages. Results A total of 313 pregnant women were included. The geometric means of urinary Li, V, U, and Bi concentrations were 37.07, 0.20, 0.06, and 0.04 μg·L⁻¹, respectively; after creatinine adjustment, the corresponding values were 46.82, 0.25, 0.07, and 0.05 μg·g⁻¹ (Cr). The mean weekly gestational weight gain was (0.19±0.25) kg in early pregnancy and (0.53 ± 0.18) kg in mid-to-late pregnancy. Both multiple linear regression and generalized additive models showed that urinary V concentration was positively associated with average weekly gestational weight gain in early pregnancy, while no significant associations were found for other metals or for gestational weight gain in mid-to-late pregnancy. In the BKMR model with early-pregnancy weight gain as the outcome, V had the strongest association [posterior inclusion probability (PIP)=0.773]. When other metals were fixed at their medians, V showed a positive non-linear association with the outcome. A significant single-metal effect of V and its interaction with Li were observed. Compared with the 50th percentile of the metal mixture, the average weekly weight gain in early pregnancy increased by 0.016 (95%CI: 0.003, 0.029) and 0.018 (95%CI: 0.001, 0.036) at the 60th and 65th percentiles, respectively; conversely, at the 25th percentile, it decreased by 0.026 (95%CI: 0.002, 0.050). Overall, the joint effect of the metal mixture on early- pregnancy weight gain showed an upward trend. In the BKMR model for mid-to-late pregnancy gestational weight gain, all PIPs were<0.5, and no significant single-metal effects, interactions, or joint effects were identified. Qgcomp results confirmed a positive association between the metal mixture and early-pregnancy weight gain (b=0.031, 95%CI: 0.010, 0.051; P<0.01), with V contributing the highest positive weight (0.71). No significant association was found for weight gain in mid-to-late pregnancy (b=0.007, P=0.339). Conclusion Higher levels of co-exposure to the Li, V, Bi, and U metal mixture during early pregnancy may be associated with increased average weekly weight gain in early pregnancy. Among these metals, V exhibits a predominant role and appears to interact with Li. No association is observed between early-pregnancy metal mixture exposure and average weekly gestational weight gain in mid-to-late pregnancy. These findings suggest that monitoring and managing metal exposure during early pregnancy may be crucial for the rational regulation of gestational weight gain.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To investigate the effect of stable low expression of DNA methyltransferase 3b（DNMT3b）on the adriamycin（ADM）resistance of wild-type bladder cancer cells BIU-87 and BIU-87/ADM-resistant cells（BIU-87/ADM）.Methods:Lentiviruses expressing DNMT3b siRNA and negative control siRNA were packaged. Stable DNMT3b-low-expressing BIU-87 cells（BIU-87-siRNA group），BIU-87/ADM cells（BIU-87/ADM-siRNA group），and corresponding control groups（BIU-87-NC group and BIU-87/ADM-NC group）were established via lentiviral infection. DNMT3b expression was detected by quantitative PCR and Western blot to validate siRNA interference efficiency. BIU-87-siRNA and BIU-87-NC cells were treated with 0.0125，0.025，0.05，0.1，and 0.2 mg/L ADM，while BIU-87/ADM-siRNA and BIU-87/ADM-NC cells were treated with 0.5，1，2，4，and 8 mg/L ADM. Cell survival rates were measured using the MTT assay to calculate the half-maximal inhibitory concentration（IC50）and relative reversal rate. Apoptosis was analyzed by flow cytometry. Expression of drug resistance-related genes（MRP1，P-gp and Survivin）was detected by quantitative PCR and Western blot. In vivo tumorigenesis experiments were performed by subcutaneously inoculating BIU-87-siRNA，BIU-87/ADM-siRNA，and control group cells into nude mice. Tumor sizes were measured on days 7，10，13，16，20，and 25 to plot growth curves and assess the effect of DNMT3b low expression on ADM resistance.Results:Quantitative PCR showed that the relative mRNA expression of DNMT3b in BIU-87-siRNA and BIU-87-NC groups were（0.32 ± 0.08） vs.（1.00±0.12）（ P < 0.01），and（0.30 ± 0.07） vs.（1.00 ± 0.11）in BIU-87/ADM-siRNA vs. BIU-87/ADM-NC groups（ P < 0.01）. Western blot confirmed significantly reduced DNMT3b protein levels in both siRNA groups（ P < 0.01）. After ADM treatment，BIU-87-siRNA cells exhibited lower survival rates compared to BIU-87-NC at 0.025，0.05，and 0.1 mg/L ADM（ P < 0.05），with IC50 values of（0.14 ± 0.02）mg/L vs.（0.18 ± 0.03）mg/L（ P > 0.05）. For BIU-87/ADM-siRNA cells，survival rates at 1，2，4，and 8 mg/L ADM were significantly lower than controls（ P < 0.05），with IC50 values of（7.10 ± 0.45）mg/L vs.（13.96 ± 1.20）mg/L and a relative reversal rate of 49.76%（ P < 0.01）. Apoptosis rates were significantly higher in siRNA groups（ P < 0.01）. mRNA and protein levels of MRP1，P-gp，and Survivin were reduced in both siRNA groups（ P < 0.05），except for P-gp protein in BIU-87-siRNA cells（ P > 0.05）. In vivo，tumor volumes in siRNA groups were significantly smaller than controls by day 25（ P < 0.05）. Conclusion:Stable low expression of DNMT3b reverses ADM resistance in bladder cancer BIU-87/ADM cells.

Objectives:This study aims to investigate the correlation between urinary crystals and the components of urinary calculi in patients with urinary calculi，as well as the accuracy of urine crystals in predicting stone components.Methods:A retrospective analysis was performed on 280 patients with positive urine crystal and urinary calculi from January 2022 to December 2024. There were a total of 280 patients consisting of 185 males and 95 females，aged from 23 to 80 years，with an average age of（49.1 ± 12.3）years. Among them，there were 243 cases of renal stones or both renal and ureteral stones，25 cases of ureteral stones，and 12 cases of bladder stones. In all cases，10 ml of morning urine was collected preoperatively and sent for examination within one hour. After centrifuging at 400 g for 1 minute，the urine sediment was examined under a microscope. All positive crystals were categorized into calcium oxalate，uric acid，calcium phosphate，magnesium ammonium phosphate，and cystine based on the morphology of the crystals. Calculi were collected after endoscopic surgery，calculi composition was analyzed using infrared spectroscopy，and the main component（the first predominant component）was recorded. Statistical analysis was conducted using a 5×5 contingency table to analyze the correlation and contingency coefficient，and the positive predictive values of the urinary crystals for predicting calculi components were calculated. Results:Among the 280 patients，calcium oxalate crystals were found in 241 cases，uric acid in 25 cases，calcium phosphate in 7 cases，magnesium ammonium phosphate in 5 cases，and cystine in 2 cases. The main components of 280 calculi were calcium oxalate in 232 cases，uric acid in 21 cases，calcium phosphate in 24 cases，magnesium ammonium phosphate in 1 case，and cystine in 2 cases. There was a statistically significant correlation between urinary crystals and stone components（ χ2 = 152.46， P < 0.01），and the contingency coefficient between crystals and calculi components was 0.809. The overall positive expected value of urine crystals was 87.5%（245/280），among which the positive expected value of calcium oxalate crystals was 91.7%（221/241），uric acid crystals was 72.0%（18/25），calcium phosphate was 42.9%（3/7），magnesium ammonium phosphate was 20.0%（1/5），and cystine was 100.0%（2/2）. Conclusions:The urinary crystals of patients with calculi are significantly related to the main components of the calculi. Using urinary crystals to predict the components of the calculi has a relatively high accuracy.

Objective To investigate the relationship between serum microRNA-132(miRNA-132)and mi-croRNA-345(miRNA-345)expression,tumor markers and prognosis in gastric cancer patients.Methods A total of 103 gastric cancer patients from the hospital from January 2020 to January 2022 were selected as study group.82 patients with benign gastric diseases in the hospital during the same period were selected as the benign group.Another 61 healthy individuals who underwent physical examinations in the hospital during the same period were selected as control group.Real-time polymerase chain reaction(qPCR)was used to measure the expression of miRNA-132 and miRNA-345.The levels of carcinoembryonic antigen(CEA),carbohydrate anti-gen 199(CA199)and glycoprotein antigen 724(CA724)were measured using electrochemiluminescence.All patients were followed up until June 2024,and the overall survival(OS)of gastric cancer patients was calcu-lated through outpatient or telephone follow-up.Results The relative expression level of miRNA-132 in the serum of the study group was higher than that of the benign group and the control group(P＜0.05),while the relative expression level of miRNA-345 was lower than that of the benign group and the control group(P＜0.05).The serum levels of CEA,CA199,and CA724 in the study group were higher than those in the be-nign group and the control group(P＜0.05).Pearson analysis showed that miRNA-132 was positively correla-ted with CEA,CA199,and CA724(P＜0.05),while miRNA-345 was negatively correlated with CEA,CA199,and CA724(P＜0.05).Patients with a relative expression level of miRNA-132≤2.76 had better OS than those with a relative expression level of miRNA-132＞2.76(P＜0.05).Patients with a relative expres-sion level of miRNA-132 greater than 0.37 have better OS than those with a relative expression level of miR-NA-132≤0.37(P＜0.05).High expression of miRNA-132 and low expression of miRNA-345 were risk fac-tors affecting the prognosis of gastric cancer patients(P＜0.05).Conclusion Patients with gastric cancer have high expression of miRNA-132 and low expression of miRNA-345,and the expression of miRNA-132 and miRNA-345 is closely related to tumor markers and prognosis,which is worthy of clinical reference.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective To compare the diagnostic performance of two syphilis reverse testing protocols recommended by the U.S.Centers for Disease Control and Prevention(USCDC)and the European Centre for Disease Prevention and Control(ECDC)within maternal and child populations,and to explore the factors contributing to false-positive results in chemiluminescent immunoassays(CLIA).Methods A retrospective analysis was performed on serological test results from 109,003 patients collected between 2021 and 2023.All participants were initially screened for specific syphilis antibodies using CLIA,and those with positive results underwent confirmatory testing with both the toluidine red unheated serum test(TRUST)and the Treponema pallidum particle agglutination assay(TPPA).The kappa statistic was employed to assess the diagnostic agreement between the USCDC and ECDC protocols,while also analyzing the distribution patterns of CLIA false-positive results across varying S/CO value ranges and demographic groups.Results Both protocols achieved a syphilis positivity rate of 0.34%.The hospital's high-prevalence syphilis screening program identified no cases with CLIA-positive or TRUST-positive but TPPA-negative results,suggesting high specificity in this population.The two protocols demonstrated perfect agreement with a Kappa value of 1.0,indicating no significant difference in diagnostic performance between maternal and child populations.CLIA exhibited a true positive rate of 71.04%,which was positively correlated with S/CO values:21.88%when 1.010.The highest false positive rate for CLIA(38.75%)was observed among young adults,while the largest proportion of false positives-reaching 40.00%—was associated with pregnancy and abortion status.Conclusions The two reverse detection methods for syphilis exhibit comparable diagnostic efficacy in women and children from populations with low syphilis prevalence.CLIA demonstrates high sensitivity;however,when the S/CO ratio is less than 10,particularly in adult women of childbearing age,clinicians should remain vigilant for potential false-positive results to prevent misdiagnosis and unnecessary medical interventions.