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.

Urolithiasis represents a prevalent clinical challenge marked by high recurrence rates and morbidity，with existing preventive strategies struggling to effectively curb its epidemic trajectory，thereby posing a significant threat to public health. The etiology of this condition is intricate，involving a complex network of interactions spanning classical supersaturation-crystallization theory，Randall’s plaque theory，and multifactorial elements such as cellular injury，inflammatory responses，metabolic derangements，the gut-kidney axis，immune dysregulation，and genetic predisposition. However，the critical mechanisms initiating stone formation and the early pathophysiological processes remain incompletely elucidated，constituting the core impasse in current preventive strategies. This review systematically synthesizes classical theories and cutting-edge advancements in urolithiasis etiology research，emphasizing the urgent need to integrate emerging technologies，including high-dimensional omics，advanced imaging modalities，and artificial intelligence，to dissect pivotal pathological nodes in early stone formation. Such interdisciplinary efforts are essential to overcome cognitive bottlenecks and ultimately achieve personalized，precision-based prevention strategies.

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.

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.

Nasojejunal feeding tubes are widely used in surgical,intensive care,and older patients.Manual blind insertion of nasojejunal feeding tubes is technically challenging,associated with a high failure rate,and prone to complications.The primary causes of suboptimal placement outcomes are the uncertainty and weak controllability of the interaction forces during the coordination between manual posterior advancement and the patient's physiological state.While current auxiliary techniques such as X-ray,ultrasound,and endoscopy can improve the success rate of nasojejunal tube placement and reduce complications to some extent,the accuracy and safety of placement remain constrained by challenges in controlling insertion forces and achieving precise positional localization.Robotic technology holds promise for addressing the uncertainties and controllability issues inherent in the placement process.By leveraging precise sensing,real-time navigation,and efficient control,robots can achieve intelligent positioning and precise control over the direction and location of the catheter tip during nasojejunal intubation.However,current research on robotic applications for nasojejunal feeding tube placement is still in an early stage,facing challenges such as high costs,operational complexity,and concerns over safety and reliability.Herein,we analyzed the limitations and causes of failure in existing placement methods and explored the application prospects of robotic technologies for precise control and intelligent positioning in nasojejunal feeding tube placement.The paper provides new insights for developing nursing techniques that enable safer and more effective,comfortable,and rapid intubation.Future efforts should focus on deepening the integration of artificial intelligence and robotics,optimizing drive technologies,and accelerating the translation of these technologies from the laboratory to clinical practice.This will drive the advancement of nasojejunal feeding tube placement techniques towards intelligent,precise,and accessible solutions.

Objective: To analyze the characteristics of first-visit cases of herpes zoster in Zhoushan City, Zhejiang Province from 2021 to 2023, so as to provide the reference for improving herpes zoster prevention and control measures. Methods: Data on the incidence and vaccination of first-visit herpes zoster cases at all levels of public medical institutions in Zhoushan City from 2021 to 2023 were collected through the Zhoushan Comprehensive Health Information Platform and Zhoushan Immunization Program Information Management System. The incidence and outpatient proportion were calculated. The population distribution, seasonal distribution, and clinical consultation status of first-visit herpes zoster cases were described. Results: From 2021 to 2023, a total of 15 156 first-visit herpes zoster cases were reported in Zhoushan City, with an average annual incidence of 5.36‰. The incidences for each year were 5.78‰, 5.29‰ and 5.02‰, respectively, and the outpatient proportions were 0.15%, 0.14% and 0.11%, respectively, showed decreasing trends (both P<0.05). The number of doses of recombinant herpes zoster vaccine or live attenuated herpes zoster vaccine administered were 130, 312, and 633, respectively. The main consultation department was dermatology, with 11 004 cases (72.60%). The primary clinical diagnosis was visceral herpes zoster, with 5 901 cases (38.94%). A total of 1 936 cases (12.77%) had at least one underlying medical condition. The mean age of onset was (56.08±16.23) years, and the incidence showed an upward trend with increasing age (P<0.05). There were 7 386 male cases and 7 770 female cases, with a male-to-female ratio of 0.95∶1. The incidence among males aged ≥50 years was lower than that among females (6.53‰ vs. 8.69‰, P<0.05). The onset of the disease exhibited a significant seasonal pattern, with a peak period from June 21st to August 21st, covering 75% of the cases Conclusions From 2021 to 2023, the incidence and outpatient proportion of herpes zoster in Zhoushan City decreased. Summer was the peak season for onset, and women and the elderly were the key populations. It is necessary to strengthen the collaborative diagnostic and treatment capabilities of key departments such as dermatology and enhance the enthusiasm for vaccination among key populations.

BACKGROUND: The primary limitation to kidney transplantation is organ shortage. Recent progress in gene editing and immunosuppressive regimens has made xenotransplantation with porcine organs a possibility. However, evidence in pig-to-human xenotransplantation remains scarce, and antibody-mediated rejection (AMR) is a major obstacle to clinical applications of xenotransplantation. METHODS: We conducted a kidney xenotransplantation in a brain-dead human recipient using a porcine kidney with five gene edits (5GE) on March 25, 2024 at Xijing Hospital, China. Clinical-grade immunosuppressive regimens were employed, and the observation period lasted 22 days. We collected and analyzed the xenograft function, ultrasound findings, sequential protocol biopsies, and immune surveillance of the recipient during the observation. RESULTS: The combination of 5GE in the porcine kidney and clinical-grade immunosuppressive regimens prevented hyperacute rejection. The xenograft kidney underwent delayed graft function in the first week, but urine output increased later and the single xenograft kidney maintained electrolyte and pH homeostasis from postoperative day (POD) 12 to 19. We observed AMR at 24 h post-transplantation, due to the presence of pre-existing anti-porcine antibodies and cytotoxicity before transplantation; this AMR persisted throughout the observation period. Plasma exchange and intravenous immunoglobulin treatment mitigated the AMR. We observed activation of latent porcine cytomegalovirus toward the end of the study, which might have contributed to coagulation disorder in the recipient. CONCLUSIONS 5GE and clinical-grade immunosuppressive regimens were sufficient to prevent hyperacute rejection during pig-to-human kidney xenotransplantation. Pre-existing anti-porcine antibodies predisposed the xenograft to AMR. Plasma exchange and intravenous immunoglobulin were safe and effective in the treatment of AMR after kidney xenotransplantation.
Transplantation, Heterologous/methods* ; Kidney Transplantation/methods* ; Heterografts/pathology* ; Immunoglobulins, Intravenous/administration & dosage* ; Graft Survival/immunology* ; Humans ; Animals ; Sus scrofa ; Graft Rejection/prevention & control* ; Kidney/pathology* ; Gene Editing ; Species Specificity ; Immunosuppression Therapy/methods* ; Plasma Exchange ; Brain Death ; Biopsy ; Male ; Aged

Early-onset colorectal cancer (EOCRC) shows a different epidemiological trend compared to later-onset colorectal cancer, with its incidence rising in most regions and countries worldwide. However, the reasons behind this trend remain unclear. The etiology of EOCRC is complex and could involve both genetic and environmental factors. Apart from Lynch syndrome and Familial Adenomatous Polyposis, sporadic EOCRC exhibits a broad spectrum of pathogenic germline mutations, genetic polymorphisms, methylation changes, and chromosomal instability. Early-life exposures and environmental risk factors, including lifestyle and dietary risk factors, have been found to be associated with EOCRC risk. Meanwhile, specific chronic diseases, such as inflammatory bowel disease, diabetes, and metabolic syndrome, have been associated with EOCRC. Interactions between genetic and environmental risk factors in EOCRC have also been explored. Here we present findings from a narrative review of epidemiological studies on the assessment of early-life exposures, of EOCRC-specific environmental factors, and their interactions with susceptible loci. We also present results from EOCRC-specific genome-wide association studies that could be used to perform Mendelian randomization analyses to ascertain potential causal links between environmental factors and EOCRC.
Humans ; Colorectal Neoplasms/etiology* ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease/genetics*

Objective To achieve a preliminary understanding of the current situation of clinical laboratories in Guangdong Province,and discuss how to establish a sound investigation system,and utilize quality indicators to improve laboratory quality through the inves-tigation and analysis of data from 16 clinical laboratory quality indicators issued by the National Center for Clinical Laboratories.Meth-ods The questionnaire was issued by Clinet-EQA system and the basic information and quality indicator information during 2023 were collected.SPSS 20.0 software was used for statistical analysis according to different specialty categories and hospital grades.The 13 quality indicators measured in rate-based units were evaluated by sigma measurement.The P75,P50 and P25 percentiles of the overall distribution of each quality index were used to explore the optimal,appropriate and minimum quality specifications.Results A total of 577 laboratories participated in this survey.In addition to the implementation rate of internal quality assessment and the inter-laboratory comparison rate,the median sigma(σ)value of 11/13 quality indicators was greater than 3σ,and some of them even reach the level of 6σ,and there were disparities between hospital laboratories at different grades.The turnaround time(TAT)of the whole process of emergency examination was significantly less than those of inpatient and outpatient,TAT before emergency examination was controlled within 20 min,TAT before outpatient examination was within 30 min,and TAT before inpatient examination was within 42 min.The optimal quality specifications of 8 out of 13 indicators reached 6σ level,while the minimum quality specifications of 2 out of 13 indica-tors were lower than 3σ level.Conclusion In Guangdong Province,the overall level of quality indicators in the post-analytical of clin-ical laboratories was superior to that in the pre-analytical and analytical process.It should be essential to continuously monitor quality indicators and actively adopt improvement measures for those laboratories with unsatisfactory results,so as to enhance the examination quality of laboratories.

Objective To investigate the relationships between adolescents'body mass index(BMI),blood pressure(BP),and myopia and provide a scientific basis for the formulation of myopia prevention and control measures.Methods Adolescent students who par-ticipated in physical fitness surveillance in Chenghua District,Chengdu,from 2014 to 2023 were selected as the study population.A cross-sectional study was conducted using multiple logistic regression,adjusting for age,sex,BP,and pulse as covariates.A Cox model was employed to examine the relationship between obesity and myopia.Additionally,for the 2023 dataset,characterized by the largest sample size and most complete data,a mediation analysis was performed with mean BP as the mediator.Results Annual cross-sectional analysis from 2014 to 2023 revealed a significant positive association between obesity and myopia,with OR and 95％CI of 1.16(1.10-1.22),1.15(1.08-1.21),1.19(1.12-1.26),1.11(1.04-1.18),1.10(1.05-1.15),1.10(1.05-1.16),1.07(1.03-1.12),1.10(1.05-1.15),1.12(1.07-1.17),and 1.11(1.06-1.15),respectively.Similarly,cohort analysis showed a significant positive association between obesity and myopia(HR=1.09,95％CI:1.01-1.19).Mediation analysis indicated that BP accounted for 21.2％of the total effect of obesity on myopia.Conclusion Obesity facilitates the onset of myopia,and weight reduction can help decrease the likelihood of latter.Furthermore,BP mediates the impact of obesity on myopia.Implementing BP-lowering interventions in obese populations may aid in the prevention of myopia.