Objective To establish an "human serum - porcine aortic endothelial cells (PAEC) - human platelets" in vitro model and explore the mechanism of xenotransplantation-related coagulation dysfunction mediated by immune complexes - platelet FcγRⅡa (CD32a) receptor. Methods Healthy human serum was co-incubated with PAEC to prepare the supernatant containing immune complexes, which was then used to stimulate healthy human platelets, or directly treated with the serum of xenogeneic liver transplant recipients. Flow cytometry was used to detect platelet activation markers CD62P and surface IgG binding levels, and the platelet adhesion function was evaluated by platelet-PAEC adhesion experiments. CD32a blocking antibody IV.3 and SYK blocker SKYIN 4 were used to clarify the signaling pathways. Results The supernatant from the co-incubation of healthy human serum and PAEC could significantly induce platelet activation and endothelial adhesion. The use of the serum from xenogeneic liver transplant recipients could also significantly induce platelet activation. Antibody IV.3 and SYK blocker SKYIN 4 could significantly inhibit these effects. Conclusions In xenotransplantation, the immune complexes formed by human serum antibodies and porcine endothelial antigens may induce abnormal platelet activation through the platelet CD32a receptor, which is an important mechanism of non-complement-dependent post-transplant coagulation dysfunction, providing a new target for the intervention of coagulation complications in xenotransplantation.

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.

Microplastics(particles<5 μm in diameter),generated through plastic processing or natural degradation,have emerged as novel environmental contaminants with increasing threats to ecosys-tems and human health.Recent studies have demonstrated the accumulation of microplastics in various environmental matrices,human tissues and even placentas,highlighting the urgent need to investigate their health effects.These particles enter the human system primarily through dietary intake and water consumption,yet critical knowledge gaps remain regarding their bioaccumulation potential,toxicological profiles,and synergistic interactions with co-existing environmental contaminants.The Drosophila mela-nogaster(fruit fly),a well-established model organism,offers distinct advantages for contaminant toxicity assessment,including a compact life cycle,small body size,rapid reproduction,ease of laboratory maintenance,and extensive availability of transgenic strains.In particular,its genome has a high degree of homology with the human genome,which further enhances its scientific relevance for toxicological studies.In this review,we summarize the advantages of Drosophila models in microplastic toxicity studies,with particular emphasis on recent advances in understanding intestinal toxicity,neuro toxicity,genotox-icity,reproductive toxicity,developmental toxicity,and transgenerational toxicity.We point to critical research gaps that require urgent investigation,including the bioaccumulation dynamics of microplas-tics,metabolic transformation and elimination pathways,multi-pollutant interaction effects,molecular mechanisms of toxicity,and comprehensive health risk assessment frameworks.The review aims to provide data for elucidating the mechanisms of microplastic toxicity and formulating evidence-based prevention strategies.

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

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.

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.

Peritoneal metastasis of colorectal cancer is the second most common metastatic pattern after liver metastasis, clinically common and associated with a poor prognosis. Refractory subtypes such as mucinous adenocarcinoma, signet-ring cell carcinoma, and BRAF V600E-mutated colorectal cancers account for a relatively high proportion in peritoneal metastasis. While previous diagnosis and treatment faced significant challenges, recent advances in new technologies and evolving therapeutic concepts have achieved progress in management. Many patients with colorectal cancer peritoneal metastasis have obtained favorable treatment outcomes, though numerous challenges persist. This article provides an in-depth analysis of current status and advancements in the diagnosis and treatment of colorectal cancer peritoneal metastasis, examines existing clinical difficulties and unresolved issues, and explores the application of advanced technologies in clinical practice. Through promoting individualized, precise, and standardized treatment concepts, we aim to enhance survival benefits for more patients.

Microplastics(particles<5 μm in diameter),generated through plastic processing or natural degradation,have emerged as novel environmental contaminants with increasing threats to ecosys-tems and human health.Recent studies have demonstrated the accumulation of microplastics in various environmental matrices,human tissues and even placentas,highlighting the urgent need to investigate their health effects.These particles enter the human system primarily through dietary intake and water consumption,yet critical knowledge gaps remain regarding their bioaccumulation potential,toxicological profiles,and synergistic interactions with co-existing environmental contaminants.The Drosophila mela-nogaster(fruit fly),a well-established model organism,offers distinct advantages for contaminant toxicity assessment,including a compact life cycle,small body size,rapid reproduction,ease of laboratory maintenance,and extensive availability of transgenic strains.In particular,its genome has a high degree of homology with the human genome,which further enhances its scientific relevance for toxicological studies.In this review,we summarize the advantages of Drosophila models in microplastic toxicity studies,with particular emphasis on recent advances in understanding intestinal toxicity,neuro toxicity,genotox-icity,reproductive toxicity,developmental toxicity,and transgenerational toxicity.We point to critical research gaps that require urgent investigation,including the bioaccumulation dynamics of microplas-tics,metabolic transformation and elimination pathways,multi-pollutant interaction effects,molecular mechanisms of toxicity,and comprehensive health risk assessment frameworks.The review aims to provide data for elucidating the mechanisms of microplastic toxicity and formulating evidence-based prevention strategies.

Peritoneal metastasis of colorectal cancer is the second most common metastatic pattern after liver metastasis, clinically common and associated with a poor prognosis. Refractory subtypes such as mucinous adenocarcinoma, signet-ring cell carcinoma, and BRAF V600E-mutated colorectal cancers account for a relatively high proportion in peritoneal metastasis. While previous diagnosis and treatment faced significant challenges, recent advances in new technologies and evolving therapeutic concepts have achieved progress in management. Many patients with colorectal cancer peritoneal metastasis have obtained favorable treatment outcomes, though numerous challenges persist. This article provides an in-depth analysis of current status and advancements in the diagnosis and treatment of colorectal cancer peritoneal metastasis, examines existing clinical difficulties and unresolved issues, and explores the application of advanced technologies in clinical practice. Through promoting individualized, precise, and standardized treatment concepts, we aim to enhance survival benefits for more patients.