Organ transplantation is regarded as the pinnacle of medical achievement in the 21st century, with innovation serving as the core engine driving the advancement of transplant surgery. Taking the decades of practical exploration in organ transplantation (especially liver transplantation) in China as its main thread, this article systematically analyzes the core logic of transplant surgical innovation. This logic adheres to a three-pronged developmental pathway characterized by being clinically problem-solving oriented, patient-need centered, and critical-technology-conquering driven. By reviewing major milestones such as living donor liver transplantation, auxiliary partial orthotopic liver transplantation, combined multi-organ transplantation, and xenotransplantation, the study elucidates how surgical innovation should be grounded in resolving clinical pain points, return to medical humanism, and leverage technological iteration to foster revolutionary progress. Furthermore, the article explores the empowering role of cutting-edge technologies-including artificial intelligence, machine perfusion, and gene editing-in driving the next surgical revolution. It aims to construct a theoretical framework and provide practical insights for the future development of transplant surgery in China.

Organ transplantation is regarded as the pinnacle of medical achievement in the 21st century, with innovation serving as the core engine driving the advancement of transplant surgery. Taking the decades of practical exploration in organ transplantation (especially liver transplantation) in China as its main thread, this article systematically analyzes the core logic of transplant surgical innovation. This logic adheres to a three-pronged developmental pathway characterized by being clinically problem-solving oriented, patient-need centered, and critical-technology-conquering driven. By reviewing major milestones such as living donor liver transplantation, auxiliary partial orthotopic liver transplantation, combined multi-organ transplantation, and xenotransplantation, the study elucidates how surgical innovation should be grounded in resolving clinical pain points, return to medical humanism, and leverage technological iteration to foster revolutionary progress. Furthermore, the article explores the empowering role of cutting-edge technologies-including artificial intelligence, machine perfusion, and gene editing-in driving the next surgical revolution. It aims to construct a theoretical framework and provide practical insights for the future development of transplant surgery in China.

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.

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

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.

Objective:To explore the differences in distribution of colorectal cancer-related risk factors between patients with early-onset colorectal cancer (EOCRC) and those with late-onset colorectal cancer (LOCRC) in a Chinese cohort, and to provide reference and guidance for the prevention, diagnosis, and treatment of EOCRC.Methods:Using data from the National Colorectal Cancer Cohort study cohort, 5377 patients with newly diagnosed colorectal cancer (CRC) attending the Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine from June 2018 to February 2023 were included in the study cohort. Questionnaires capturing epidemiological features, including lifestyle and dietary habits, were administered. The patients were divided into two groups, the cut-off age being 50 years. Those aged ≥50 years were defined as having LOCRC and those aged <50 years as having EOCRC. Wilcoxon (continuous variates) or χ 2 tests (categorical variates) were performed to compare differences in epidemiological features. Results:A total of 3799 people who had completed the questionnaire were included in this study, 491 of whom had EOCRC and 3308 LOCRC. The response rate to the questionnaire was 70.7%. The median ages of patients in the EOCRC and LOCRC groups were 43 and 66 years, respectively. There was a higher proportion of female patients (48.5% [253/491] vs. 35.8% [1184/3308], χ 2=28.8, P<0.001) in the EOCRC than the LOCRC group. Patients with EOCRC and lower body mass index (medium 22.1 kg/m 2 vs. 22.9 kg/m 2, W=744 793, P=0.005) and lower proportion of abdominal obesity (87.2% [428/491] vs. 93.8% [3103/3308], χ 2=38.3, P<0.001). Patients with EORC significantly less commonly reported a history of hypertension (5.9% [29/491] vs. 41.6% [1375/3308], χ 2=231.8, P<0.001), diabetes (1.4% [7/491] vs. 14.4% [476/3308], χ 2=63.6, P<0.001) and cardiovascular and cerebrovascular diseases (0.8% [4/491] vs. 7.3% [241/3308], χ 2=28.6, P<0.001). However, the proportion of patients with a family history of CRC was significantly higher ( P<0.05) in the EOCRC group (10.2% [50/491] vs. 6.9% [227/3 308], χ 2=6.5, P=0.010]. In terms of lifestyle, patients with EOCRC had shorter sleep duration (median: 8.0 hours vs. 8.5 hours, W=578 989, P<0.001), and were less likely to participate in physical exercise (29.5% [145/491] vs. 38.7% [1281/3308] χ 2=15.0, P<0.001) or engage in physical work (65.2% [320/491] vs. 74.1% [2450/3308], χ 2=16.7, P<0.001). Meanwhile, in the EOCRC group a lower percentage of patients were smokers (29.3% [144/491] vs. 42.7% [1411/3308], χ 2=46.9, P<0.001) and they smoked less (median 17.6 pack/year vs. 30.0 pack/year,W=55 850, P<0.001). Fewer patients in the EOCRC group habitually drank alcohol (21.0% [103/491] vs. 38.0% [1257/3308], χ 2=57.5, P<0.001) or tea (17.5% [86/491] vs. 28.7% [948/3308], χ 2=26.2, P<0.001) than in the LOCRC group. Compared with the LOCRC group, patients with EOCRC had a higher frequency of intake of fresh meat, fresh fruit, eggs, and dairy products and a lower frequency of intake of preserved meat and pickled vegetables; these differences are statistically significant (all P<0.05). There was no statistically significant difference in consumption of fresh vegetables or a high-sugar diet between the two groups (both P>0.05). Conclusions:This study highlights disparities in adverse lifestyle and dietary habits between patients in China with EOCRC versus LOCRC.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,and the results highly rely on the personal knowledge and experience.The goal of this work was to establish a software-aided approach,by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS)and in-house high-definition MS2 library,to enhance the identification of prototypes and metabolites of the compound formulae in vivo,taking Sishen formula(SSF)as a template.Seven different MS2 acquisition methods were compared,which demonstrated the potency of a hybrid scan approach(namely high-definition data-independent/data-dependent acquisition(HDDIDDA))in the identification precision,MS1 coverage,and MS2 spectra quality.The HDDIDDA data for 55 reference compounds,four component drugs,and SSF,together with the rat bio-samples(e.g.,plasma,urine,feces,liver,and kidney),were acquired.Based on the UNIFI? platform(Waters),the efficient data processing workflows were estab-lished by combining mass defect filtering(MDF)-induced classification,diagnostic product ions(DPIs),and neutral loss filtering(NLF)-dominated structural confirmation.The high-definition MS2 spectral li-braries,dubbed in vitro-SSF and in vivo-SSF,were elaborated,enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples.Consequently,118 prototypes and 206 metabolites of SSF were identified,with the identification rate reaching 80.51％and 79.61％,respectively.The metabolic pathways mainly involved the oxidation,reduction,hydrolysis,sulfation,methylation,demethylation,acetylation,glucuronidation,and the combined reactions.Conclusively,the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Objective:To explore the differences in distribution of colorectal cancer-related risk factors between patients with early-onset colorectal cancer (EOCRC) and those with late-onset colorectal cancer (LOCRC) in a Chinese cohort, and to provide reference and guidance for the prevention, diagnosis, and treatment of EOCRC.Methods:Using data from the National Colorectal Cancer Cohort study cohort, 5377 patients with newly diagnosed colorectal cancer (CRC) attending the Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine from June 2018 to February 2023 were included in the study cohort. Questionnaires capturing epidemiological features, including lifestyle and dietary habits, were administered. The patients were divided into two groups, the cut-off age being 50 years. Those aged ≥50 years were defined as having LOCRC and those aged <50 years as having EOCRC. Wilcoxon (continuous variates) or χ 2 tests (categorical variates) were performed to compare differences in epidemiological features. Results:A total of 3799 people who had completed the questionnaire were included in this study, 491 of whom had EOCRC and 3308 LOCRC. The response rate to the questionnaire was 70.7%. The median ages of patients in the EOCRC and LOCRC groups were 43 and 66 years, respectively. There was a higher proportion of female patients (48.5% [253/491] vs. 35.8% [1184/3308], χ 2=28.8, P<0.001) in the EOCRC than the LOCRC group. Patients with EOCRC and lower body mass index (medium 22.1 kg/m 2 vs. 22.9 kg/m 2, W=744 793, P=0.005) and lower proportion of abdominal obesity (87.2% [428/491] vs. 93.8% [3103/3308], χ 2=38.3, P<0.001). Patients with EORC significantly less commonly reported a history of hypertension (5.9% [29/491] vs. 41.6% [1375/3308], χ 2=231.8, P<0.001), diabetes (1.4% [7/491] vs. 14.4% [476/3308], χ 2=63.6, P<0.001) and cardiovascular and cerebrovascular diseases (0.8% [4/491] vs. 7.3% [241/3308], χ 2=28.6, P<0.001). However, the proportion of patients with a family history of CRC was significantly higher ( P<0.05) in the EOCRC group (10.2% [50/491] vs. 6.9% [227/3 308], χ 2=6.5, P=0.010]. In terms of lifestyle, patients with EOCRC had shorter sleep duration (median: 8.0 hours vs. 8.5 hours, W=578 989, P<0.001), and were less likely to participate in physical exercise (29.5% [145/491] vs. 38.7% [1281/3308] χ 2=15.0, P<0.001) or engage in physical work (65.2% [320/491] vs. 74.1% [2450/3308], χ 2=16.7, P<0.001). Meanwhile, in the EOCRC group a lower percentage of patients were smokers (29.3% [144/491] vs. 42.7% [1411/3308], χ 2=46.9, P<0.001) and they smoked less (median 17.6 pack/year vs. 30.0 pack/year,W=55 850, P<0.001). Fewer patients in the EOCRC group habitually drank alcohol (21.0% [103/491] vs. 38.0% [1257/3308], χ 2=57.5, P<0.001) or tea (17.5% [86/491] vs. 28.7% [948/3308], χ 2=26.2, P<0.001) than in the LOCRC group. Compared with the LOCRC group, patients with EOCRC had a higher frequency of intake of fresh meat, fresh fruit, eggs, and dairy products and a lower frequency of intake of preserved meat and pickled vegetables; these differences are statistically significant (all P<0.05). There was no statistically significant difference in consumption of fresh vegetables or a high-sugar diet between the two groups (both P>0.05). Conclusions:This study highlights disparities in adverse lifestyle and dietary habits between patients in China with EOCRC versus LOCRC.