Objective To systematically analyze the literature characteristics of Journal of Organ Transplantation since its inception. Methods Using the China National Knowledge Infrastructure (CNKI) academic journal full-text database as the data source, all articles published in the Journal of Organ Transplantation from January 2010 to August 2025 were retrieved. After excluding non-academic papers, a total of 1 568 research papers were included. R language 4.3.0, Bibliometrix package 3.2.1, and Citespace software were used to analyze the number of publications, publishing institutions, authors, keywords and other aspects. Results The number of publications in Journal of Organ Transplantation increased from an average of 82 articles per year in the early years after its inception to 113 articles per year in recent years, a growth of 37.8%. The geographical distribution of publishing institutions covers 32 provinces, cities and autonomous regions nationwide, mainly concentrated in the South China, East China and North China regions, and has now basically covered the central and western regions in recent years. The author collaboration network includes 45 authors distributed across 7 major collaboration clusters, forming a stable multi-level national research system centered on key university-affiliated hospitals. The high-frequency keywords are dominated by "liver transplantation" (425 times) and "kidney transplantation" (396 times). The theme evolution shows a clear three-stage characteristic: initially focusing on clinical technology application, deepening to immune mechanism exploration in the middle stage, and recently (since 2022) focusing on cutting-edge research areas such as xenotransplantation. Conclusions Journal of Organ Transplantation has witnessed the rapid development of China's organ transplantation cause, fully reflecting the research status and trends in China's organ transplantation field, and has provided an important platform for the future development and international cooperation in China's organ transplantation field.

ObjectiveTo observe the effects of Dihuang Yinzi （DY） on motor dysfunction in rats with advanced Parkinson's disease （PD） and to investigate the mechanisms by which DY improves advanced PD symptoms through the "gut microbiota-short-chain fatty acids （SCFAs）-inflammation-neuroprotection pathway". MethodsAn advanced PD rat model was induced by rotenone. Rats were divided into a normal group， model group， positive drug group （levodopa， 50 mg·kg^-1）， and DY low-， medium-， and high-dose groups （5.2， 10.4， 20.8 g·kg^-1）. After 7 days of administration， motor function was evaluated using the open-field， pole-climbing， and inclined plate tests. Hematoxylin-eosin （HE） staining was used to observe pathological changes in the substantia nigra and colon， and immunohistochemistry was performed to detect α-Synuclein （α-Syn） and tyrosine hydroxylase （TH） expression in the substantia nigra. Enzyme-linked immunosorbent assay （ELISA） was used to measure levels of dopamine （DA）， 5-hydroxytryptamine （5-HT）， 3，4-dihydroxyphenylacetic acid （DOPAC）， Levodopa， homovanillic acid （HVA）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）. Western blot analysis was used to detect the expression of zonula occludens-1 （ZO-1） and occludin. Gut microbiota diversity was analyzed by 16S rRNA sequencing， and gas chromatography （GC） was used to determine the content of SCFAs in colonic contents. ResultsCompared with the normal group， the model group showed significantly decreased movement speed and distance in the open-field test， prolonged pole-climbing time， and reduced retention angle on the inclined plate （P<0.01）， accompanied by increased α-Syn expression （P<0.01） and decreased TH expression （P<0.01） in the brain. Compared with the model group， all DY dose groups improved motor dysfunction in advanced PD rats to varying degrees （P<0.05， P<0.01） and alleviated pathological damage in the brain and colon. High-dose DY significantly reduced α-Syn aggregation in the substantia nigra （P<0.01） and increased TH expression （P<0.01）. ELISA and Western blot results showed that， compared with the normal group， the model group exhibited decreased levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum （P<0.01）， increased levels of TNF-α， IL-6， and IL-1β in the colon and striatum （P<0.01）， and significantly reduced expression of ZO-1 （P<0.05） and occludin in the colon （P<0.01）. Compared with the model group， all DY dose groups increased the levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum to varying degrees （P<0.05， P<0.01）. In the high-dose DY group， the levels of TNF-α， IL-6， and IL-1β in the colon and striatum were reduced （P<0.01）， while the expression of ZO-1 （P<0.05） and occludin in the intestine was increased. The 16S rRNA sequencing results indicated that the relative abundances of Actinobacteriota， Enterobacteriaceae， and Erysipelotrichaceae were increased in the model group， whereas the relative abundances of Bacteroidota， class Clostridia， Lachnospiraceae， and Akkermansia muciniphila were decreased. These changes were effectively reversed after high-dose DY intervention. GC analysis showed that the content of SCFAs in the colonic contents of rats in the model group was decreased （P<0.05， P<0.01）， while after high-dose DY intervention， the levels of acetate， propionate， isobutyrate， and butyrate were significantly increased （P<0.05， P<0.01）. ConclusionDY may exert therapeutic effects in advanced PD by regulating the gut microbiota-SCFAs-inflammation pathway.

BACKGROUND:How to effectively promote bone regeneration and bone reconstruction after bone injury has always been a key issue in clinical bone repair research.The use of biological and degradable materials loaded with bioactive factors to treat bone defects has excellent application prospects in bone repair. OBJECTIVE:To investigate the effect of polylactic acid-glycolic acid copolymer(PLGA)composite scaffold modified by lysine-grafted graphene oxide nanoparticles(LGA-g-GO)on osteogenic differentiation and new bone formation. METHODS:PLGA was dissolved in dichloromethane and PLGA scaffold was prepared by solvent evaporation method.PLGA/GO composite scaffolds were prepared by dispersing graphene oxide uniformly in PLGA solution.LGA-g-GO nanoparticles were prepared by chemical grafting method,and the PLGA/LGA-g-GO composite scaffolds were constructed by blending LGA-g-GO nanoparticles at different mass ratios(1%,2%,and 3%)with PLGA.The micromorphology,hydrophilicity,and protein adsorption capacity of scaffolds of five groups were characterized.MC3T3 cells were inoculated on the surface of scaffolds of five groups to detect cell proliferation and osteogenic differentiation. RESULTS AND CONCLUSION:(1)The surface of PLGA scaffolds was smooth and flat under scanning electron microscope,while the surface of the other four scaffolds was rough.The surface roughness of the composite scaffolds increased with the increase of the addition of LGA-g-GO nanoparticles.The water contact angle of PLGA/LGA-g-GO(3%)composite scaffolds was lower than that of the other four groups(P<0.05).The protein adsorption capacity of PLGA/LGA-g-GO(1%,2%,and 3%)composite scaffolds was stronger than PLGA and PLGA/GO scaffolds(P<0.05).(2)CCK-8 assay showed that PLGA/LGA-g-GO(2%,3%)composite scaffold could promote the proliferation of MC3T3 cells.Alkaline phosphatase staining and alizarin red staining showed that the cell alkaline phosphatase activity in PLGA/LGA-g-GO(2%,3%)group was higher than that in the other three groups(P<0.05).The calcium deposition in the PLGA/GO and PLGA/LGA-g-GO(1%,2%,and 3%)groups was higher than that in the PLGA group(P<0.05).(3)In summary,PLGA/LGA-g-GO composite scaffold can promote the proliferation and osteogenic differentiation of osteoblasts,and is conducive to bone regeneration and bone reconstruction after bone injury.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

A 71-year-old male presented with esophageal cancer and severe aortic valve regurgitation. Treatment strategies for such patients are controversial. Considering the risks of cardiopulmonary bypass and potential esophageal cancer metastasis, we successfully performed transcatheter aortic valve implantation and minimally invasive three-incision thoracolaparoscopy combined with radical resection of esophageal cancer (McKeown) simultaneously in the elderly patient who did not require neoadjuvant treatment. This dual minimally invasive procedure took 6 hours and the patient recovered smoothly without any surgical complications.

To systematically review randomized controlled trials （RCTs） of traditional Chinese medicine （TCM） intervention in ulcerative colitis （UC）， and analyze the characteristics of these studies and their outcome indicators， thereby providing references for the design of future RCTs of TCM intervention in UC and offering evidence supporting the clinical application of TCM in UC. A computerized search was conducted in the China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， SinoMed， PubMed， Cochrane Library， EMbase， and Web of Science databases for RCTs of TCM intervention in UC published from January 2021 to August 2024. The risk of bias was assessed， and outcome indicators were qualitatively analyzed. A total of 555 RCTs were included， with a sample size of 44 853 participants. The largest sample size was 218 cases， and the smallest was 28 cases， with most studies focusing on 60-100 participants. Of the 386 RCTs that explicitly reported TCM syndrome types， the top three were large intestine dampness-heat syndrome （31.05%）， spleen and kidney yang deficiency syndrome （12.47%）， and spleen deficiency with dampness syndrome （9.17%）. The interventions， ranked by frequency of use， included internal Chinese medicine compounds/preparations （64.5%）， Chinese medicine compounds/preparations with retained enema （18.2%）， internal Chinese medicine compounds/preparations + external TCM treatment （5.95%）， and external TCM treatment alone （4.86%）. The treatment duration was mainly 4-8 weeks （64.86%）， with 61 studies （10.99%） reporting follow-up time. A total of 157 outcome indicators were used， with a frequency of 3 460 occurrences， classified into six domains： TCM syndromes and symptoms （346 occurrences， 10%）， symptoms/signs （541 occurrences， 15.64%）， physical and chemical examinations （2 119 occurrences， 61.24%）， quality of life （107 occurrences， 3.09%）， long-term prognosis （61 occurrences， 1.76%）， and safety events （284 occurrences， 8.21%）. The analysis reveals several limitations in the outcome indicators of TCM intervention in UC， including the lack of a basis for sample size calculation， non-standardized TCM syndrome classification， absence of trial design and registration， inadequate blinding and allocation concealment， adherence issues with interventions， imbalanced selection of surrogate and endpoint indicators， inconsistency in the timing of outcome measurements， design issues that require standardization， and ethical and safety concerns. It is recommended that future studies actively construct a set of core indicators for UC that include standardized TCM syndrome classification， clear efficacy evaluation indicators， key endpoint indicators， and reasonable measurement time points. Long-term prognostic impacts， comprehensive assessments of patients' quality of life， and consideration of economic benefits should be emphasized， providing a basis for the clinical practice of TCM in the treatment of UC.

To systematically review randomized controlled trials （RCTs） of traditional Chinese medicine （TCM） intervention in ulcerative colitis （UC）， and analyze the characteristics of these studies and their outcome indicators， thereby providing references for the design of future RCTs of TCM intervention in UC and offering evidence supporting the clinical application of TCM in UC. A computerized search was conducted in the China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， SinoMed， PubMed， Cochrane Library， EMbase， and Web of Science databases for RCTs of TCM intervention in UC published from January 2021 to August 2024. The risk of bias was assessed， and outcome indicators were qualitatively analyzed. A total of 555 RCTs were included， with a sample size of 44 853 participants. The largest sample size was 218 cases， and the smallest was 28 cases， with most studies focusing on 60-100 participants. Of the 386 RCTs that explicitly reported TCM syndrome types， the top three were large intestine dampness-heat syndrome （31.05%）， spleen and kidney yang deficiency syndrome （12.47%）， and spleen deficiency with dampness syndrome （9.17%）. The interventions， ranked by frequency of use， included internal Chinese medicine compounds/preparations （64.5%）， Chinese medicine compounds/preparations with retained enema （18.2%）， internal Chinese medicine compounds/preparations + external TCM treatment （5.95%）， and external TCM treatment alone （4.86%）. The treatment duration was mainly 4-8 weeks （64.86%）， with 61 studies （10.99%） reporting follow-up time. A total of 157 outcome indicators were used， with a frequency of 3 460 occurrences， classified into six domains： TCM syndromes and symptoms （346 occurrences， 10%）， symptoms/signs （541 occurrences， 15.64%）， physical and chemical examinations （2 119 occurrences， 61.24%）， quality of life （107 occurrences， 3.09%）， long-term prognosis （61 occurrences， 1.76%）， and safety events （284 occurrences， 8.21%）. The analysis reveals several limitations in the outcome indicators of TCM intervention in UC， including the lack of a basis for sample size calculation， non-standardized TCM syndrome classification， absence of trial design and registration， inadequate blinding and allocation concealment， adherence issues with interventions， imbalanced selection of surrogate and endpoint indicators， inconsistency in the timing of outcome measurements， design issues that require standardization， and ethical and safety concerns. It is recommended that future studies actively construct a set of core indicators for UC that include standardized TCM syndrome classification， clear efficacy evaluation indicators， key endpoint indicators， and reasonable measurement time points. Long-term prognostic impacts， comprehensive assessments of patients' quality of life， and consideration of economic benefits should be emphasized， providing a basis for the clinical practice of TCM in the treatment of UC.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.