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.

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.

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.

Tissue-resident memory T cells （T_RM cells） are a subset of memory T cells that reside in tissues， exhibit tissue specificity， and do not recirculate. When potential hazards threaten the liver， such as pathogen invasion （bacteria， viruses， etc.） and excessive autoimmune responses， T_RM cells are essential as the first line of immune defense， playing an important role in viral hepatitis， autoimmune liver disease， metabolic dysfunction-associated fatty liver disease， liver cirrhosis， and liver transplantation. Here， we present the immunophenotypes of T_RM cells in the liver and their surface markers and transcriptional profiles， aiming to clarify the role of T_RM cells in chronic liver diseases and explore their potential function as therapeutic targets in immunotherapy.

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.