The application of immunosuppressants has significantly reduced the incidence rate of rejection reaction after liver transplantation， but the clinical efficacy of immunosuppressants is greatly affected by individual differences between patients. This article systematically reviews the recent research advances in the interaction between immunosuppressants and gut microbiota， with a focus on the regulatory role and mechanism of intestinal microflora communities on the efficacy of immunosuppressants. Studies have shown that intestinal microbiome is one of the key factors influencing the efficacy of immunosuppressive therapy after liver transplantation. This review aims to provide a theoretical basis for in-depth research in this field and provide new insights for developing individualized immunosuppressive treatment regimens based on the regulation of intestinal microflora.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To construct prostate cancer(PCa)prediction models based on machine learning algorithms, so as to improve the accuracy of early diagnosis of PCa. Methods A retrospective analysis was performed on the clinical data of 504 patients who underwent prostate biopsy at our hospital during Jan. 2020 and Nov. 2024. Patients' age, body mass index(BMI), history of hypertension, diabetes and smoking, total prostate-specific antigen(tPSA), free prostate-specific antigen(fPSA), f/tPSA, prostate volume(PV), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio(NLR), Prostate Imaging Reporting and Data System(PI-RADS)score, digital rectal examination(DRE)results, and pathological findings were collected. The patients were divided into the training and testing sets at a ratio of 7:3. Ten early diagnosis prediction models of PCa were constructed using 10 supervised machine learning algorithms. Model performance was evaluated and validated using metrics including area under the receiver operating characteristic curve(AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis(DCA). SHAP analysis was used to interpret the models, and to clarify the importance of each feature and the basis for model decisions. Results All models showed good predictive value, with the gradient boosting machine(GBM)model performing the best(AUC=0.905, accuracy=84.1%, sensitivity=90.2%, specificity=80.0%). Calibration curves indicated good calibration and fitting of the GBM model, while DCA demonstrated favorable clinical net benefits. SHAP analysis identified the most significant features affecting PCa occurrence in descending order:tPSA, f/tPSA, PIRADS score, PV, age, and DRE. Conclusion The GBM model exhibits the optimal performance among the 10 models. The importance of features for predicting the occurrence of PCa, from the highest to the lowest, is as follows:tPSA, f/tPSA, PIRADS score, PV, age, and DRE.

Objective:To investigate the current status and influencing factors of ICU nurses' perception of risks and benefits of family presence during resuscitation, and to provide scientific evidence for the formulation of relevant policies in medical institutions.Methods:A convenience sampling method was used to recruit 370 ICU nurses from four Class Ⅲ Grade A hospitals in Taizhou between October and December 2024. A general information questionnaire, the Chinese version of the Family Presence Risk-Benefit Scale (FPR-BS), and the Chinese version of the Family Presence Self-Confidence Scale (FPS-CS) were used. Hierarchical multiple linear regression was applied to explore the influencing factors of ICU nurses' perception of risks and benefits of family presence during resuscitation.Results:A total of 370 questionnaires were distributed, and 357 valid questionnaires were collected, with a valid response rate of 96.49%. The total score of the Chinese version of FPR-BS among 357 ICU nurses was (50.25±10.54), and the total score of the Chinese version of FPS-CS was (40.36±8.19). Hierarchical multiple linear regression analysis showed that gender, age, years of work experience, educational level, number of times participating in resuscitation, and self-confidence regarding family presence during resuscitation were influencing factors of risk-benefit perception ( P<0.05) . Conclusions:ICU nurses' perception of the risks and benefits of family presence during resuscitation was at a relatively low to moderate level. Self-confidence regarding family presence during resuscitation was correlated with risk-benefit perception. When implementing policies on family presence during resuscitation, medical institutions should first improve nurses' self-confidence in this regard and provide targeted support.

Objective To investigate the spectrum-efficacy relationship between the fingerprint of green tea extract and its pharmacological effects in intervention of neovascular age-related macular degeneration(nAMD),explore the phar-macodynamic material basis,and identify key active components.Methods The fingerprint of green tea extract was es-tablished using UPLC-Q-Exactive Orbitrap MS.Hierarchical cluster analysis and orthogonal partial least squares discrimi-nant analysis were used to explore differences in chemical composition among green tea samples from different origins.A zebrafish nAMD model induced by cobalt chloride was established to evaluate the pharmacological activity of green tea ex-tract in nAMD intervention.Partial least squares regression analysis(PLSR)and grey relational analysis(GRA)were em-ployed to study the spectrum-efficacy correlation,comprehensively analyze the pharmacodynamic material basis of green tea in AMD intervention,and screen for key active components.Results A fingerprint detection method for green tea ex-tract was successfully established,and 31 chemical components were qualitatively and quantitatively characterized.Hierar-chical cluster analysis and orthogonal partial least squares discriminant analysis classified 50 batches of green tea samples into two major categories,identifying 15 key differential components.Pharmacological experiments demonstrated that green tea extract inhibited abnormal vascular growth in the zebrafish retina caused by cobalt chloride.Through PLSR and GRA,six components of green tea extract were found to be significantly correlated with pharmacological activity and showed high variable importance in projection:quinic acid,epicatechin,gallocatechin,epigallocatechin,gallocatechin gal-late,and epigallocatechin gallate.Conclusion This study comprehensively characterized 31 chemical components in green tea.Based on the nAMD zebrafish model and spectrum-effect correlation theory,PLSR and GRA analyses were ap-plied to accurately identify quinic acid,epicatechin,gallocatechin,epigallocatechin,gallocatechin gallate,and epigallocate-chin gallate as the key effective substances in green tea for intervening in nAMD.This provides a foundation for the devel-opment of new drugs with therapeutic potential against nAMD,and offers data support for establishing a comprehensive quality evaluation system for green tea.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Artificial intelligence(AI)is reconstructing drug quality control systems with unprecedented depth.With the rapid development of the traditional Chinese medicine(TCM)industry,the quality regulation of TCM has garnered increasing attention.The efficacy and safety of TCM directly impact public health,making it imperative to enhance the scientific rigor and effectiveness of its quality control.In recent years,the application of AI technology has expanded across various fields,and its potential in TCM quality regulation has gradually been recognized.Cur-rently,AI has demonstrated advantages in multiple stages of TCM production,including cultivation and planting,authenticity identification of decoction pieces,compliance review of manufacturing processes,market circulation monitoring,and risk assessment.However,despite existing research achievements,challenges such as limited technological adoption,low standardization,and insufficient data sharing persist.This paper provides an in-depth review of innovative AI applications in TCM quality control,comprehensively analyzes their effectiveness and future prospects,and proposes a novel"AI-Digital Sensory-Blockchain"tri-synergistic quality control paradigm based on domestic and international research advancements.Furthermore,it explores potential future research directions to advance the modernization of TCM quality regulation,thereby achieving higher levels of safety assurance and scien-tifically robust supervision.

Objective:To explore the effect of self-made static progressive braces in the stepwise treatment of hand flexion dysfunction caused by scar contracture after burn injury.Methods:This study was a retrospective observational study. From January 2022 to March 2024, the First Affiliated Hospital of Air Force Medical University admitted twenty-nine patients who met the inclusion criteria and used the self-made static progressive braces for stepwise treatment of hand flexion dysfunction caused by scar contracture, including 23 males and 6 females, aged 16 to 55 years. The depth of burn on the back of the hand was deep partial-thickness to full-thickness. The self-made static progressive brace referred to the self-made static progressive joint distractor and the self-made static progressive hand flexion distractor. The stepwise treatment was adopted. In the first stage, in-hospital treatment for 2 to 4 weeks was selected. The treatment contents included conventional rehabilitation training and training on the application of the self-made joint distractors. When the active flexion range of motion of the metacarpophalangeal joint was ≥60°, the second stage of treatment was carried out, that is, two weeks of home remote rehabilitation treatment. The treatment contents included individualized exercise training such as training with self-made static progressive joint distractors and training with self-made static progressive hand flexion distractors. Adverse events such as blister, brace compression, and tissue strain during the stepwise treatment were recorded. After the end of the stepwise treatment, the active flexion of the affected hand was observed. During follow-up, the total active range of motion of the affected hand was measured, and the extensibility of the scar and skin and soft tissue on the back of the affected hand and the patient's satisfaction with the therapeutic effect were evaluated. At the last follow-up, the function of the affected hand was evaluated according to the trial standards for evaluation of partial function of upper extremity by the Hand Surgery Society of Chinese Medical Association.Results:During the stepwise treatment, a small amount of blisters developed on the skin at the wrist joint in 2 patients, which was cured after symptomatic treatment; the other patients had no adverse events such as brace compression and tissue strain. After the end of the stepwise treatment, the active flexion function of the hand was better, and the fist could be basically clenched. After 1-3 months of follow-up, the total active range of motion of the affected hand was 200-245°; the scar on the back of the hand was soft and light-colored, and the skin and soft tissue was malleable. Twenty-two patients were very satisfied with the curative effect, 6 patients were satisfied, and 1 patient was dissatisfied. At the last follow-up, there were 20 cases with excellent hand function and 9 cases with good hand function.Conclusions:The stepwise treatment of hand flexion dysfunction caused by scar contracture after burn injury with self-made static progressive braces has a good effect and few complications. Moreover, the two self-made static progressive distractors are convenient in material acquisition, simple to make, cost-effective, and highly practical, which is worthy of clinical promotion.

Artificial intelligence(AI)is reconstructing drug quality control systems with unprecedented depth.With the rapid development of the traditional Chinese medicine(TCM)industry,the quality regulation of TCM has garnered increasing attention.The efficacy and safety of TCM directly impact public health,making it imperative to enhance the scientific rigor and effectiveness of its quality control.In recent years,the application of AI technology has expanded across various fields,and its potential in TCM quality regulation has gradually been recognized.Cur-rently,AI has demonstrated advantages in multiple stages of TCM production,including cultivation and planting,authenticity identification of decoction pieces,compliance review of manufacturing processes,market circulation monitoring,and risk assessment.However,despite existing research achievements,challenges such as limited technological adoption,low standardization,and insufficient data sharing persist.This paper provides an in-depth review of innovative AI applications in TCM quality control,comprehensively analyzes their effectiveness and future prospects,and proposes a novel"AI-Digital Sensory-Blockchain"tri-synergistic quality control paradigm based on domestic and international research advancements.Furthermore,it explores potential future research directions to advance the modernization of TCM quality regulation,thereby achieving higher levels of safety assurance and scien-tifically robust supervision.

Objective:To investigate the current status and influencing factors of ICU nurses' perception of risks and benefits of family presence during resuscitation, and to provide scientific evidence for the formulation of relevant policies in medical institutions.Methods:A convenience sampling method was used to recruit 370 ICU nurses from four Class Ⅲ Grade A hospitals in Taizhou between October and December 2024. A general information questionnaire, the Chinese version of the Family Presence Risk-Benefit Scale (FPR-BS), and the Chinese version of the Family Presence Self-Confidence Scale (FPS-CS) were used. Hierarchical multiple linear regression was applied to explore the influencing factors of ICU nurses' perception of risks and benefits of family presence during resuscitation.Results:A total of 370 questionnaires were distributed, and 357 valid questionnaires were collected, with a valid response rate of 96.49%. The total score of the Chinese version of FPR-BS among 357 ICU nurses was (50.25±10.54), and the total score of the Chinese version of FPS-CS was (40.36±8.19). Hierarchical multiple linear regression analysis showed that gender, age, years of work experience, educational level, number of times participating in resuscitation, and self-confidence regarding family presence during resuscitation were influencing factors of risk-benefit perception ( P<0.05) . Conclusions:ICU nurses' perception of the risks and benefits of family presence during resuscitation was at a relatively low to moderate level. Self-confidence regarding family presence during resuscitation was correlated with risk-benefit perception. When implementing policies on family presence during resuscitation, medical institutions should first improve nurses' self-confidence in this regard and provide targeted support.