OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method.METHODS The contents of 12 components in 15 batches of Sanzi powder(No.S1-S15)were determined by HPLC-MS/MS,such as ethyl gallate,gallic acid,ferulic acid,corilagin,genipin-1-O-β-D-gentiobioside,toosendanin,geniposide,caffeic acid,methyl deacetylated coumarinate,tannic acid,rutin,quercetin.Cluster analysis(CA),principal component analysis(PCA),and orthogonal partial least squares-discriminant analysis(OPLS-DA)were conducted on the assay results.Using variable importance projection(VIP)value＞1 and P＜0.05 as the evaluation criteria,the quality differential markers in Sanzi powder were screened.The entropy weight method was used to calculate the weight value,and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior.RESULTS The contents of the 12 components were 13.494-24.292,2 069.608-3 188.100,1.410-3.616,1 065.030-2 630.584,1 404.704-1 838.078,101.640-354.268,9 193.720-14 777.854,1.240-5.060,148.028-5 541.990,4 261.422-5 607.438,107.560-195.512,2.226-4.192 μg/g,respectively.The results of CA,PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups.Specifically,batches S3,S7,S10 and S15 were grouped into one category,and remaining batches were grouped into one category.VIP values of geniposide,quercetin,caffeic acid,and methyl deacetylated coumarinate were all greater than 1,with corresponding P-values less than 0.05.The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight.The relative closeness degrees of samples S3,S7,S10 and S15 ranged from 0.789 to 0.973,while the remaining samples ranged from 0.054 to 0.172.CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology.Methyl deacetylated coumarinate,geniposide,quercetin and caffeic acid were identified as the quality differential markers.It was found that the overall quality of samples S3,S7,S10 and S15 were superior to that of other batches.Notably,the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation's quality.

As the globalization process accelerates and international medical exchanges become increasingly frequent, the status of foreign trainees in Chinese medical education system has become increasingly important. This paper explores the application of artificial intelligence technology in teaching vascular surgery for foreign trainees, including its specific application forms in theoretical teaching, surgical skill training, case analysis, and clinical decision-making. This article analyzes the advantages of integrating artificial intelligence into teaching foreign trainees, such as improving teaching efficiency, enhancing individualized teaching, and promoting cultural integration. Moreover, this paper discusses the challenges such as data security and privacy protection, technical reliability and adaptability, the transformation of educational concepts, and cross-cultural education, and proposes corresponding strategies to address these issues. The rational use of artificial intelligence technology is expected to improve the teaching quality of vascular surgery for foreign trainees and make a positive contribution to the cultivation of international medical talents.

OBJECTIVE To evaluate the quality of Mongolian medicine Sendeng-4 based on qualitative and quantitative analysis combined with chemical pattern recognition， in order to provide the reference for its quality control. METHODS The chemical components in Sendeng-4 were analyzed qualitatively by HPLC-Q-Exactive-MS. The contents of 16 components （methyl gallate， ethyl gallate， epicatechin， dihydromyricetin， genipin-1-O-β-D-gentiobioside， caffeic acid， catechin， corilagin， deacetylasperulosidic acid methyl ester， rutin， geniposide， luteolin， myricetin， quercetin， ferulic acid， and toosendanin） in 15 batches of Sendeng-4 （sample S1-S15） were quantitatively analyzed by HPLC-MS/MS. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares discriminant analysis were conducted and variable importance projection （VIP） value greater than 1 was used as the index to screen the differential components. RESULTS A total of 73 chemical components were identified in Sendeng-4， including 20 flavonoids， 16 tannins， 14 organic acids， etc. According to the quantitative analysis， the results exhibited that the average contentsthe of above 16 components in 15 batches of Sendeng-4 were 3.683-7.730， 2.391-6.952， 2 275.538-4 377.491， 2 699.188-3 537.924， 858.266-1 377.393， 3.366-11.003， 140.624-315.683，414.629-978.334， 285.501-1 510.457， 27.799-48.325， 3 625.415-6 309.563， 0.506-0.656， 442.337-649.283， 47.093-59.736， 12.942-15.822， 127.738-326.649 μg/g， respectively. According to the results of CA and PCA， 15 batches of samples could be clustered into two categories: S1-S3， S5-S6， S9-S10 and S13 were clustered into one category； S4， S7-S8， S11-S12， S14-S15 were clustered into one category. VIP values of geniposide， epicatechin， deacetylasperulosidic acid methyl ester and genipin-1-O- β-D-gentiobioside were all greater than 1. CONCLUSIONS HPLC-Q-Exactive-MS and HPLC-MS/MS techniques are employed for the qualitative and quantitative analysis of Sendeng-4. Through chemical pattern recognition analysis， four differential components are identified: geniposide， epicatechin， deacetylasperulosidic acid methyl ester， and genipin-1-O-β-D-gentiobioside.

Hepatitis B virus (HBV)-specific CD8⁺ T cells play a central role in controlling HBV infection; however, their function is impaired during chronic HBV infection, manifesting as a state of dysfunction. Recent studies have revealed that CD8⁺ T cell dysfunction in chronic HBV infection differs from the classical exhaustion observed in other viral infections or tumors. In 2024, several pivotal studies further elucidated novel mechanisms underlying CD8⁺ T cell dysfunction in chronic HBV infection and identified new therapeutic targets, including 4-1BB and transforming growth factor-beta (TGF-β). This review, while elucidating the dysfunction of CD8⁺ T cells in chronic HBV infection and its underlying mechanisms, focuses on summarizing the key findings from these latest studies and explores their translational value and clinical significance.
Humans ; Hepatitis B, Chronic/virology* ; CD8-Positive T-Lymphocytes/immunology* ; Hepatitis B virus/physiology* ; Animals ; Transforming Growth Factor beta/immunology*

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

As the globalization process accelerates and international medical exchanges become increasingly frequent, the status of foreign trainees in Chinese medical education system has become increasingly important. This paper explores the application of artificial intelligence technology in teaching vascular surgery for foreign trainees, including its specific application forms in theoretical teaching, surgical skill training, case analysis, and clinical decision-making. This article analyzes the advantages of integrating artificial intelligence into teaching foreign trainees, such as improving teaching efficiency, enhancing individualized teaching, and promoting cultural integration. Moreover, this paper discusses the challenges such as data security and privacy protection, technical reliability and adaptability, the transformation of educational concepts, and cross-cultural education, and proposes corresponding strategies to address these issues. The rational use of artificial intelligence technology is expected to improve the teaching quality of vascular surgery for foreign trainees and make a positive contribution to the cultivation of international medical talents.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

OBJECTIVE To establish fingerprint,chemical pattern recognition and multi-component quantification analysis of Sanzi powder,and evaluate its quality.METHODS HPLC method was adopted.The fingerprints of 15 batches of Sanzi powder were established by using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(2012 edition).Cluster analysis,principal component analysis and orthogonal partial least squares-discriminant analysis were also conducted.The variable importance in projection(VIP)value greater than 1 was used as the index to screen the differential markers,and the contents of the differential markers were determined by the same HPLC method.RESULTS A total of 21 common peaks in the HPLC fingerprints of 15 batches of Sanzi powder were calibrated,and the similarities of them were 0.994-0.999;6 common peaks were identified,including gallic acid(peak 3),garminoside(peak 10),corilagin(peak 11),chebulinic acid(peak 16),ellagic acid(peak 18),crocin Ⅰ(peak 19).According to the results of cluster analysis,principal component analysis and orthogonal partial least squares-discriminant analysis,15 batches of samples could be clustered into two categories:S1,S5,S7,S9,S14 were clustered into one category;S2-S4,S6,S8,S10-S13,S15 were clustered into one category.VIP values of 11 differential components such as corilagin,chebulinic acid and ellagic acid were higher than 1.Among 15 batches of samples,the contents of corilagin,chebulinic acid and ellagic acid ranged 2.667-5.152,9.506-13.522,0.891-1.811 mg/g.CONCLUSIONS Established HPLC fingerprint and multi-component quantification analysis of Sanzi powder are rapid and simple,and can be used for quality evaluation of Sanzi powder by combining with chemical pattern recognition.Eleven components such as corilagin,chebulinic acid and ellagic acid are differential markers affecting the quality of Sanzi powder.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.