Objective:To investigate the prognosis of postoperative adjuvant therapy for hepatocellular carcinoma after conversion therapy of combined targeted therapy and immunotherapy followed by sequential hepatectomy.Methods:The retrospective cohort study was conducted. The clinicopathological data of 103 patients with initially unresectable hepatocellular carcinoma (HCC) who were admitted to 11 medical centers in China, including Mengchao Hepatobiliary Hospital of Fujian Medical University et al, from November 2019 to May 2023 were collected. There were 83 males and 20 females, aged (54±12)years. All 103 patients underwent conversion therapy of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) successfully followed by sequential hepatectomy, of which 72 patients undergoing postoperative adjuvant therapy were divided into the adjuvant therapy group, and 31 patients undergoing postoperative follow-up monitoring were divided into the follow-up monitoring group. Observation indicators: (1) follow-up and postoperative condi-tions; (2) analysis of factors influencing recurrence-free survival time of patients; (3) stratified ana-lysis. Comparison of count data between group was conducted using the chi-square test or Fisher exact probability. The R software was used to draw survival curves, and the Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the Cox proportional hazard model. Results:(1) Follow-up and postoperative conditions. All 103 patients were followed up for 21.0(range, 1.9?47.2)months, with the median recurrence-free survival time of 28.7 months and the 1-, 2-, 3-year recurrence-free survival rates of 68.6%, 55.6%, 41.2%. The median overall survival time of 103 patients was unreached, and the 1-, 2-, 3-year overall survival rates were 90.9%, 82.1%, 69.6%, respectively. The median recurrence-free survival time was 33.1 months in patients of the adjuvant therapy group, with the 1-, 2-year recurrence-free survival rates as 77.2%, 61.5%. The median recurrence-free survival time was 11.1 months in patients of the follow-up monitoring group, with the 1-, 2-year recurrence-free survival rates as 46.6%, 40.8%. There was a significant difference in recurrence-free survival between the two groups of patients ( χ2=5.492, P<0.05). (2) Analysis of factors influencing recurrence-free survival time of patients. Results of multivariate analy-sis showed that pathologic complete response and postoperative adjuvant therapy were independent factors influencing recurrence-free survival time of HCC patients undergoing conversion therapy of combined targeted therapy and immunotherapy followed by sequential hepatectomy ( hazard ratio=0.297, 0.492, 95% confidence interval as 0.137?0.647, 0.268?0.903, P<0.05). (3) Stratified analysis. Of the 71 patients with non-pathologic complete response, the median recurrence-free survival time of 48 patients in the adjuvant therapy group was 24.0 months, with the 1-, 2-year recurrence-free survival rates as 67.4%, 48.8%. The median recurrence-free survival time of 23 patients with non-pathological complete response in the follow-up monitoring group was 7.4 months, with the 1-, 2-year recurrence-free survival rates as 35.0%, 26.3%. There was a significant difference in recurrence-free survival between the 48 patients with non-pathologic complete response in the adjuvant therapy group and the 23 patients with non-pathologic complete response in the follow-up monitoring group ( χ2=5.241, P<0.05). Conclusion:For HCC patients with conversion therapy of TKIs and ICIs followed by sequential hepatectomy, postoperative adjuvant therapy, compared to postoperative follow-up monitoring, can prolong the recurrence-free survival time of patients, of whom cases with non-pathologic complete response can benefit from adjuvant therapy.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective:To investigate the prognosis of postoperative adjuvant therapy for hepatocellular carcinoma after conversion therapy of combined targeted therapy and immunotherapy followed by sequential hepatectomy.Methods:The retrospective cohort study was conducted. The clinicopathological data of 103 patients with initially unresectable hepatocellular carcinoma (HCC) who were admitted to 11 medical centers in China, including Mengchao Hepatobiliary Hospital of Fujian Medical University et al, from November 2019 to May 2023 were collected. There were 83 males and 20 females, aged (54±12)years. All 103 patients underwent conversion therapy of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) successfully followed by sequential hepatectomy, of which 72 patients undergoing postoperative adjuvant therapy were divided into the adjuvant therapy group, and 31 patients undergoing postoperative follow-up monitoring were divided into the follow-up monitoring group. Observation indicators: (1) follow-up and postoperative condi-tions; (2) analysis of factors influencing recurrence-free survival time of patients; (3) stratified ana-lysis. Comparison of count data between group was conducted using the chi-square test or Fisher exact probability. The R software was used to draw survival curves, and the Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the Cox proportional hazard model. Results:(1) Follow-up and postoperative conditions. All 103 patients were followed up for 21.0(range, 1.9?47.2)months, with the median recurrence-free survival time of 28.7 months and the 1-, 2-, 3-year recurrence-free survival rates of 68.6%, 55.6%, 41.2%. The median overall survival time of 103 patients was unreached, and the 1-, 2-, 3-year overall survival rates were 90.9%, 82.1%, 69.6%, respectively. The median recurrence-free survival time was 33.1 months in patients of the adjuvant therapy group, with the 1-, 2-year recurrence-free survival rates as 77.2%, 61.5%. The median recurrence-free survival time was 11.1 months in patients of the follow-up monitoring group, with the 1-, 2-year recurrence-free survival rates as 46.6%, 40.8%. There was a significant difference in recurrence-free survival between the two groups of patients ( χ2=5.492, P<0.05). (2) Analysis of factors influencing recurrence-free survival time of patients. Results of multivariate analy-sis showed that pathologic complete response and postoperative adjuvant therapy were independent factors influencing recurrence-free survival time of HCC patients undergoing conversion therapy of combined targeted therapy and immunotherapy followed by sequential hepatectomy ( hazard ratio=0.297, 0.492, 95% confidence interval as 0.137?0.647, 0.268?0.903, P<0.05). (3) Stratified analysis. Of the 71 patients with non-pathologic complete response, the median recurrence-free survival time of 48 patients in the adjuvant therapy group was 24.0 months, with the 1-, 2-year recurrence-free survival rates as 67.4%, 48.8%. The median recurrence-free survival time of 23 patients with non-pathological complete response in the follow-up monitoring group was 7.4 months, with the 1-, 2-year recurrence-free survival rates as 35.0%, 26.3%. There was a significant difference in recurrence-free survival between the 48 patients with non-pathologic complete response in the adjuvant therapy group and the 23 patients with non-pathologic complete response in the follow-up monitoring group ( χ2=5.241, P<0.05). Conclusion:For HCC patients with conversion therapy of TKIs and ICIs followed by sequential hepatectomy, postoperative adjuvant therapy, compared to postoperative follow-up monitoring, can prolong the recurrence-free survival time of patients, of whom cases with non-pathologic complete response can benefit from adjuvant therapy.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Chinese Society of Hepatology and Chinese Society of Infectious Diseases, Chinese Medical Association update the guidelines for the prevention and treatment of chronic hepatitis B (version 2022) in 2022. The latest guidelines recommend more extensive screening and more active antiviral treating for hepatitis B virus infection. This article interprets the essential updates in the guidelines to help deepen understanding and better guide the clinical practice.
Humans ; Hepatitis B, Chronic/drug therapy* ; Hepatitis B/drug therapy* ; Hepatitis B virus ; Antiviral Agents/therapeutic use* ; Gastroenterology

Objective: To explore the prognostic factors of extracellular NK/T cell lymphoma (ENKTL) treated with pegaspargase/L-asparaginase. Methods: The clinical data of 656 ENKTL patients diagnosed at 11 medical centers in the Huaihai Lymphoma Working Group from March 2014 to April 2021 were retrospectively analyzed. The patients were randomly divided into two groups: a training set (460 cases) and a validation set (196 cases) at 7∶3, and the prognostic factors of the patients were analyzed. A prognostic scoring system was established, and the predictive performance of different models was compared. Results: Patients' median age was 46 (34, 57) years, with 456 males (69.5% ) and 561 nasal involvement (85.5% ). 203 patients (30.9% ) received a chemotherapy regimen based on L-asparaginase combined with anthracyclines, and the 5-year overall survival rate of patients treated with P-GEMOX regimen (pegaspargase+gemcitabine+oxaliplatin) was better than those treated with SMILE regimen (methotrexate+dexamethasone+cyclophosphamide+L-asparaginase+etoposide) (85.9% vs 63.8% ; P=0.004). The results of multivariate analysis showed that gender, CA stage, the Eastern Cooperative Oncology Group performance status (ECOG PS) score, HGB, and EB virus DNA were independent influencing factors for the prognosis of ENKTL patients (P<0.05). In this study, the predictive performance of the prognostic factors is superior to the international prognostic index, Korean prognostic index, and prognostic index of natural killer lymphoma. Conclusion: Gender, CA stage, ECOG PS score, HGB, and EB virus DNA are prognostic factors for ENKTL patients treated with pegaspargase/L-asparaginase.
Male ; Humans ; Middle Aged ; Asparaginase/therapeutic use* ; Prognosis ; Retrospective Studies ; Lymphoma, Extranodal NK-T-Cell/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Etoposide ; Cyclophosphamide ; Methotrexate/therapeutic use* ; DNA/therapeutic use* ; Treatment Outcome