arcopenia is a systemic skeletal muscle disorder characterized by a decrease in skeletal muscle mass and progressive decline in function， with multiple signaling pathways involved in its occurrence and development. Among them， the AMP-activated protein kinase （AMPK） signaling pathway， as a key pathway regulating cellular energy homeostasis， plays an important role in the regulation of skeletal muscle metabolism and functional maintenance by improving abnormalities in glucose and lipid metabolism， balancing skeletal muscle protein synthesis and degradation， improving mitochondrial function， promoting autophagy， and inhibiting inflammatory responses and oxidative stress. This article reviews the research progress on how various traditional Chinese medicine （TCM） monomers， including polyphenols， flavonoids， and terpenoids； various traditional Chinese medicine extracts， such as those from Lycium barbarum ， Asini Corii Colla， and Panax quinquefolium ， and TCM compounds， such as Guiqi zhuangjin decoction， Jianpi qiangji granules， and Qigu capsules， intervene in sarcopenia by regulating the AMPK signaling pathway to promote muscle protein synthesis， inhibit protein degradation， improve mitochondrial function， and alleviate inflammation and oxidative stress. Additionally， their molecular mechanisms are explored. The aim is to deeply elucidate the basis of TCM in the prevention and treatment of sarcopenia and to provide theoretical support for the development of related innovative drugs.

Objective To establish a fluorescent recombinase-aided amplification (RAA) assay for detection of Strongyloides stercoralis nucleic acid and to preliminarily evaluate its performance. Methods Six sets of specific primers targeting S. stercoralis 18S ribosomal RNA (18S rRNA) gene and one fluorescent probe were designed and synthesized. The optimal primer-probe set was determined through systematic screening and optimization to establish the fluorescent RAA assay. The assay was evaluated using S. stercoralis genomic DNA at concentrations of 100, 10, and 1 pg/μL, and 100, 10, and 1 fg/μL, as well as recombinant pUC57 plasmids containing the target gene fragments at 1 × 10⁵, 1 × 10⁴, 1 × 10³, 1 × 10², 1 × 10¹, 1 × 10⁰ copies/reaction, to determine the analytical sensitivity. Genomic DNA from Ascaris lumbricoides, Ancylostoma duodenale, Enterobius vermicularis, Angiostrongylus cantonensis, Trichinella spiralis, Clonorchis sinensis, Schistosoma japonicum, and Taenia saginata was used to assess assay specificity. A total of 25 stool samples from patients suspected of S. stercoralis infection were tested by the modified Baermann funnel technique, PCR, and the established fluorescent RAA assay. The sensitivity, specificity, concordance rate and their 95% confidence intervals (CI) of these three techniques were estimated, and agreement between methods was evaluated using the Kappa coefficient. Results Exo-4 was identified as the optimal primer set screened from the six primer sets, and the best amplification performance was achieved when the final concentrations of the forward and reverse primers were 0.44 μmol/L and a probe concentration was 0.20 μmol/L. The limit of detection of the fluorescent RAA assay was 100 fg/μL for genomic DNA of S. stercoralis and 1 × 10⁰ copies/reaction for recombinant plasmids. Specific fluorescence signals were detected within 5 min, with no cross-reactivity observed with A. lumbricoides, A. duodenale, E. vermicularis, A. cantonensis, T. spiralis, C. sinensis, S. japonicum, or T. saginata. Among the 25 clinical stool samples from patients suspected of S. stercoralis infections, the modified Baermann funnel technique and fluorescent RAA assay detected 19 positives and 6 negatives, whereas PCR detected 18 positives and 7 negatives. The fluorescent RAA assay showed a sensitivity of 100.00% [95% CI: (82.35%, 100.00%)], specificity of 100.00% [95% CI: (54.07%, 100.00%)], concordance rate of 100.00% [95% CI: (86.28%, 100.00%)], and a Kappa coefficient of 1.00 [95% CI: (1.00, 1.00)] (P < 0.001) relative to the modified Baermann funnel technique, and a sensitivity of 100.00% [95% CI: (81.47%, 100.00%)], specificity of 85.71% [95% CI: (42.13%, 99.64%)], concordance rate of 96.00% [95% CI: (79.65%, 99.90%)], and a Kappa coefficient of 0.90 [95% CI: (0.70, 1.00)] (P < 0.001). Positive amplification products emitted green fluorescence under a portable blue-light device, enabling visual interpretation of results. Conclusions The fluorescent RAA assay established in this study is rapid, highly sensitive, and highly specific. It enables detection of S. stercoralis nucleic acid under isothermal conditions and allows visual interpretation of results, providing a novel tool for rapid clinical diagnosis and field screening of S. stercoralis infections.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To investigate the causes and factors affecting unplanned reoperation,and to provide a reference basis for reducing the incidence of unplanned return of the patient to the operating room for reoperation.Methods Surgical data from the hospital was extracted spanning from January to December 2022,and subjected to a descriptive analysis of the overall situation,departmental distribution,primary reasons,and patient referrals related to unplanned reoperations in the hospital,and analyzed factors contributing to unplanned reoperations in the hospital using binary logistic regression.Results In 2022,130 unplanned reoperations were reported in this hospital,corresponding to an incidence of 0.35％.Patients who required unplanned reoperation were predominantly male(63.08％).The majority had surgical incision grade of category 0(46.92％),and surgeries were classified as levels 3 and 4(80.77％).Furthermore,88.46％of the surgeries were performed by surgeons with advanced degrees or higher.The common causes were postoperative bleeding,failure to achieve the desired result,need for the condition,probing for the cause,and occurrence of leakage or fistula,collectively accounting for 50.00％of the cases.Key factors contributing to unplanned reoperations were sex,type of surgical incision,and incision healing grade;among which male patients(OR=1.733,P=0.006),patients with class Ⅰ surgical incision(OR=2.909,P=0.004),and patients with incision grade B healing(OR=6.565,P＜0.001)showed a higher propensity for unplanned reoperations.Conclusion Hos-pitals should emphasize monitoring and managing unplanned reoperations by improving perioperative supervision,conducting thorough root cause analyses,and focusing on continuous quality improvement to enhance surgical outcomes and patient safety.

Objective To investigate the causes and factors affecting unplanned reoperation,and to provide a reference basis for reducing the incidence of unplanned return of the patient to the operating room for reoperation.Methods Surgical data from the hospital was extracted spanning from January to December 2022,and subjected to a descriptive analysis of the overall situation,departmental distribution,primary reasons,and patient referrals related to unplanned reoperations in the hospital,and analyzed factors contributing to unplanned reoperations in the hospital using binary logistic regression.Results In 2022,130 unplanned reoperations were reported in this hospital,corresponding to an incidence of 0.35％.Patients who required unplanned reoperation were predominantly male(63.08％).The majority had surgical incision grade of category 0(46.92％),and surgeries were classified as levels 3 and 4(80.77％).Furthermore,88.46％of the surgeries were performed by surgeons with advanced degrees or higher.The common causes were postoperative bleeding,failure to achieve the desired result,need for the condition,probing for the cause,and occurrence of leakage or fistula,collectively accounting for 50.00％of the cases.Key factors contributing to unplanned reoperations were sex,type of surgical incision,and incision healing grade;among which male patients(OR=1.733,P=0.006),patients with class Ⅰ surgical incision(OR=2.909,P=0.004),and patients with incision grade B healing(OR=6.565,P＜0.001)showed a higher propensity for unplanned reoperations.Conclusion Hos-pitals should emphasize monitoring and managing unplanned reoperations by improving perioperative supervision,conducting thorough root cause analyses,and focusing on continuous quality improvement to enhance surgical outcomes and patient safety.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective·To analyze the clinicopathologic characteristics,gene mutation profile,and prognostic factors of patients with adrenal diffuse large B-cell lymphoma(DLBCL).Methods·From March 2002 to December 2022,a total of 105 patients with adrenal DLBCL admitted to Ruijin Hospital,Shanghai Jiao Tong University School of Medicine were retrospectively analyzed for their clinicopathological data,survival outcomes,and prognostic factors.Patients'gene mutation profiles were evaluated by targeted sequencing of 152 lymphoma-related genes.Results·The median age of the patients was 62(15?82)years and the male-to-female ratio was 2.3∶1.Among them,63 patients(60.0%)were over 60 years old,22 patients(21.0%)had an Eastern Cooperative Oncology Group(ECOG)performance status of two or higher,87 patients(82.9%)were staged Ann Arbor Ⅲ?Ⅳ,92 patients(87.6%)had elevated serum lactate dehydrogenase(LDH)levels(above the upper limit of reference),84 patients(80.0%)had extranodal invasion in at least two organs,67 patients(63.8%)were of non-germinal center B-cell(non-GCB)origin,and 95 patients(90.5%)had an international prognosis index(IPI)scored over 2.With a median follow-up of 28.3(0.7?191.9)months,the estimated 2-year overall survival(OS)rate and progression-free survival(PFS)rate were 68.3%and 53.1%,respectively.The estimated 5-year OS rate and PFS rate were 52.6%and 44.0%,respectively.Among 93 patients who could be evaluated for clinical outcomes,62(66.7%)got a complete response(CR).Univariate analysis and multivariate Cox analysis revealed that age over 60 years was an adverse prognostic factor for PFS,and ECOG performance status of two or higher was an adverse prognostic factor for both OS and PFS.Targeted gene sequencing in 46 adrenal diffuse DLBCL patients showed high mutation frequencies in lysine methyltransferase 2D(KMT2D;n=17,37%),Pim-1 proto-oncogene,serine/threonine kinase(PIM1;n=17,37%),MYD88 innate immune signal transduction adaptor(MYD88;n=15,33%),CD79b molecule(CD79B;n=13,28%),and BTG anti-proliferation factor 2(BTG2;n=10,22%).Conclusion·Age over 60 years is an adverse prognostic factor for PFS,and ECOG performance status of two or higher is an adverse prognostic factor for both OS and PFS in patients with adrenal DLBCL.Patients exhibited high frequencies of KMT2D,PIM1,MYD88,CD79B,and BTG2 mutations,as well as an increased proportion of the MCD-like subtype.

Objective:To investigate the inhibitory effect and mechanism of puerarin(Pue)on hydrogen peroxide(H2 O2)induced ferroptosis in the rat Schwann cell derived cell line RSC96.Methods:The RSC96 cells were incuba-ted with H2 O2 to establish a cellular injury model,while a subset of cells were co-incubated with H2 O2 and Pue.The vi-ability of cells was examined using the CCK-8 assay.The intracellular levels of glutathione(GSH),total superoxide dismutase(T-SOD),reactive oxygen species(ROS),malondialdehyde(MDA),and ferrous ions(Fe2+)levels were quantified using commercial kits.Western blot was employed to detect the protein expression level of glutathione peroxi-dase 4(GPX4),cyclooxygenase-2(COX-2),solute carrier family 7 member 11(SLC7A11),nuclear factor erythroid 2-related factor 2(Nrf2),and heme oxygenase-1(HO-1).Immunofluorescence assay was performed to examine the expression and nuclear distribution of Nrf2.Results:Pue pretreatment significantly increased the survival rate of H2 O2-treated RSC96 cells,increased the intracellular content of GSH and T-SOD,and inhibited the concentration of ROS and MDA.It also activated the nuclear translocation of Nrf2 and upregulated GPX4,SLC7A11,HO-1,and Nrf2 proteins in RSC96 cells;This effect was abolished by the Nrf2 inhibitor ML385.Conclusion:Pue treatment alleviated the H2 O2-induced ferroptosis of RSC96 cells via the Nrf2/SLC7A11/GPX4 signaling pathway.

Objective:To investigate the inhibitory effect and mechanism of puerarin(Pue)on hydrogen peroxide(H2 O2)induced ferroptosis in the rat Schwann cell derived cell line RSC96.Methods:The RSC96 cells were incuba-ted with H2 O2 to establish a cellular injury model,while a subset of cells were co-incubated with H2 O2 and Pue.The vi-ability of cells was examined using the CCK-8 assay.The intracellular levels of glutathione(GSH),total superoxide dismutase(T-SOD),reactive oxygen species(ROS),malondialdehyde(MDA),and ferrous ions(Fe2+)levels were quantified using commercial kits.Western blot was employed to detect the protein expression level of glutathione peroxi-dase 4(GPX4),cyclooxygenase-2(COX-2),solute carrier family 7 member 11(SLC7A11),nuclear factor erythroid 2-related factor 2(Nrf2),and heme oxygenase-1(HO-1).Immunofluorescence assay was performed to examine the expression and nuclear distribution of Nrf2.Results:Pue pretreatment significantly increased the survival rate of H2 O2-treated RSC96 cells,increased the intracellular content of GSH and T-SOD,and inhibited the concentration of ROS and MDA.It also activated the nuclear translocation of Nrf2 and upregulated GPX4,SLC7A11,HO-1,and Nrf2 proteins in RSC96 cells;This effect was abolished by the Nrf2 inhibitor ML385.Conclusion:Pue treatment alleviated the H2 O2-induced ferroptosis of RSC96 cells via the Nrf2/SLC7A11/GPX4 signaling pathway.

Objective·To analyze the clinicopathologic characteristics,gene mutation profile,and prognostic factors of patients with adrenal diffuse large B-cell lymphoma(DLBCL).Methods·From March 2002 to December 2022,a total of 105 patients with adrenal DLBCL admitted to Ruijin Hospital,Shanghai Jiao Tong University School of Medicine were retrospectively analyzed for their clinicopathological data,survival outcomes,and prognostic factors.Patients'gene mutation profiles were evaluated by targeted sequencing of 152 lymphoma-related genes.Results·The median age of the patients was 62(15?82)years and the male-to-female ratio was 2.3∶1.Among them,63 patients(60.0%)were over 60 years old,22 patients(21.0%)had an Eastern Cooperative Oncology Group(ECOG)performance status of two or higher,87 patients(82.9%)were staged Ann Arbor Ⅲ?Ⅳ,92 patients(87.6%)had elevated serum lactate dehydrogenase(LDH)levels(above the upper limit of reference),84 patients(80.0%)had extranodal invasion in at least two organs,67 patients(63.8%)were of non-germinal center B-cell(non-GCB)origin,and 95 patients(90.5%)had an international prognosis index(IPI)scored over 2.With a median follow-up of 28.3(0.7?191.9)months,the estimated 2-year overall survival(OS)rate and progression-free survival(PFS)rate were 68.3%and 53.1%,respectively.The estimated 5-year OS rate and PFS rate were 52.6%and 44.0%,respectively.Among 93 patients who could be evaluated for clinical outcomes,62(66.7%)got a complete response(CR).Univariate analysis and multivariate Cox analysis revealed that age over 60 years was an adverse prognostic factor for PFS,and ECOG performance status of two or higher was an adverse prognostic factor for both OS and PFS.Targeted gene sequencing in 46 adrenal diffuse DLBCL patients showed high mutation frequencies in lysine methyltransferase 2D(KMT2D;n=17,37%),Pim-1 proto-oncogene,serine/threonine kinase(PIM1;n=17,37%),MYD88 innate immune signal transduction adaptor(MYD88;n=15,33%),CD79b molecule(CD79B;n=13,28%),and BTG anti-proliferation factor 2(BTG2;n=10,22%).Conclusion·Age over 60 years is an adverse prognostic factor for PFS,and ECOG performance status of two or higher is an adverse prognostic factor for both OS and PFS in patients with adrenal DLBCL.Patients exhibited high frequencies of KMT2D,PIM1,MYD88,CD79B,and BTG2 mutations,as well as an increased proportion of the MCD-like subtype.