Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

ObjectiveTo investigate the efficacy and safety of lenvatinib combined with sintilimab versus atezolizumab combined with bevacizumab in patients with unresectable hepatocellular carcinoma （uHCC）， aims to provide real-world evidence for clinical personalized treatment. MethodsA retrospective analysis was performed for 78 patients with uHCC who were admitted to Beijing Ditan Hospital， Capital Medical University， from January 1， 2023， to May 31， 2025， and according to the treatment modality， they were divided into lenvatinib+sintilimab group （L+S group with 49 patients） and atezolizumab+bevacizumab group （A+T group with 29 patients）. The primary endpoints were progression-free survival （PFS） and overall survival （OS）， and the secondary endpoints included objective response rate （ORR）， disease control rate （DCR）， and the incidence rate of adverse events. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method was used for survival analysis， and the log-rank test was used for comparison between groups. ResultsThe 78 patients had a median PFS of 9 months and a median OS of 15 months. The median PFS was 11 months in the L+S group and 7 months in the A+T group， with no significant difference between the two groups （χ²=0.247， P=0.619）； the median OS was 19 months in the L+S group and 12 months in the A+T group， with a significant difference between the two groups （χ²=6.565， P=0.010）. There were no significant differences between the two groups in complete remission， partial remission， stable disease， disease progression， DCR， and ORR （all P>0.05）. The L+S group had a significantly higher incidence rate of adverse events than the A+T group （95.9% vs 75.9%， P=0.007）， and there was a significant difference in the incidence rate of grade ≥3 adverse events between the L+S group and the A+T group （65.3% vs 34.5%， P=0.008）. ConclusionCompared with atezolizumab combined with bevacizumab， lenvatinib combined with sintilimab can improve the OS of patients with uHCC， while atezolizumab combined with bevacizumab has a better safety profile.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Objective To understand the epidemiological characteristics and trends of postoperative pneumonia(POP)in tertiary general hospitals in Jiangsu Province,and provide theoretical basis for carrying out targeted pre-vention and control measures.Methods Surgery patients from 22 tertiary general hospitals in 12 cities in north,central,and south of Jiangsu Province from January 1,2022 to December 31,2023 were chosen as studied subjects,occurrence of POP was analyzed and compared.Results A total of 848 274 surgical procedures were performed in 22 hospitals,and 3 606 cases of POP occurred,with an incidence of 0.43％.The incidence in 2023 was 0.37％,which was lower than that in 2022(0.49％),with statistically significant difference(P＜0.001).The top three de-partments with high incidence of POP were neurosurgery(6.71％),cardiothoracic surgery(2.91％),and general surgery(0.77％).Among hospitals of different grades,the incidence of POP in tertiary first-class hospitals was 0.44％,which was higher than that in other tertiary hospitals(0.37％).There was no statistically significant difference in the incidence of POP between municipal and district/county hospitals(P＞0.05).The incidence of POP in hospitals with a bed:infection control full-time staff ratio＜200∶1 was lower than that in hospitals with the ratio ≥200∶1(0.39％vs 0.47％,P＜0.001),while the incidence of POP in hospitals with a proportion ≥30％of full-time staff being doctors was higher than that in hospitals with a proportion＜30％(0.45％vs 0.36％,P＜0.001).The incidence of POP in male patients was higher than that in female patients(0.62％vs 0.26％,P＜0.001).The incidence of POP in elderly patients aged≥65 was higher than that in patients aged＜65(0.73％vs 0.26％,P＜0.001).A total of 2 667 strains of infectious pathogens were detected,with the top three being Acine-tobacter baumannii,Klebsiella pneumoniae,and Pseudomonas aeruginosa,accounting for 28.95％,22.72％,and 15.45％,respectively.The detection rates of carbapenem-resistant Acinetobacter baumannii(CRAB),carba-penem-resistant Klebsiella pneumoniae(CRKP),and carbapenem-resistant Pseudomonas aeruginosa(CRPA)were 60.75％,21.45％,and 32.28％,respectively.The detection rate of CRKP decreased in 2023 compared with 2022,with statistically significant difference(P＜0.05).Conclusion The overall incidence of POP in tertiary general hos-pitals in Jiangsu Province is relatively low,but there are significant differences among different hospitals.There-fore,perioperative prevention and control measures should be carried out based on the epidemiological characteristics of patients.

Objective To understand the epidemiological characteristics and trends of postoperative pneumonia(POP)in tertiary general hospitals in Jiangsu Province,and provide theoretical basis for carrying out targeted pre-vention and control measures.Methods Surgery patients from 22 tertiary general hospitals in 12 cities in north,central,and south of Jiangsu Province from January 1,2022 to December 31,2023 were chosen as studied subjects,occurrence of POP was analyzed and compared.Results A total of 848 274 surgical procedures were performed in 22 hospitals,and 3 606 cases of POP occurred,with an incidence of 0.43％.The incidence in 2023 was 0.37％,which was lower than that in 2022(0.49％),with statistically significant difference(P＜0.001).The top three de-partments with high incidence of POP were neurosurgery(6.71％),cardiothoracic surgery(2.91％),and general surgery(0.77％).Among hospitals of different grades,the incidence of POP in tertiary first-class hospitals was 0.44％,which was higher than that in other tertiary hospitals(0.37％).There was no statistically significant difference in the incidence of POP between municipal and district/county hospitals(P＞0.05).The incidence of POP in hospitals with a bed:infection control full-time staff ratio＜200∶1 was lower than that in hospitals with the ratio ≥200∶1(0.39％vs 0.47％,P＜0.001),while the incidence of POP in hospitals with a proportion ≥30％of full-time staff being doctors was higher than that in hospitals with a proportion＜30％(0.45％vs 0.36％,P＜0.001).The incidence of POP in male patients was higher than that in female patients(0.62％vs 0.26％,P＜0.001).The incidence of POP in elderly patients aged≥65 was higher than that in patients aged＜65(0.73％vs 0.26％,P＜0.001).A total of 2 667 strains of infectious pathogens were detected,with the top three being Acine-tobacter baumannii,Klebsiella pneumoniae,and Pseudomonas aeruginosa,accounting for 28.95％,22.72％,and 15.45％,respectively.The detection rates of carbapenem-resistant Acinetobacter baumannii(CRAB),carba-penem-resistant Klebsiella pneumoniae(CRKP),and carbapenem-resistant Pseudomonas aeruginosa(CRPA)were 60.75％,21.45％,and 32.28％,respectively.The detection rate of CRKP decreased in 2023 compared with 2022,with statistically significant difference(P＜0.05).Conclusion The overall incidence of POP in tertiary general hos-pitals in Jiangsu Province is relatively low,but there are significant differences among different hospitals.There-fore,perioperative prevention and control measures should be carried out based on the epidemiological characteristics of patients.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective To carry out a proficiency testing of content determination of geniposide in Gardeniae fructus,evaluate the content determination ability of index components in traditional Chinese medicine in the laboratory of inspection and detection in drug-related fields,and improve the quality control ability of content determination of related laboratories.Methods The laboratory's capability-verification activities were conducted based on the CNAS-RL02 Rules for Proficiency Testing and ISO/IEC 17043 Conformity Assessment-General Requirements for Proficiency Testing.After preparing the sample,the results of homogeneity and stability tests were analyzed according to CNAS-GL003 Guidance on Evaluating the Homogeneity and Stability of Samples Used for Proficiency Testing.After the test results were qualified,they were used as proficiency testing samples and randomly distributed to participants.The results were collected,and the robust statistical method and the Z scores were used to analyze the results of these laboratories'reports.Results 403 laboratories in this proficiency testing program reported the results,of which 367 results were acceptable,accounting for 91.07％,17(4.22％)laboratories obtained suspicious results,and 19 laboratories gave unsatisfactory results,with the dissatisfaction rate of 4.71％.Conclusion The majority of the 403 participant laboratories have the ability to determine the content of geniposide in Gardeniae fructus by HPLC and the laboratory testing ability and quality management level of the drug monitoring system are high.This proficiency testing provides a basis for understanding the technical reserve capacity and management level of China's pharmaceutical inspection and testing laboratories,and provides technical support for future government supervision.

Objective:To examine the relationship between internalized homophobia and self-concept clarity in lesbians,and explore the mediating role of gender role conflict and the moderating role of loneliness.Methods:Totally 510 lesbians were selected and assessed with the Internalized Homophobia Scale(IHS),Gender Role Con-flict Scale-Short Form(GRCS-SF),Self-Concept Clarity Scale(SCCS),and UCLA Loneliness Scale(UCLALS).Model 4 in the PROCESS macro program was used to test the mediating role of gender roles,and Model 14 was used to test the moderating role of loneliness.Results:The IHS scores were negatively correlated with the SCCS scores(r=-0.11,P＜0.05).Gender role conflict fully mediated the relationship between lesbian internalized ho-mophobia and self-concept clarity(indirect effect=-0.09,95％CI=-0.13--0.05).Loneliness moderated the relationship between gender role conflict and self-concept clarity among lesbians(Bsimple=-0.17,-0.30,P＜0.01).Conclusion:It suggests that the internalized homophobia may be related to self-concept clarity in lesbi-ans,with gender role conflict as a mediator and loneliness as a moderator moderates between their relationships.