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.

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 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 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 explore the role of indole-3-propionic acid(IPA)in the pathogenesis of metabolic associated fatty liver disease(MAFLD)induced by high-fat diet(HFD)in order to reveal the role and related mechanism of adipose tissue metabolism in the process.Methods A mouse model of MAFLD was induced by HFD.Male C57BL/6J mice(6～7 weeks old)were randomly divided into control group(CON),HFD group,and HFD+IPA intervention group(HFD+IPA).The CON group was fed with control diet,and the HFD group and HFD+IPA group were fed with 60％of high-fat diet.The experiment period was 12 weeks,and IPA was administered at 20 mg/(kg·d)for 6 weeks starting from the 7th week.The body weight and food intake of each group were monitored weekly.After the intervention,the body composition of mice was detected by animal body composition analyzer.After the mice were euthanized,the morphological and structural changes in the liver and adipose tissues were observed by HE staining,the indicators relevant to lipid metabolism in the serum,l iver and adipose tissues were detected by automatic blood biochemical analyzer and biochemical kits,and the mRNA expression changes of lipid metabolism and inflammation related genes were detected by qRT-PCR.Results Compared with the CON group,the HFD group had significantly increased body weight and body fat percentage,obvious lipid deposition in the liver,obviously elevated serum alanine aminotransferase,aspartate aminotransferase,liver triglyceride and total cholesterol levels(P＜0.05),and raised mRNA levels of liver fatty acid transporter CD36(P＜0.05),while IPA intervention significantly reversed the above changes(P＜0.05).IPA intervention significantly inhibited the HFD-induced enlargement of visceral and brown fat cells,reduced the content of visceral adipose tissue(VAT)and serum level of free fatty acids(P＜0.05),and increased the mRNA expression levels of VAT lipolysis(HSL,CGI58),browning genes(Cidea,ND5,UCP1,Prdm16)(P＜0.05),as well as those of brown adipose tissue(BAT)lipolysis(HSL,ATGL)and fatty acid beta oxidation(Cpt1a,PPARα)genes(P＜0.05).Meanwhile,the mRNA levels of TNF-α,IL-1β,CXCL1 and CCL2 in VAT and BAT were decreased after IPA intervention(P＜0.05).Conclusion IPA can improve the occurrence of MAFLD induced by HFD,and its mechanism may be closely associated with its regulation of BAT and VAT morphology,and the mRNA expression of metabolic function and inflammation related genes.

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.

Objective To explore the role of transcription factor EB(TFEB)induced by aerobic exer-cise in improving insulin resistance(IR)of skeletal muscles in high-fat diet mice.Method Eighteen male SPF C57BL/6 mice aged 6 weeks were randomly divided into a common diet group(CON),a high-fat diet group(HFD),and a high-fat diet exercise group(HFDE),each of 6.Both high-fat groups were on high-fat diet for 12 weeks.Then the HFDE group underwent daily 60-minute tread-mill exercise with the slope of 0°,at a speed of 12 m/min,5 times per week for 12 weeks.Finally,the glucose tolerance and insulin sensitivity were detected using the intraperitoneal injection glucose tol-erance test(IPGTT)and intraperitoneal injection insulin tolerance test(IPITT).The fasting blood glu-cose and insulin contents were measured by biochemical method,and the IR level was calculated by using the homeostatic model assessment of insulin resistance(HOMA-IR).Moreover,Western blotting was employed to detect the expression of phosphorylated transcription factor EB(pTFEB)and TFEB in skeletal muscle,glucose transporter 4(GLUT4)in cytoplasm and membrane,phosphorylated insulin re-ceptor substrates1(pIRS1),phosphorylated protein kinase B(pAKT),phosphoinositide 3-kinase(PI3K),and phosphorylated akt substrate of 160 kD(pTBC1D4)in skeletal muscle.The correlation of pTFEB and TFEB to insulin signaling pathway-related proteins was analyzed.Results(1)Compared with the CON group,there was a significant increase in the average body weight,IPGTT,blood glucose at each time point of IPGTT,area under curve(AUC),serum insulin,HOMA-IR,protein expressions of pTFEB,total-TFEB and GLUT4 in cytoplasm of the HFD group(P<0.01),but a significant decrease in the average protein expressions of pIRS1,pAKT,PI3K,pTBC1D4 and GLUT4 in cell membrane of skeletal muscles(P<0.01).However,no significant differences were found between the two groups in the average expression of pTFEB and T-TFEB proteins.(2)Compared to the HFD group,a signifi-cant increase was found in the average body weight,blood glucose at each time point of IPGTT,blood glucose of IPITT at 0 min,30 min and 60 min and AUC,serum insulin,HOMA-IR,expres-sions of pTFEB,T-TFEB and GLUT4 in cytoplasm of HFDE group(P<0.05 or P<0.01),with a signifi-cant decrease in the average protein expressions of pIRS1,pAKT,PI3K,pTBC1D4 and GLUT4 in cell membrane and T-TFEB in nucleus(P<0.01).(3)TFEB was negatively correlated with the expres-sion of pIRS1,PI3K,pAKT,and pTBC1D4 proteins(r=-0.8642,r=-0.7789,r=-0.8946,r=-0.8040)but positively correlated with cytoplasmic GLUT4(r=0.8532,P<0.01).Moreover,TFEB in the nucleus was of positive correlation with GLUT4 in the cell membrane(r=0.7744,P<0.01).Conclusions High-fat diet can decrease the expression of insulin signaling pathway related proteins in skeletal mus-cles of mice and weaken their insulin action,leading to the disorder of glucose and lipid metabolism.However,aerobic exercise can significantly increase the expression of such proteins to promote insulin function and sensitivity,and relieve disorders in glucose and lipid metabolism.This effect may be achieved through the promotion of skeletal muscle TFEB nuclear translocation,which activates IRS1/PI3K/AKT/TBC1D4/GLUT4 signaling pathway and facilitates GLUT4 membrane translocation,ultimately enhancing glucose uptake in skeletal muscle cells.The authors speculate that the TFEB-mediated insu-lin signaling pathway may be an important molecular pathway for aerobic exercise to improve insulin re-sistance.