Objective:To construct a prognostic model of hepatocellular carcinoma (HCC) with differential expression of autophagy genes.Method:Autophagy genes expression data of HCC and normal liver tissues were obtained from The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) database respectively. The gene expression data from different platforms is normalized into log 2(FPKM value + 1). Differentially expressed autophagy-related genes of HCC were identified by using R program limma package from the TCGA-GTEx combined data set, the criteria of |logFC| > 1 and FDR < 0.05 was deemed to be of statistically significance. The Gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by using R program clusterProfiler package, as criteria of P<0.05. Univariate and multivariate Cox proportional hazards regression analyses were performed by using R program survival package to identify the HCC potential prognostic differentially expressed autophagy-related genes. Furthermore, the statistically significant ( P<0.05) autophagy genes in the univariate Cox regression analysis were included in the multivariate Cox regression analysis, and the expression of each differentially expressed autophagy gene and the corresponding regression coefficient coef value based on this, the autophagy gene prognosis model of HCC was constructed: expmRNA1×βmRNA1+ expmRNA2×βmRNA2+ …+ expmRNAn×βmRNAn (exp: gene expression level; β: regression coefficient coef of multivariate Cox regression analysis). Draw the receiver operating characteristic (ROC) curve of the predictive model and calculate the area under curve (AUC) to evaluate the predictive value of the model. Results:The genes expression data and clinical information of 374 HCC samples and 160 normal liver tissue samples were obtained from TCGA and GTEx databases. Total 205 autophagy genes expression data was obtained from the TCGA-GTEx combined sequence. Among them, SPNS1, DIRAS3, TMEM74, NRG2, NRG1, IRGM, IKBKE, NKX2-3, BIRC5, CDKN2A, TP73 are differentially expressed autophagy genes that meet the screening criteria. GO analysis mainly enriched in "regulation of protein serine/threonine kinase activity" , "ErbB 2 signaling pathway" , "protein kinase regulator activity" and "kinase regulator activity" ; KEGG analysis enriched frequently in "EGFR tyrosine kinase inhibitor resistance" , "Hippo signaling pathway" . After integrating and deleting samples with missing survival information, a total of 418 sample expressions were included in the Cox regression analysis. After univariate and multivariate Cox risk regression analysis, the two autophagy genes NRG1 ( HR=1.5565, 95% CI: 1.1793-2.0543) and IKBKE ( HR=1.7502, 95% CI: 1.2093-2.5330) were screened out and a prognostic prediction model was established: (0.44247 × NRG1 expression level) + (0.55977 × IKBKE expression level). The ROC of the prognosis model shows that the AUC of the overall seven-year survival is 0.711. Conclusion:The prognosis model of HCC based on NRG1 and IKBKE has high predictive value for the long-term survival rate of hepatocellular carcinoma patients.

Objective To explore the effects of exercise on the levels of neurotrophin-3 (NT-3) in the skeletal muscles of streptozotocin-induced diabetic rats. Methods The rats were divided into an 8-week exercise group (A), a 4-week exercise group (B), a diabetes control group (C), an exercise group (D) in which no dia-betes was induced, and a control group (E). The rat model of diabetes was induced in the rats of groups A, B and C by intra-abdominal injection of streptozotocin (STZ) at 55 mg/kg. The exercising rats were forced to swim for 60 minutes once daily, 5 days a week. The levels of NT-3 in skeletal muscles were measured with an enzyme-linked immunosorbent assay. Cadual nerve conduction velocity (CNCV) in all of the rats was evaluated at the beginning, and after 4 and 8 weeks of swimming exercise. Results NT-3 levels in the skeletal muscles in group C were sig-nificantly lower than in groups A, D and E. There was no statistically significant difference in NT-3 levels between groups B and C. The NT-3 levels showed a significant positive correlation with CNCV at the 8th week. Conclu-sions The increase in NT-3 levels of skeletal muscles induced by exercise could contribute partially to the im-provement of diabetic neuropathy.

Objective:To identify dermatopathological features of patients with dermatomyositis (DM) and analyze its correlation with cutaneous diseases activity.Methods:The clinical data and skin biopsies of 48 patients were collected. The relevance was analyzed using Spearman's correlation analysis. The two groups were compared using Chi-square test or Fisher's exact test. Multi-factors line regression model was established to analyze the relationship between cutaneous disease activity and dermatopathological features.Results:The most common dermatopathological feature was perivascular inflammation (37 cases, 88%), followed by epidermal atrophy (22 cases, 52%) and melanocyte loss (20 cases, 48%), basal vacuolization (15 cases, 36%). The incidence of basal vacuolization ( χ2=9.110, P=0.022), interface dermatitis ( χ2=11.672, P=0.005) and mucin deposition ( χ2=7.795, P=0.029) were significantly different in patients with myositis specific antibody (MSA) subgroup. The patients with positive tranional intermediary factor-1 (anti-TIF1-γ) antibody had higher incidence of interface dermatitis and basal vacuolization, and patients with melanoma differentiation-associated gene 5 (anti-MDA5) antibody had lower incidence of interface dermatitis. Interface dermatitis was positively associated with epidermal atrophy ( r=0.371, P=0.016) and parakeratosis ( r=0.316, P=0.041). Pigment inco-ntinence was positively associated with basal vacuolization ( r=0.384, P=0.012). Multi-factor line regression showed interface dermatitis was positively related to cutaneous disease area and severity index (CDASI). Conclusion:The dermatopathological features is different in subgroup of patients with DM ( β=10.295, P=0.004). Interface dermatitis is a marker of cutaneous disease activity, and its pathogenesis may be different from that of perivascular inflammation. Keratinocytes may be involved in the pathological process in interface dermatitis.

Objective To study the correlations between tumor mutation burden (TMB) and the prognosis of hepatocellular carcinoma (HCC) patients,and to investigate the effect of TMB on differential expression genes of HCC and the proportion of invasive immune cells in tumor tissues.Methods The somatic variation data,gene transcriptional expression data and clinical information of HCC patients were obtained from the cancer genome atlas (TCGA) database.The R program language (version 3.6.1)maftools function package was used to analyze the gene mutation data characteristics of the samples.The TMB value of each sample was calculated using the full-exon sequencing data of patients with hepatocellular carcinoma on the VarScan2 platform,sorted by TMB value,and the median value was used to divide all samples into high TMB and low TMB groups.Kaplan-Meier method was used to draw the survival curves of two groups of patients and log-rank test was performed to determine the correlation between tumor mutation load and prognosis.The Limma function package of R language was used to screen the differentially expressed genes between the two groups (FDR =0.05 and logFC =1),and the clusterProfiler function package of R language was used to perform gene ontology (GO) enrichment analysis of the differential genes and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis (screening criteria were all P ＜ 0.05).Then the CIBERSORT tool was used to compare and analyze the difference in the proportion of invasive immune cells between the two groups.Results A total of 364 patients with HCC from TCGA database were included in the study.Mutations were found in 327 (84％) samples,and there was a synergistic correlation between OBSCN and FLG mutations (P ＜ 0.05),while mutations in CTNNB1 and AXIN1 are mutually exdusive (P ＜ 0.05).A total of 363 patients were included in the TMB survival analysis,sorted by the size of TMB value.All samples were divided into high TMB group (182 cases) and low TMB group (181 cases) with the median value.We found that TMB had no significant effect on the prognosis of HCC patients (P ＞ 0.05).A total of 198 with differentially expressed genes (28 up-regulated genes and 170 down-regulated genes) were screened between the high TMB group and the low TMB group.In GO analysis,it was found that the differentially expressed genes were mainly enriched in extracellular matrix tissues,extracellular structural tissues,extracellular matrix,extracellular matrix containing collagen,extracellular matrix structural components and other functions.In KEGG analysis,differential genes were highly enriched in extracellular matrix receptor interaction pathway and adhesive plaque pathway.In the correlation analysis of the proportion of infiltrating immune cells,CD4 + memory T cells were more infiltrating in the low TMB group (P ＜ 0.05).Monocytes showed a higher degree of infiltration in the high TMB group (P ＜ 0.05).Conclusion There was no correlation between TMB and the prognosis of HCC patients.TMB has significant influence on the differential expression genes of HCC and the proportion of invasive immune cells in tumor tissues.

Objective To observe the clinical efficacy and safety of apatinib in the treatment of advanced malignant tumors and to analyze the prognostic indicators affecting the survival of patients.Methods A total of 100 patients with advanced malignant tumors who were treated with apatinib at Jinan Central Hospital Affiliated to Shandong University from February 2015 to July 2018 were enrolled and their data were analyzed retrospectively.The clinical efficacy was evaluated and the related adverse reactions were recorded.Single and multiple factor analyses were pefformed by Cox regression model.The predictive factors of progression-free survival (PFS) and overall survival (OS) were analyzed.Results One-hundred patients with advanced malignant tumors who were treated with second-line and above treatment were collected.All patients were assessable for response,no complete response was observed,22 patients (22％) achieved partial remission,58 patients (58％) in stable disease,and 20 patients (20％) appeared progressive disease.The objective response rate was 22％ (22/100),the disease control rate was 80％ (80/100),the median PFS was 3.6 months (95％ CI:2.7-4.5 months),and the median OS was 7.0 months (95％ CI:4.7-9.3 months).Univariate analysis showed that Eastern Cooperative Oncology Group (ECOG) score (HR =0.340,95％ CI:0.211-0.546,P ＜0.001),tumor primary site (HR =1.757,95％ CI:1.053-2.932,P =0.031),neutrophil to lymphocyte ratio (NLR) (HR =0.389,95％ CI:0.227-0.666,P =0.001),hemoglobin (HR =1.696,95％ CI:1.023-2.813,P =0.041) and proteinuria (HR =1.790,95％ CI:1.105-3.155,P =0.044) were related to PFS;age (HR =2.082,95 ％ CI:1.320-3.285,P =0.002),ECOG score (HR =0.206,95％ CI:0.123-0.344,P＜0.001),tumor primary site (HR=1.784,95％CI:1.077-2.954,P=0.025),NLR (HR=0.410,95％CI:0.238-0.704,P =0.001),hemoglobin (HR =1.958,95％ CI:1.175-3.264,P =0.010) and albumin (HR =0.467,95％ CI:0.277-0.787,P =0.004) were related with OS.Multivariate analysis showed that PFS was related to ECOG score (HR =0.254,95％ CI:0.123-0.523,P ＜ 0.001) and NLR (HR =0.378,95％CI:0.161-0.888,P =0.026),and OS was related to ECOG score (HR =0.147,95％ CI:0.067-0.326,P ＜0.001),NLR (HR =0.327,95％ CI:0.140-0.765,P =0.010) and hemoglobin (HR =1.975,95％ CI:1.101-3.543,P =0.022).In term of safety,the most common adverse events among 100 cases of treated patients with advanced malignant tumors were hypertension (53,53 ％),anorexia (51,51％),fatigue (51,51％) and anemia (50,50％),among which the most common ones of grade 3 and 4 were hypertension (10,10％),thrombocytopenia (8,8％),leukopenia (7,7％) and hand-foot syndrome (6,6％).Conclusion Apatinib has certain clinical efficacy and manageable adverse events in the treatment of advanced malignant tumors at and above second-line treatment.ECOG score and NLR are independent predictors of PFS and OS in patients with advanced malignant tumors treated with apatinib.

Objective:To study the relative proportion of tumorinfiltrating immune cells (TIICs) in colorectal adenocarcinoma (CRC), and to explore the correlation between TIICs and CRC in prognosis and clinical staging.Methods:CRC gene transcriptional expression data and clinical information were obtained from TCGA database. The CIBERSORT software was used to calculate the relative proportions of 22 TIICs in each sample. R software was used to compare the proportion of TIICs between CRC and normal tissues. Single factor survival analysis was performed for each TIICs. Finally, the correlation between each TIICs and CRC clinical stage was studied.Results:A total of 514 gene transcriptional expression data and clinical information were obtained from TCGA database, including 473 CRC and 41normal adjacent tissues.The relative proportions of 22 TIICs in each sample were calculated using the CIBERSORT software "deconvolution method" . In the study, 12 TIICs including naive B cells were found to have statistically significant differences between CRC and normal tissues (all P<0.05). After matching the clinical information of the samples, a total of 222 cases were included in the survival analysis.The relative proportion of each TIICs was arranged in descending order, and all samples were divided into high and low infiltration groups according to the median value. Then, univariate survival analysis was performed for each TIICs, and it was found that memory B cells had a statistically significant effect on the prognosis of CRC ( P<0.05). It was found that the proportion of four types of TIICs, including activated CD 4 memory T cells, in different CRC clinical staging was statistically differe (all P<0.05). Conclusion:TIICs is related to the prognosis and clinical stage of CRC.

Objective To explore whether inhibiting autophagy can enhance the sensitivity of photothermal treatment under mild photothermal conditions. Methods CQ@PLGA@PDA NPs were prepared by an improved double emulsification method and a PDA-based surface modification method. After basic characterization, CCK-8 method was used to detect the cytotoxicity of nanoparticles; the near-infrared laser irradiation nanoparticle solution was used to detect the heating effect; CCK-8 method and live-dead cell staining were used to detect the killing effect of tumor cells; Western blot was used to detect the expression of autophagy-related proteins. Results The CQ@PLGA@PDA NPs were successfully prepared, with a particle size of 253.10±2.39 nm, a zeta potential of -22.57±0.80 mV, uniform particle size and good dispersion. The temperature of nanoparticle solution increased to 45℃ after the near-infrared laser irradiation for 10 min. CQ@PLGA@PDA NPs had no obvious toxicity to cells. The survival rates of breast cancer cell MDA-MB-231 and mouse embryonic fibroblast NIH-3T3 cell were above 95%. The inhibition of autophagy under mild photothermal conditions could improve the sensitivity of photothermal therapy. Conclusion The prepared CQ@PLGA@PDA NPs have good photothermal performance and high biological safety; by inhibiting autophagy, they can effectively kill tumor cells under mild photothermal conditions(< 50℃).

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.