Objective To investigate the mechanism by which cancer-associated fibroblast(CAF)in the tumor microenvironment regulate key pathways in prostate cancer stem cells(PCSCs).Methods An in vitro co-culture system of CAF and PCSC was established to observe the effects of CAF on PCSC proliferation and sphere formation.Prostate cancer stem cells were treated with CAF conditioned medium pre-treated with Wnt inhibitor DKK-1 and SDF-1 neutralizing anti-body(MAB310).Western blot was used to detect the expression of β-catenin,CXCR4,CD133 and CD44 in PCSCs.And PCR was used to detect the expression of β-catenin,CXCR4,TCF,and LEF mRNA.TOPflash/FOPflash dual-luciferase reporter assays were conducted to detect the effects of SDF-1 on Wnt/β-catenin signaling activity in PCSCs.Results ELISA results showed that the secretion of Wnt3a and SDF-1 in CAF supernatant was significantly higher than that in WPMY-1 cells(P＜0.05).The A value of PCSCs co-cultured with CAF at a 1∶6 ratio was significantly higher than that of the PCSC-only group(P＜0.0 1),and CAF promoted sphere formation in PCSCs(P＜0.05).Western blot results showed that CAF-CM significantly increased the relative expression of β-catenin,CXCR4,CD133 and CD44 in PCSCs(P＜0.01).Compared to CAF-CM,CAFanti-Wnt-CM significantly reduced the expression of β-catenin,CD133 and CD44(P＜0.01).CAFanti-SDF-1-CM also significantly inhibited the expression of CXCR4,β-catenin,CD133 and CD44(P＜0.01).PCR results showed that CAFanti-SDF-1-CM inhibited the expression of β-catenin,CXCR4 and downstream Wnt signaling effectors TCF and LEF(P＜0.01).Dual-luciferase reporter assay results showed that luciferase activity in the CAFanti-SDF-1-CM group was significantly lower than that in the CAF-CM group(P＜0.05).Conclusion CAF reg-ulates the stemness of PCSCs through the Wnt/β-catenin and SDF-1/CXCR4 pathways.CXCR4 may enhance the mainte-nance of stemness by activating β-catenin.

Objective To investigate the effects of lncRNA/Hedgehog signaling pathway-mediated autophagy on chondrocyte function in osteoarthritis(OA).Methods Established an LPS-induced inflammatory chondrocyte model in OA chondrocytes(SW1353),and identified it through collagen Ⅱ immunofluorescence staining and toluidine blue staining,dividing the groups into Normal,LPS,LPS/lncRNA HHIP-AS1 inhibitor,and LPS/Scr groups.RT-qPCR was used to detect lncRNA HHIP-AS1 and HHIP expression in chondrocytes,Western blot was used to assess HHIP,Gli1,Gli2,LC3B-Ⅰ/Ⅱ,and p62 protein expression,TUNEL staining and flow cytometry(FC)were used to detect cell apoptosis,and immunofluorescence assay(IFA)was used to detect autophagy LC3B expression.Results When SW1 cells were treated with LPS,compared with normal chondrocytes,after LPS induction,the volume of chondrocytes increased,the number of vacuoli in the cytoplasm increased,the volume of the nucleus increased,the morphology of some cells was irregular,and the number relatively decreased.Toluidine blue staining and type Ⅱ collagen immunohistochemical staining decreased.LPS stimulation would induce cell death and autophagy.lncRNA HIP-AS1 and HHIP were upregulated(P<0.05),the key molecules of the Hedgehog signaling pathway HHIP,Gli1 and Gli2 were continuously upregulated(P<0.05),chondrocytes treated with LPS showed obvious apoptosis(P<0.05),and LC3B(green)accumulated.The biosynthesis and processing of LC3B increased(the levels of LC3B Ⅰ and Ⅱ increased),the degradation of p62 increased(P<0.05),and the lncRNA HIP-AS1 inhibitor reduced LPS-induced apoptosis of OA chondrocytes(decreased apoptosis rate)and autophagy(decreased autophagy rate of chondrocytes treated with LPS).The biosynthesis and processing of LC3B decreased(the levels of LC3B Ⅰ and Ⅱ decreased),and the degradation of p62 weakened),and the difference was statistically significant(P<0.05).Conclusion The lncRNA HHIP-AS1 may inhibit LPS-induced OA chondrocyte apoptosis and autophagy by regulating the Hedgehog signaling pathway.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective To investigate the mechanism by which cancer-associated fibroblast(CAF)in the tumor microenvironment regulate key pathways in prostate cancer stem cells(PCSCs).Methods An in vitro co-culture system of CAF and PCSC was established to observe the effects of CAF on PCSC proliferation and sphere formation.Prostate cancer stem cells were treated with CAF conditioned medium pre-treated with Wnt inhibitor DKK-1 and SDF-1 neutralizing anti-body(MAB310).Western blot was used to detect the expression of β-catenin,CXCR4,CD133 and CD44 in PCSCs.And PCR was used to detect the expression of β-catenin,CXCR4,TCF,and LEF mRNA.TOPflash/FOPflash dual-luciferase reporter assays were conducted to detect the effects of SDF-1 on Wnt/β-catenin signaling activity in PCSCs.Results ELISA results showed that the secretion of Wnt3a and SDF-1 in CAF supernatant was significantly higher than that in WPMY-1 cells(P＜0.05).The A value of PCSCs co-cultured with CAF at a 1∶6 ratio was significantly higher than that of the PCSC-only group(P＜0.0 1),and CAF promoted sphere formation in PCSCs(P＜0.05).Western blot results showed that CAF-CM significantly increased the relative expression of β-catenin,CXCR4,CD133 and CD44 in PCSCs(P＜0.01).Compared to CAF-CM,CAFanti-Wnt-CM significantly reduced the expression of β-catenin,CD133 and CD44(P＜0.01).CAFanti-SDF-1-CM also significantly inhibited the expression of CXCR4,β-catenin,CD133 and CD44(P＜0.01).PCR results showed that CAFanti-SDF-1-CM inhibited the expression of β-catenin,CXCR4 and downstream Wnt signaling effectors TCF and LEF(P＜0.01).Dual-luciferase reporter assay results showed that luciferase activity in the CAFanti-SDF-1-CM group was significantly lower than that in the CAF-CM group(P＜0.05).Conclusion CAF reg-ulates the stemness of PCSCs through the Wnt/β-catenin and SDF-1/CXCR4 pathways.CXCR4 may enhance the mainte-nance of stemness by activating β-catenin.

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 diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

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 investigate the role of miRNAs in maternal amniotic fluid exosomes in development of isolated ventriculomegaly(VM)in fetuses.Methods Amniotic fluid samples were collected from 9 cases of moderate isolated VM and 8 normal control cases to extract exosomal miRNA,and miRNA sequencing technique was used to identify differentially expressed miRNAs between the two groups.Three miRNAs with significant differential expression between the two groups,whose high expression was associated with VM,were selected for verification with RT-qPCR.Dual luciferase reporter assays were used to verify the regulatory effect of miR-122-5p on its predicted target genes AKT3 and CCDC88C.Gene ontology(GO)and KEGG pathway analyses were performed to explore the possible roles of the top 40 significant differential miRNAs in the pathophysiology of VM.Results We identified a total of 272 differentially expressed miRNAs in VM cases,including 43 up-regulated and 229 down-regulated miRNAs.The target genes of these differential miRNAs were associated with DNA and transcription factor binding,transmembrane transporter and nucleic acid binding transcription factor activity,and cell developmental process.These miRNAs were mostly enriched in the MAPK,cGMP-PKG and Wnt signaling pathways.Verification with RT-qPCR showed that miR-122-5p expression level was significantly lower in VM group than in the control group(P<0.05),which was consistent with miRNA sequencing results;let-7b-5p expression level was significantly lower in VM group,which was contrary to miRNA sequencing result.Dual luciferase reporter assays showed that miR-122-5p was not capable of regulating AKT3 or CCDC88C expressions.Conclusions The highly abundant differentially expressed miRNAs in maternal amniotic fluid exosomes play important roles in the occurrence of fetal VM possibly by regulating the MAPK,PI3K-Akt,Wnt and cGMP-PKG signaling pathways.

ObjectiveTo identify the rate， population characteristics， and vaccination history of repeat infections among previously infected people in the current epidemic based on the rate of repeat infection and population characteristics of different mutant strains at different times in Pudong New Area of Shanghai， and to provide reference for the prevention and control strategies of novel coronavirus repeat infections. MethodsA total of 9 250 investigated subjects were randomly selected from the new cases of asymptomatic infection and confirmed cases reported by Pudong New Area from March to May 2022. The investigation mainly focused on demographic characteristics， nucleic acid or antigen test results， and symptoms after infection. The repeat infection rates among different populations were compared， and logistic regression was used to analyze the impact of gender， age， and vaccination status on repeat infections. ResultsThe survey sample of 9 250 people had a response rate of 81.85%. There were 4 043 males （53.40%） and 3 528 females （46.60%）， with a median age of 34 years old （P₂₅， P₇₅： 7， 61）. The overall vaccine uptake rate was 59.44% （4 500/7 571）. In December of 2022， there were 563 cases of repeat infection， with an infection rate of 7.44%. The lowest rate of repeat infection was seen in the 3‒ year-old group （2.86%） and the highest rate in the 30‒ year-old group （12.42%）， with significant differences between different age groups. The repeated infection rate for those who had completed their vaccinations was significantly lower （6.57%） compared to those who had not （7.11%）. The age groups of 3‒ years， 70‒79 years， as well as individuals who completed full vaccination and received booster shots were protective factors against repeat infections. ConclusionThe overall rate of reinfection among the infected in Shanghai during the spring of 2022 was low in the outbreak of the Omicron variant， and the rate of reinfection in the 3‒ year-old group was significantly lower than in other age groups. Completing the full course of vaccination significantly reduces the risk of reinfection. Although the reinfection rate is high in individuals who received booster shots， it remains a mitigating factor compared to those who do not receive the vaccine. It is recommended to continue monitoring reinfections in key populations and further strengthen immunization efforts.

A combined epidemic prediction method based on multi-source data fusion is presented to address the common problems of low accuracy,weak generalization,single structure,poor nonlinear processing ability,and long prediction time in traditional epidemic prediction models.The collected multi-source epidemic data are normalized and subjected to feature selection using principal component analysis.An ARIMA-GM-BPNN model for pandemic prediction is constructed by combining ARIMA model,grey GM model and BPNN.The fitting values of the first two prediction models are used as inputs to BPNN for model training.After sufficiently integrating the data and combining the advantages of different prediction models,the optimal combined model is obtained and used for forecasting the incidence and trend of epidemics.Experimental results show that the combined model exhibits excellent fitting performance,with predicted incidences and trends consistent with the real conditions.The proposed approach improves prediction accuracy and generalization capabilities,and it can provide reliable data support for epidemic prediction and control.