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 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 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 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 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 explore the association between postoperative radiotherapy for bladder cancer and the risk of second primary rectal cancer.Methods:Eligible 75 120 patients with bladder cancer from the Surveillance, Epidemiology, and End Result database (SEER) of the National Cancer Institute (NCI) (1975-2017) were enrolled in this study. The second primary cancers referred to rectal cancers patients suffered after more than five years post-treatment for bladder cancer, and the cumulative incidence was estimated using Fine-Gray competing risk regression. The relative risk (RR) of rectal cancer in patients treated with or without radiotherapy (the RT group or the NRT group) was evaluated using Poisson regression.Results:Among the 75 120 patients, 70 045 (92.4%) were Caucasian, with a median age of 65.8 years (54-74 years). A total of 2 236 (3%) received postoperative radiotherapy, while 72 884 (97%) received surgery alone. The 30-year follow-up revealed a cumulative incidence of rectal cancer of 0.93% in the RT group and 0.43% in the NRT group ( P = 0.004). The competing risk regression analysis identified a significant association between radiotherapy and rectal cancer ( HR: 1.86; 95% CI 1.26-2.74, P < 0.009). Furthermore, the RR of radiotherapy-associated rectal cancer significantly increased as the diagnosis occurred earlier (1975-1985 vs. 1985-1994: RR 2.59; 95% CI 1.20-4.86, P < 0.001), and a lower age at the time of radiotherapy was associated with a higher probability of second primary tumors (≤50-year old vs. > 50 year old : RR 7.89, 95% CI 2.97-21.30, P < 0.001). As calculated using the Poisson distribution, the RR of second rectal tumors was higher in the RT group ( RR: 2.20, 95% CI 1.45-3.18, P < 0.001), even after adjusting the date of diagnosis ( RR: 1.77, 95% CI 1.17-2.57, P = 0.009). Conclusions:An increased risk of rectal cancer following bladder cancer radiotherapy necessitates aggressive follow-ups for the purpose of early detecting second primary rectal cancer associated with bladder cancer radiotherapy.

Objective To systematically explore the traditional Chinese medicine(TCM)common symptoms,syndrome elements,clinical syndrome differentiation,and medication rules of coronary microvascular disease(CMVD),and to provide a reference for quantitative criteria of clinical differentiation of CMVD,specification of the diagnosis and efficacy evaluation of TCM clinical syndrome,and guidance of clinical medication.Methods The databases including CNKI,Wanfang,VIP,and SinoMed were searched for research papers on the treatment of CMVD by TCM published from database inception to May 16,2023.Relevant information of the included literature was extracted and the database was established.Then,the frequency statistics of symptoms,syndrome elements,syndrome types and Chinese medicinals were carried out.Latent structural models were constructed using Latern 5.0 and Rstudio softwares respectively for comprehensive clustering and association rule analysis,so as to explore the symptom characteristics,syndrome elements distribution,common syndromes and medication rules for TCM treatment of CMVD.Results A total of 107 literature were included,involving 36 syndromes,17 syndrome elements,121 symptoms and 143 Chinese medicinals.It was speculated that the main syndrome element of CMVD was blood stasis,followed by qi deficiency,qi stagnation,phlegm turbidity,yin deficiency and yang deficiency.The main type of syndrome was qi deficiency and blood stasis,followed by heart blood stasis obstruction,qi stagnation and blood stasis,phlegm blended with stasis,qi-yin deficiency,etc..The main medicinals were Chuanxiong Rhizoma,Salviae Miltiorrhizae Radix et Rhizoma,Angelica Sinensis Radix and Astragali Radix.The medicinals used in the treatment of CMVD were classified as blood-activating and stasis-resolving drugs,deficiency-tonifying drugs,qi-regulating drugs in terms of their efficacy.Conclusion The location of CMVD is in heart,and related to liver and kidney.The syndrome of CMVD is deficiency in origin and excess in superficiality.Blood stasis runs through the development of the disease.The treatment is mainly to activate blood circulation and remove stasis,activate meridians and relieve pain,which should be supplemented with the therapies of tonifying and invigorating qi,soothing the liver and regulating qi,dispelling phlegm and dissipating masses according to the patients'syndromes.

Objective To observe the value of nomogram based on enhanced cortical phase CT Radscore combined with CT features for predicting synchronous distant metastasis(SDM)of renal cell carcinoma(RCC).Methods Totally 139 RCC patients from center A were retrospectively enrolled and divided into SDM group(n=46)and non-SDM group(n=93),also classified as training set(n=97)and test set(n=42)at a ratio of 7∶3.Additionally,20 RCC patients from center B were included as validation set(8 cases with SDM and 12 cases without SDM).Radiomics features were extracted and screened based on enhanced cortical phase CT images to calculate Radscore.Multivariate logistic regression analysis was performed to identify independent predictors of RCC SDM among clinical and CT features.Then a logistic regression model was constructed combining Radscore and independent predictors of RCC SDM and visualized as a nomogram.The receiver operating characteristic curve and the area under the curve(AUC)was used to assess the efficacy of the nomogram for predicting RCC SDM.Results The maximum tumor diameter,CT-T stage and perirenal adipose stranding were all independent predictors of RCC SDM(all P＜0.01).Radscore was calculated based on 5 optimal features.The nomogram was constructed based on perirenal adipose stranding,CT-T stage and Radscore.AUC of the model for predicting RCC SDM in training set,test set and validation set was 0.964,0.921 and 0.885,respectively.Conclusion Enhanced cortical phase CT Radscore combined with perirenal adipose stranding and CT-T stage could effectively predict RCC SDM.