Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective To establish an HPLC method to determine the concentration of inositol nicotinate（IN） in rat skin, and study the pharmacokinetic characteristics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats. Methods HPLC method was used to establish a simple and rapid analytical method for the determination of IN concentration in the skin of rats at different time points after administration. The established method was used to study the pharmacokinetics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats, and the pharmacokinetic parameters were fitted with DAS software. Results The linearity of the analytical method was good in the concentration range of 0.25-20 μg/ml, the quantitative limit was 0.25 μg/ml, and the average recovery rate was 96.18%. The pharmacokinetic parameters of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats were as follows: t_1/2 was （4.555±2.054） h, T_max was （6±0）h, C_max was （16.929±2.153）mg/L, AUC_0−t was （150.665±16.568） mg·h /L ,AUC_0−∞ was （161.074±23.917） mg·h /L, MRT_（0−t） was （9.044±0.618）h, MRT_{（0−∞）} was （10.444±1.91） h, CLz/F was （0.19±0.03） L/（h·kg）, and Vz/F was （1.19±0.437） L/（h·kg）. Conclusion IN could quickly penetrate the skin and accumulate in the skin for a long time, which was beneficial to the pharmacological action of drugs on the lesion site for a long time. The method is simple, rapid, specific and reproducible, which could be successfully applied to the pharmacokinetic study of IN after transdermal administration in rats.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Aim To investigate the regulatory mecha-nism of butin on the proliferation,migration,cycle blockage and pyroptosis related inflammatory factors in human fibroblast-like synoviocytes of rheumatoid arthri-tis(HFLS-RA).Methods Cell proliferation,migra-tion and invasion were studied using cell migration and invasion assays.Cell cycle was detected by flow cytom-etry,and the expression of the pyroptosis-associated in-flammatory factors IL-1β,IL-18,caspase-1 and caspase-3 was detected by ELISA,RT-qPCR and West-ern blot.Results Migration and invasion experiments showed that the cell proliferation rate of the butin group was lower than that of the blank control group(P＜0.05).Cell cycle analysis demonstrated that in the G0/G1 phase,the DNA expression was elevated in the medium and high-dose groups of butin(P＜0.05),while in the G2 and S phases,the DNA expression was reduced in the medium and high-dose groups of butin(P＜0.05).The results of ELISA,RT-qPCR and Western blot assay revealed that the expression of IL-1β,IL-1 8,caspase-1,and caspase-3 decreased in the butin group compared with the IL-1β+caspase-3 in-hibitor group(P＜0.05).Conclusions Butin inhib-its HFLS-RA proliferation by inhibiting the synthesis of inflammatory vesicles by caspase-1 in the pyroptosis pathway,thereby reducing the production and release of inflammatory factors such as IL-1β and IL-18 down-stream of the pathway,and also inhibits HFLS-RA pro-liferation by exerting a significant blocking effect in the G1 phase,which may be one of the potential mecha-nisms of butin in the treatment of RA.

Objective:To evaluate the safety and efficiency of robotic visualization system(RVS)-assisted microsurgical re-construction of the reproductive tract in male rats and the satisfaction of the surgeons.Methods:We randomly divided 8 adult male SD rats into an experimental and a control group,the former treated by RVS-assisted microsurgical vasoepididymostomy(VE)or vaso-vasostomy(VV),and the latter by VE or VV under the standard operating microscope(SOM).We compared the operation time,me-chanical patency and anastomosis leakage immediately after surgery,and the surgeons'satisfaction between the two groups.Results:No statistically significant difference was observed the operation time between the experimental and the control groups,and no anasto-mosis leakage occurred after VV in either group.The rate of mechanical patency immediately after surgery was 100％in both groups,and that of anastomosis leakage after VE was 16.7％in the experimental group and 14.3％in the control.Compared with the control group,the experimental group achieved dramatically higher scores on visual comfort(3.00±0.76 vs 4.00±0.53,P＜0.05),neck/back comfort(2.75±1.16 vs 4.38±1.06,P＜0.01)and man-machine interaction(3.88±1.55 va 4.88±0.35,P＜0.05).There were no statistically significant differences in the scores on image definition and operating room suitability between the two groups.Conclusion:RVS can be used in microsurgical reconstruction of the reproductive tract in male rats and,with its advantages over SOM in ergonomic design and image definition,has a potential application value in male reproductive system micosurgery.

Objective:To compare the diagnostic efficacy of 22 G fine needle aspiration (FNA) needles and 22 G end-cutting fine needle biopsy (FNB) needles for solid pancreatic lesion using both cytological and histological examination.Methods:Clinical data of 116 patients who underwent endoscopic ultrasound-guided fine needle aspiration/biopsy (EUS-FNA/FNB) at the Digestive Endoscopy Center of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine from June 2022 to March 2023 were retrospectively analyzed. Sixty-three patients sampled with 22 G FNA needles were the FNA group, and 53 sampled with 22 G FNB needles were the FNB group. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and cytological and histological diagnostic yield of FNA needles and FNB needles for solid pancreatic lesions were compared.Results:There were no significant differences in age, gender, lesion location, lesion size, or the number of passes between the FNA group and the FNB group ( P>0.05). There were no significant differences in the diagnostic accuracy [93.7% (59/63) VS 90.6% (48/53), P=0.730], sensitivity [93.0% (53/57) VS 90.2% (46/51), P=0.732], specificity [100.0% (6/6) VS 100.0% (2/2), P=1.000], positive predictive value [100.0% (53/53) VS 100.0% (46/46), P=1.000] and negative predictive value [60.0% (6/10) VS 28.6% (2/7), P=0.335] of combined cytology and histology in distinguishing benign and malignant lesions between the two groups. In the FNA group, the diagnostic accuracy of combined cytology and histology was higher than cytology alone [93.7% (59/63) VS 81.0% (51/63), P=0.008], and was higher than histology alone without statistical significance [93.7% (59/63) VS 87.3% (55/63), P=0.125]. In the FNB group, the diagnostic accuracy of combined cytology and histology was higher than cytology alone [90.6% (48/53) VS 69.8% (37/53), P=0.001], but not than histology alone [90.6% (48/53) VS 90.6% (48/53)， P=1.000]. For solid masses located in pancreatic body/tail, the diagnostic accuracy for malignancy by histology using FNB needles tended to be higher than that of FNA needles [100.0% (17/17) VS 81.3% (26/32), P=0.080]. Conclusion:Both FNA needles and FNB needles exhibit adequate diagnostic yield for solid pancreatic masses when combining cytology and histology. FNB needles may offer a higher histological diagnostic yield.

Objective:To investigate the effects and mechanism of metformin on the wound healing of full-thickness skin defects in diabetic rats.Methods:This study was an experimental study. Eighteen 8-week-old male Sprague Dawley rats were divided into control group, diabetes group, and diabetes+metformin group according to complete random grouping method, with 6 rats in each group. The latter two groups of rats were used to create diabetic models, and then four circular full-thickness skin defect wounds with a diameter of 5 mm were made on the back of 18 rats. Metformin F-127 hydrogel was applied only to the wounds of rats in diabetes+metformin group. The wound healing status on post injury day (POD) 7 and 13 was observed and the wound healing rate was calculated. The wound tissue on POD 7 and 13 was collected for hematoxylin-eosin staining to measure the length of re-epithelialized epidermis and calculate the change rates in diameters of epidermal and dermal wounds, for immunohistochemical staining to detect the relative expressions of keratin 10 and proliferating cell nuclear antigen (PCNA), and for Western blotting to detect the protein expressions of keratin 10 and PCNA. The sample size in all the above experiments was 8 except that in the last experiment was 3. The correlations between the relative expressions of keratin 10 and PCNA in wound tissue in three groups of rats and their wound healing rates, and the correlation between the relative expressions of keratin 10 and PCNA in wound tissue were analyzed.Results:On POD 7, the wound healing rates of rats in diabetes group and diabetes+metformin group were 81.48% (77.89%, 85.53%) and 93.04% (92.51%, 94.24%), which were significantly lower than 100% (97.17%, 100%) in control group (with Z values of 2.37 and -3.36, respectively, P<0.05); the wound healing rate of rats in diabetes+metformin group was significantly higher than that in diabetes group ( Z=3.45, P<0.05). On POD 13, the wound healing rates of rats in control group and diabetes+metformin group were both 100% (100%, 100%), which were significantly higher than 94.47% (90.68%, 99.82%) in diabetes group (with Z values of 2.90 and -2.90, respectively, P<0.05). On POD 7, the change rates in epidermal wound diameter of rats in control group and diabetes+metformin group were significantly higher than that in diabetes group (with Z values of 3.36 and -2.74, respectively, P<0.05). The change rates in dermal wound diameter of rats in the three groups were similar on POD 7 and 13 ( P>0.05). The lengths of re-epithelialized epidermis of rats in control group and diabetes+metformin group on POD 13 were significantly longer than that in diabetes group (with Z values of 3.34 and -2.64, respectively, P<0.05). The relative expressions of keratin 10 in wound tissue of rats in diabetes group on POD 7 and 13 were significantly higher than those in control group (with Z values of -3.36 and -3.26, respectively, P<0.05) and diabetes+metformin group (with Z values of 3.36 and 3.15, respectively, P<0.05), and the relative expression of keratin 10 in wound tissue of rats in diabetes+metformin group on POD 7 was significantly lower than that in control group ( Z=3.05, P<0.05); the relative expressions of PCNA in wound tissue of rats in diabetes group on POD 7 and 13 were significantly lower than those in control group (with both Z values of 3.36, P<0.05) and diabetes+metformin group (with both Z values of -3.36, P<0.05). The protein expressions of keratin 10 in wound tissue of rats in control group and diabetes+metformin group on POD 7 as well as that in diabetes+metformin group on POD 13 were significantly lower than those in diabetes group ( P<0.05), and the protein expressions of PCNA in wound tissue of rats in control group and diabetes+metformin group on POD 7 were significantly higher than that in diabetes group ( P<0.05). There was a significant positive correlation between the relative expression of keratin 10 in wound tissue and the wound healing rate in control group and diabetes+metformin group of rats (with r values of 0.78 and 0.71, respectively, P<0.05), there was a significant negative correlation between the relative expression of PCNA in wound tissue and the wound healing rate in diabetes+metformin group of rats ( r=-0.60, P<0.05), and there was a significant negative correlation between the relative expressions of PCNA and keratin 10 in wound tissue of rats in diabetes group and diabetes+metformin group (with r values of -0.41 and -0.49, respectively, P<0.05). Conclusions:The diabetic rats with full-thickness skin defect wound exhibit delayed healing, accompanied by up-regulation of keratin 10 and down-regulation of PCNA in keratinocytes in the wound tissue. Metformin can promote wound healing in diabetic rats with full-thickness skin defects by down-regulating keratin 10 expression and up-regulating PCNA expression in keratinocytes in the wound tissue, and the wound healing rate was positively correlated with the expression of keratin 10 and negatively correlated with the expression of PCNA.