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.

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 explore the effect of scutellarin on lipopolysaccharide(LPS)induced neuroinflammation in BV-2 microglia cells.Methods BV-2 microglia were cultured and randomly divided into 6 groups:control group(Ctrl),cyclic GMP-AMP synthetase(cGAS)inhibitor RU320521 group(RU.521 group),LPS group,LPS+RU.521 group,LPS+scutellarin pretreatment group(LPS+S)and LPS+S+RU.521 group.The expressions of cGAS,stimulator of interferon gene(STING),nuclear factor kappa B(NF-κB),phosphorylated NF-κB(p-NF-κB),neuroinflammatory factors PYD domains-containing protein 3(NLRP3)and tumor necrosis factor α(TNF-α)in BV-2 microglia were detected by Western blotting and immunofluorescent double staining(n= 3).Results Western blotting and immunofluorescent double staining showed that compared with the control group,the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in BV-2 microglia increased significantly after LPS induction(P<0.05),while the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in LPS+S group were significantly lower than those in LPS group(P<0.05).Treatment with cGAS pathway inhibitor RU.521 showed similar effects as the pre-treatment group with scutellarin.In addition,the change of NF-κB in each group was not statistically significant(P>0.05).Conclusion Scutellarin inhibits the neuroinflammation mediated by BV-2 microglia cells,which may be related to cGAS-STING signaling pathway.

Traumatic fractures and stress fractures are common orthopedic diseases,and there is great potential in researching bone turnover,repair,and promotion of fracture healing.Basic medical experiments often use animal models of long bone fractures in limbs to study the mechanisms of various interventions on fracture healing.Fracture healing is a complex process influenced by multiple factors and involves multiple molecules and pathways.Therefore,to explore the mechanisms more deeply,accelerate the translation of results,and improve the clinical efficacy,it is particularly important to choose the appropriate animal fracture modeling methods in experimental research.Based on this,this paper conducts a literature review of animal species and modeling methods commonly used for long bone fracture models in experimental research.It summarizes five methods:bone defect method,physical impact method,mechanical bending method,open osteotomy method,and drilling method.A side-by-side comparison of their advantages,disadvantages,and scope of application is made,aiming to provide suitable fracture models for studyingthe mechanisms of fracture healing interventions.

BACKGROUND:Early identification of patients requiring ventilator support will be beneficial for the outcomes of botulism.The present study aimed to establish a new scoring system to predict mechanical ventilation(MV)for botulism patients. METHODS:A single-center retrospective study was conducted to identify risk factors associated with MV in botulism patients from 2007 to 2022.Univariate analysis and multivariate logistic regression analysis were used to screen out risk factors for constructing a prognostic scoring system.The area under the receiver operating characteristic(ROC)curve was calculated. RESULTS:A total of 153 patients with botulism(66 males and 87 females,with an average age of 43 years)were included.Of these,49 patients(32.0%)required MV,including 21(13.7%)with invasive ventilation and 28(18.3%)with non-invasive ventilation.Multivariate analysis revealed that botulinum toxin type,pneumonia,incubation period,degree of hypoxia,and severity of muscle involvement were independent risk factors for MV.These risk factors were incorporated into a multivariate logistic regression analysis to establish a prognostic scoring system.Each risk factor was scored by allocating a weight based on its regression coefficient and rounded to whole numbers for practical utilization([botulinum toxin type A:1],[pneumonia:2],[incubation period≤1 day:2],[hypoxia<90%:2],[severity of muscle involvement:grade II,3;grade III,7;grade IV,11]).The scoring system achieved an area under the ROC curve of 0.82(95%CI 0.75-0.89,P<0.001).At the optimal threshold of 9,the scoring system achieved a sensitivity of 83.7%and a specificity of 70.2%. CONCLUSION:Our study identified botulinum toxin type,pneumonia,incubation period,degree of hypoxia,and severity of muscle involvement as independent risk factors for MV in botulism patients.A score≥9 in our scoring system is associated with a higher likelihood of requiring MV in botulism patients.This scoring system needs to be validated externally before it can be applied in clinical settings.

Objective:To investigate the effect of stress-induced protein Sestrin2 (SESN2) on necroptosis of mouse dendritic cell (DC) induced by lipopolysaccharide (LPS) combined with zVAD, a panaspartate-specific cysteine protease (caspase) inhibitor.Methods:The DC2.4 cell line derived from the bone marrow of mouse in the 3rd to 10th generations was cultured. The cells were stimulated with LPS for 0 hour, 6 hours, 12 hours, and 24 hours, and grouped according to the stimulation time points. Western blotting was performed to determine the protein expression of SESN2 in each group. Overexpression empty lentivirus (NC), SESN2 gene overexpression RNA sequence lentivirus (SESN2 LV-RNA), small interfering empty lentivirus (NS), and SESN2 gene small interfering RNA sequence lentivirus (SESN2 siRNA) were transfected into DC2.4 cells. After 72 hours of transfection, cell fluorescence expression was observed under the inverted fluorescence microscope. Cells in each transfection group were stimulated with LPS for 24 hours. The blank control groups were set up and cultured with phosphate buffered saline (PBS) for 24 hours. Western blotting was performed to measure SESN2 protein expression. In the same groups as above, cells were stimulated with LPS+zVAD for 24 hours. The blank control groups were set up and cultured with PBS for 24 hours. Western blotting was used to determine the expression of mixed lineage kinase domain-like protein (MLKL) and phosphorylated-MLKL (p-MLKL). The p-MLKL levels and the number of positive cells were observed using laser scanning confocal microscopy. The necroptotic cell ratios were assessed by both flow cytometry and Hoechst staining.Results:Compared to the LPS 0 hour group, the expression of SESN2 in the LPS 24 hours group showed a significant increase. Therefore, 24 hours was chosen as the subsequent stimulation time point. After successful lentivirus transduction and 24 hours of cultivation, the MLKL phosphorylation level in the SESN2 siRNA+LPS+zVAD group was significantly higher than that in the NS+LPS+zVAD group. The MLKL phosphorylation in the SESN2 LV-RNA+LPS+zVAD group was significantly lower than that in the NC+LPS+zVAD group. The MLKL phosphorylation levels in both the NS+LPS+zVAD group and the NC+LPS+zVAD group were obviously higher than those in the NS+PBS group and the NC+PBS group, respectively. Laser scanning confocal microscopy showed that the trends in quantity and fluorescence intensity of p-MLKL protein expressions were consistent with the above results. The results from flow cytometry analysis and Hoechst staining showed that the rates of cell necrotic apoptosis in SESN2 siRNA+LPS+zVAD group were significantly higher than those in NS+LPS+zVAD group [flow cytometry analysis: (30.800±1.153)% vs. (20.800±1.114)%, Hoechst staining: (75.267±0.451)% vs. (46.267±3.371)%, both P < 0.05], indicating that knocking down SESN2 further exacerbated the occurrence of necroptosis. The necrotic apoptosis rates in SESN2 LV-RNA+LPS+zVAD group were significantly lower than those in NC+LPS+zVAD group [flow cytometry analysis: (7.160±0.669)% vs. (19.240±2.322)%, Hoechst staining: (32.433±3.113)% vs. (48.567±4.128)%, both P < 0.05], indicating that overexpressing SESN2 reversed such response and markedly reduced the proportion of necroptotic cells compared to the corresponding empty vector group. Conclusion:SESN2 exhibits an inhibitory effect on necroptosis of DC in sepsis. Targeted SESN2 expression may regulate the process of DC-mediated immune response in sepsis.