Objective To develop and validate a prediction model for risk of amputation in patients with diabetic foot ulcers(DFU)based on systematic review and meta-analysis.Methods The studies on the risk factors of amputation in DFU patients was retrieved by using subject words+free words.After screening,37 cohort studies were finally included,and the Newcastle-Ottawa scale(NOS)was used for quality evaluation.Meta-analysis was performed on the risk factors of amputation in DFU.Then a prediction model for DFU amputation risk were constructed based on the statistically significant risk factors in the meta-analysis.The corresponding β value was calculated based on the combined odds ratio(OR)value of each risk factor,and each risk factor was scored to establish a scoring system model.The clinical data of 453 DFU patients hospitalized in our department from 2021 to 2023 were collected as a validation cohort.Receiver operating characteristic(ROC)curve analysis was used to evaluate the model performance.The area under the curve(AUC)was calculated,and the optimal cutoff score was determined by calculation of the maximum Youden index through sensitivity and specificity.Results Our meta-analysis showed a cumulative amputation rate of approximately 34.65%in 11 779 DFU patients.The final risk prediction models include gangrene[OR=11.92(5.86～24.24)],ulcer depth[OR=4.93(2.52～9.64)],osteomyelitis[OR=3.19(2.36～4.29)],previous amputation history[OR=3.19(2.00～5.09)]and lower extremity arterial disease[OR=3.10(2.31～4.17)].According to the weights of each risk factor,the total score of the model is 76,and the optimal cut-off score is 36.5.The prediction model performed well,with an AUC value of 0.864(0.824,0.903),a sensitivity of 0.743,a specificity of 0.859,and an accuracy rate of 83.00%.Conclusion A prediction model for DFU amputation risk is developed based on risk factor scoring,and has good discrimination and calibration,providing effective scientific basis for clinical research and clinical decision-making related to DFU amputation.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

This study aims to investigate the molecular mechanism of polyphyllin Ⅰ(PPⅠ) inhibiting proliferation of human breast cancer cells. Human breast cancer BT474 and MDA-MB-436 cells were treated with different concentrations of PPⅠ, and then the effect of PPⅠ on cell proliferation was detected by MTT assay, trypan blue dye exclusion assay, real-time cell analysis, and clone forming assay, respectively. The apoptosis was detected by Annexin V-FITC/PI staining and then analyzed by flow cytometry. The change of mitochondrial membrane potential was detected by flow cytometry after fluorescent probe JC-1 staining. Western blot was used to detect protein expression and phosphorylation. Molecular docking was performed to detect the binding between PPⅠ and EGFR. The affinity between PPⅠ and EGFR was determined by drug affinity responsive target stability assay. The results indicated that PPⅠ inhibited the proliferation and colony formation of BT474 and MDA-MB-436 cells in a time-and concentration-dependent manner. The PPⅠ treatment group showed significantly increased apoptosis rate and significantly decreased mitochondrial membrane potential. PPⅠ down-regulated the expression of pro-caspase-3 protein, promoted the cleavage of PARP, and significantly reduced the phosphorylation levels of EGFR, Akt, and ERK. Molecular docking showed that PPⅠ bound to the extracellular domain of EGFR and formed hydrogen bond with Gln366 residue. Drug affinity responsive target stability assay confirmed that PPⅠ significantly prevented pronase from hydrolyzing EGFR, indicating that PPⅠ and EGFR have a direct binding effect. In conclusion, PPⅠ inhibited the proliferation and induced apoptosis of breast cancer cells by targeting EGFR to block its downstream signaling pathway. This study lays a foundation for the further development of PPⅠ-targeted drugs against breast cancer.
Apoptosis ; Breast Neoplasms/genetics* ; Cell Line, Tumor ; Cell Proliferation ; Diosgenin/analogs & derivatives* ; ErbB Receptors ; Female ; Humans ; Molecular Docking Simulation

Objective To improve the reliability and accuracy of WBC counting in cerebrospinal fluid (CSF) ,this article is stud-ying the improved method of WBC counting in CSF by finding out the optimum percentage of CSF specimen with the most suitable concentration of acetic acid .Methods CSF specimen was mixed with different acetic acid at different ratio respectively .WBC counts were performed in 5 minutes on diluted samples of various concentrations .A series of 20 CSF specimens were analyzed via the proposed assay and conventional method .The average value and coefficient of variation (CV) of WBC count of each sample were c compared and analyzed .Results The optimum percentage of CSF sample was obtained at 60∶40 ratio .In this percentage , the maximal WBC count (189/μL) was obtained compared that of conventional method (161/μL) .Moreover ,the CV of the WBC counts in this percentage (7% ) was also lower than that of the conventional method (18% ) .Conclusion The reliability and accur-ancy of WBC counting in CSF was the optimum percentage of CSF specimen and 5% acetic acid was 60 :40 .It may lead to a more reliable ,accurate and standard way of WBC counts in CSF .

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a common condition obsessing urologists and patients. It is also known as a heterogeneous syndrome, with varied etiologies, progression courses and responses to treatment. Based on the deeper insights into its pathogenesis and re-evaluation of its clinical trials, a novel phenotypic classification system UPOINT has been developed, which clinically classifies CP/CPPS patients into six domains: urinary (U), psychosocial (P), organ-specific (O), infection (I) , neurologic/systemic (N) and tenderness of pelvic floor skeletal muscles (T), and directs individualized and multimodal therapeutic approaches to CP/CPPS. This review systematically summarizes the theoretical foundation, clinical characteristics of UPOINT and treatment strategies based on the UPOINT phenotypic classification system.
Chronic Disease ; Humans ; Male ; Pelvic Pain ; classification ; diagnosis ; therapy ; Phenotype ; Prostatitis ; classification ; diagnosis ; therapy ; Severity of Illness Index