Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Knee osteoarthritis (KOA) is a prevalent degenerative joint disease characterized by synovial inflammation, cartilage loss. Often manifesting as joint pain and limited mobility, it severely affects the quality of life of patients. Traditional treatment methods such as pharmacological injections and surgical interventions primarily aim to alleviate symptoms but have limited effects on cartilage repair. Human umbilical cord mesenchymal stem cells (hUC-MSCs), due to their anti-inflammatory and chondrogenic capabilities, is considered a new hope for the treatment of KOA. This article synthesizes the latest research findings from both domestic and international sources to discuss the theoretical basis for the clinical application of hUC-MSCs in treating KOA, clinical study design, and efficacy evaluation. It also addresses the challenges in the clinical application of hUC-MSCs and explores future directions, in the hope of providing feasible theoretical support for the treatment of KOA with hUC-MSCs.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: To investigate the expression and functional role of FK506 binding protein 10 (FKBP10) in oral squamous cell carcinoma (OSCC), and to provide a research basis for the estimated prognosis and targeted therapy of OSCC. Methods: A total of 284 OSCC samples and 19 normal samples were selected from the Cancer Genome Atlas (TCGA) database, and diagnostic analysis was performed to determine mRNA expression. Survival analysis for FKBP10 and OSCC was conducted on a gene expression profile interaction analysis website. Real-time fluorescence quantitative PCR and Western Blot were used to detect the mRNA and protein expression of FKBP10 in four OSCC cell lines and SAS and SCC9 cells transfected with siRNA. The cell proliferation ability of FKBP10-silenced cells was detected using the CCK8 method, and the cell cycle distribution and apoptosis were detected by flow cytometry. Cell migration and invasion ability were detected through wound healing and invasion experiments. The expression changes of total protein and phosphatidylinositol 3-kinase (PI3K)-serine/threonine kinase (AKT) after FKBP10 silencing were analyzed by proteomics and Western Blot. Results: According to the analysis of gene expression levels, the mRNA expression level of FKBP10 in OSCC was significantly higher than that in normal tissues (P < 0.001). In terms of diagnosis, the expression level of FKBP10 has unique diagnostic value for OSCC (P < 0.05). The survival analysis of FKBP10 and OSCC showed that a high expression of FKBP10 led to a decrease in patient survival and poor prognosis (P < 0.05). The expression of FKBP10 mRNA and protein in OSCC cell lines was higher than that in normal oral keratinocytes (P < 0.001). Silencing FKBP10 can reduce the proliferation, invasion, and migration ability of SAS and SCC9 (P < 0.001), and also block their cell cycle in the G0/G1 phase (P < 0.001), with a significant increase in apoptosis (P < 0.05). Protein mass spectrometry and Western blot analysis revealed that FKBP10 silencing significantly downregulated the expression of multiple proteins in the RAP1 signaling pathway, mainly RAP guanine nucleotide exchange factor 1 (RAPGEF1) (P < 0.05) and the phosphorylation of PI3K-AKT proteins (P < 0.05). Conclusion FKBP10 is highly expressed in OSCC, leading to poor prognosis for patients. Downregulated FKBP10 expression can inhibit the proliferation, migration, and invasion ability of OSCC cells, hinder cell cycle progression, and promote apoptosis via the RAP1-PI3K-AKT axis. FKBP10 is a potential therapeutic target and prognostic biomarker for OSCC.

Objective:To determine the content of the released nickel ion through the 7 extraction media to extract the Ni-Ti wires and to plot the curve of the released nickel ion so as to identify a leaching medium that can be substituted for blood for in vitro Ni release evaluation. Methods:The release of Ni through microwave digestion/inductively coupled plasma mass spectrometry (ICP-MS) in the goat serum was determined. Because of the high content of Ni release, it could be determined by diluting the extraction medium, and other extraction media could be determined directly. Ni release standard curves were plotted by the release amount and different time point variables. Though the different extraction media Ni release curves confirm the specificity of extraction media instead of blood.Results:By analyzing the Ni release curves of seven leaching media, it was found that none of these seven extraction media was suitable for the evaluation of Ni release in in vitro leaching media. Considering the safety of the leaching medium and the simplicity of preparation, hydrochloric acid solution was chosen as the leaching medium, but the concentration needed to be diluted accordingly. Finally, a hydrochloric acid solution was created as an alternative to blood for the in vitro study of Ni release from Ni-Ti alloy cardiovascular products, with a volume fraction of 0.005%. Conclusions:The in vitro leaching medium that can replace blood was found to be hydrochloric acid for the time being, but its concentration was too high, resulting in too much Ni release as well, which deviated from the actual situation. Therefore, the hydrochloric acid solution was diluted step by step, and the Ni release curve was examined until it was close to the clinical release level, and the actual concentration was determined, thus laying a solid foundation for the subsequent evaluation of the safety and risk.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Objective This study aims to develop and validate a dynamic web-based nomogram for predicting kinetophobia in patients following percutaneous coronary intervention(PCI).Methods A prospective design was employed to selectively enroll 330 PCI patients admitted to a hospital in Hangzhou from December 2022 to July 2023.Single-factor analysis and Lasso regression were utilized to identify independent risk factors for kinesophobia post-PCI.Logistic regression was performed using R software,and a nomogram was constructed.The model was assessed through the area under the receiver operating characteristic curve(AUC)and Hosmer-Lemeshow tests.Results There were 206 cases of kinesiophobia in 330 patients after PCI,and the incidence was 62.4％.Logistic regression analysis identified combined heart failure,emergency surgery,NYHA cardiac function grade,ADL level,sedentary behavior,Chinese version of PROMIS Physical Function Summary Table score,and Chinese version of Perceptive Social Support Scale score as independent influencing factors for kinesophobia after PCI(P＜0.05).The AUC value of the model was 0.821,with a sensitivity of 70.4％and specificity of 82.0％.The Hosmer-Lemeshow fit test yielded a non-significant result(x2=9.350,P=0.314).Calibration and decision curves demonstrated the model's favorable calibration and clinical practicability.The C-index of the nomogram prediction model was 0.778,0.774,and 0.800,respectively,by 5-fold cross-validation,10-fold cross-validation,and the Bootstrap method.Conclusion The dynamic nomogram model developed in this study effectively predicts kinesophobia in patients after PCI.It provides valuable references and support for clinical staff in early identification of high-risk patients,enabling the formulation of individualized health education strategies and exercise rehabilitation plans.