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.

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.

Aim To investigate the effect of benzyl iso-thiocyanate (BITC) on the proliferation of mouse U14 cervical cancer cells and to explore the mechanism of cytotoxicity based on transcriptomic data analysis. Methods The effect of BITC on U14 cell activity was detected by MTT, nuclear morphological changes were observed by Hochest 33258 and fluorescent inverted microscope, cell cycle and apoptosis were determined by flow cytometry, and the transcriptome database of U14 cells before and after BITC (20 μmol · L

Acute cerebral infarction(ACI)has the characteristics of onset nasty and high mortality,and thus the rapid determination of the occurrence and development of ACI plays a key role in the diagnosis,treatment and prognosis of ACI patients.It has shown that the serum level of human haptoglobin(Hp)is related to ACI.In this study,surface enhanced Raman scattering(SERS)combined with immune recognition was applied to establish a quantitative analysis method for serum Hp.Firstly,the SERS substrate of silver nanoparticles was prepared on silicon wafer,and 4-mercaptobenzoic Acid(MBA)was used as a Raman probe by forming Ag—S bond and connecting it on the surface of nanoparticles.The carboxyl group of MBA was linked to amino group of self-made high-affinity antibody through forming CO—NH structure thus forming a SERS self-assembled chip of Hp(Ag/MBA/anti-Hp).Hp in serum could be specifically captured by antibodies on SERS substrate,which caused the shift of SERS characteristic peak of MBA.The results showed that there was a good linear relationship between the logarithm of Hp concentration and the SERS characteristic peak shift of MBA.The detection range was 1-1000 ng/mL(R2=0.988).The Hp concentrations in serum of 90 ACI patients were determined by this method,and the results were consistent with those of ELISA method,which proved the practicability and accuracy of this method.This method was highly specific,simple and convenient,which could realize the specific recognition and quantitative analysis of serum Hp,so as to be an effective means for clinical detection of serum Hp,thus providing a reference for the treatment and prognosis of ACI.

ObjectiveTo investigate the possible mechanism of Buzhong Yulin Decoction （补中愈淋汤） in the prevention and treatment of recurrent urinary tract infection. MethodsThe mouse models of recurrent urinary tract infection were established by uropathogenic Escherichia coli （UPEC） strain UTI89 by bladder perfusion， and the successful mouse models were randomly divided into a model group， an antibiotic group， and a low- and high-dose Buzhong Yulin Decoction group， with six mice in each group. In addition， 5 C57BL/6 mice without modelling were taken as blank group. The low- and high-dose Buzhong Yulin Decoction groups received 0.1 ml/10 g of decoction by gavage， with concentrations of 1.3 g/ml and 5.2 g/ml， respectively； the antibiotic group received 0.1 ml/10 g of levofloxacin hydrochloride solution with 5 mg/ml by gavage； the blank and model groups received 0.1 ml/10 g of distilled water by gavage. Each group was gavaged once a day for 7 consecutive days. The total number of urine marks， the number of central urine marks， and the total urine volume of the urine marks were observed by the urine marking test； HE staining was used to observe the histopathological changes in the bladder of mice； serum levels of the inflammatory factors interleukin 1β （IL-1β）， interleukin 6 （IL-6） and tumour necrosis factor α （TNF-α） were detected by ELISA； the morphology of the epithelial cells of bladder was observed by scanning electron microscopy； immunofluorescence assay to detect bladder tissue anti-UroPlakin 3A protein level and UPEC bacterial load； the spread plate method to detect urinary bacterial load and bacterial load of bladder epithelial cells； RT-PCR method to detect Ras-related protein Rab-11A （RAB11A） and Ras-related protein Rab-27B （RAB27B） mRNA level in bladder tissue； immunoblotting to detect microtubule-associated protein 1 light chain3 （LC3） and P62 protein levels in bladder tissue. ResultsCompared with the blank group， the bladder epithelial cell layers were lost and showed abnormal morphology in mice of the model group； bladder tissue UroPlakin 3A protein and RAB11A and RAB27B mRNA levels reduced， the total number of urine marks， the number of central urine marks， bladder tissue UPEC bacterial load， urinary bacterial load， bacterial load in bladder epithelial cells， serum IL-1β， IL-6， and TNF-α levels， and LC3 and P62 protein levels in bladder tissue all elevated （P＜0.05 or P＜0.01）. Compared with the model group， the bladder epithelial cell layers were intact and the morphology of epithelial cells were regular in the low- and high-dose Buzhong Yulin Decoction groups； the average surface area of bladder epithelial cells reduced， the levels of UroPlakin 3A protein and RAB11A and RAB27B mRNA in bladder tissues elevated， and total number of urine marks， the number of central urine marks， bladder tissue UPEC bacterial load， urinary bacterial load， bacterial load in bladder epithelial cells， serum IL-1β， IL-6， and TNF-α levels， and P62 protein levels in bladder tissue all reduced （P＜0.05 or P＜0.01）， but LC3 protein levels showed no statistically significant （P＞0.05）. In the antibiotic group， the bladder epithelial cells were partially missing and the morphology of epithelial cells was abnormal. Compared with the antibiotic group， the average surface area of the bladder epithelial cells in the mice increased in the low- and high-dose Buzhong Yulin Decoction groups， the bacterial load of the bladder epithelial cells decreased， and the P62 protein level of the bladder tissue decreased （P＜0.05）. When comparing between the low- and high-dose Buzhong Yulin Decoction groups， the differences in each index were not statistically significant （P＞0.05）. ConclusionBuzhong Yulin Decoction may prevent and treat recurrent urinary tract infection by repairing the bladder mucosal barrier， increasing RAB11A and RAB27B level and enhancing autophagy in bladder tissues， thereby facilitating bacterial clearance from bladder epithelial cells and reducing the bacterial load of bladder epithelial cells.

CD200 and its receptor CD200R constitute an endogenous inhibitory signal. The binding of CD200 and CD200R can regulate the immune response to pathogenic stimuli, which has received much attention in recent years. It has been found that CD200-CD200R is involved in the regulation of many kinds of pathological inflammation, including autoimmune diseases, cardiac cerebrovascular disease, infection and tumor. This paper reviews the protein structure, distribution, expression, biological function of CD200-CD200R and the correlation with diseases, and analyses the current status and development ideas of CD200-CD200R as drug targets. It aims to provide theoretical support for new drug research and development based on this target.

Three-dimensional ordered porous carbon materials exhibit potential application prospects as excellent drug supports in drug delivery systems due to their high specific surface area, tunable pore structure, and excellent biocompatibility. In this study, three-dimensional ordered porous carbon materials were prepared using Acanthopanax senticosus herbal residues as raw material and KOH as activating agent through a one-step pyrolysis method. The prepared carbon-based material was systematically characterized by powder X-ray diffraction, scanning electron microscopy, N₂ adsorption-desorption and Fourier-transform infrared spectroscopy. The results show that the three-dimensional ordered porous carbon materials prepared with KOH as the activator via pyrolysis possess abundant functional groups, high porosity, and high specific surface area, with a specific surface area of 1 471.6 m²·g^-1. The three-dimensional ordered porous carbon materials prepared at 800 ℃ exhibits a high drug loading capacity (78.0%) and drug release rate (86.8%) for 5-fluorouracil. Three-dimensional orderly porous carbon materials show significant application advantages in drug construction, and their high specific surface area and adjustable pore size structure significantly improve the drug load rate and drug release rate, providing a solid foundation for the development of efficient and accurate drug delivery system.