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.

ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

Objective:To explore the effect of different treatment methods on the survival time of patients with ovarian metastasis of digestive tract malignant tumors.Methods:A retrospective analysis was conducted on the clinical data of 159 patients with gastrointestinal malignant tumors and ovarian metastases admitted to Hunan Cancer Hospital from 2008 to 2018. They were divided into 5 groups according to different treatment methods. Group A (94 cases): Total resection of primary and metastatic lesions+ chemotherapy; Group B (13 cases): Primary lesion resection+ chemotherapy; Group C (12 cases): Only metastatic lesions were removed; Group D (17 cases): Resection of metastatic lesions+ chemotherapy; Group E (23 cases): Both the primary and metastatic lesions were not removed, and chemotherapy was used alone. A survival curve was plotted using R language and Kaplan Meier method, and the impact of different treatment methods on patient survival time was analyzed.Results:Among the 159 patients, the average survival time of patients in groups A, B, C, D, and E was 26.6, 23, 17.3, 20.3, and 16 months, respectively. Compared with groups E, C+ D, and B, the survival time of the group A was extended by 10.6( P=0.018), 9.3( P=0.013), and 3.3( P=0.003 1)months, respectively, while there was no statistically significant difference between the other groups (all P>0.05). Conclusions:Patients who undergo surgery to simultaneously remove both primary and metastatic lesions and receive chemotherapy have significantly longer survival times, while patients who only remove metastatic lesions and receive chemotherapy have slightly longer survival times. Not undergoing surgery or only removing metastatic lesions or primary lesions does not help prolong patient survival times.

Objective:To evaluate the impact of self-crosslinked hyaluronic acid (SCH) gel on endometrium recovery after artificial abortion.Methods:A multicenter, prospective randomized controlled trial was conducted across 18 hospitals from December 2021 to February 2023, involving 382 women who underwent artificial abortion. Participants were randomly allocated to receive either treatment with SCH gel (SCH group) or no treatment (control group) in a 1∶1 ratio. The primary outcome was endometrium thickness in 14 to 18 days after the first postoperative menstruation. Secondary outcomes included changes in menstrual volume during the first postoperative menstruation, menstruation resumption within 6 postoperative weeks, time to menstruation resumption, duration of the first postoperative menstruation, and incidence of dysmenorrhea.Results:Baseline characteristics of participants were comparable between the two groups (all P>0.05), with 95.3% (182/191) in SCH group and 92.7% (177/191) in the control group completed the study. The postoperative endometrial thickness in SCH group was significantly greater than that in the control group [(9.78±3.15) vs (8.95±2.32) mm; P=0.005]. SCH group also had significantly fewer participants with reduced menstrual volume [23 cases (12.6%, 23/182) vs 31 cases (17.5%, 31/177); P=0.038]. Although SCH group experienced less dysmenorrhea during the first postoperative menstrual period, this difference was not statistically significant [28.5% (51/179) vs 37.1% (65/175); P=0.083]. Outcomes were similar between SCH group and the control group regarding the proportion of participants who resumed menstruation within 6 weeks postoperatively, time to menstruation resumption, and duration of the first postoperative menstruation ( P=0.792, 0.485, and 0.254, respectively). No serious adverse events were observed during the study period, and no adverse events were attributed to SCH gel treatment. Conclusion:The application of SCH gel after artificial abortion is safe and might aid in the recovery of the endometrium.

Objective:To analyze human factors in radiotherapy safety incidents and identify their correction for the purpose of mining the latent incident chains.Methods:A total of 60 radiotherapy safety incidents were included in the Radiation Oncology Incident Learning System (ROILS) for cause identification and frequency statistics using the Human Factors Analysis and Classification System (HFACS). Latent class analysis (LCA) was performed for the result to correlate the incident causes.Results:Incidents in the protocol design stage were the most common, accounting for 35%. Adverse organizational climate, inadequate supervision, and personnel factors were the primary causes of incidents at each level of the HFACS, accounting for 4.66%, 15.68%, and 16.20%, respectively. Three latent incident chains were identified through LCA, comprising two originating from organizational climate issues and one from organizational process issues, which were passed down via various human factors or " loopholes"Conclusions:HFACS assists in tracing the human factors at all levels that lead to radiotherapy safety incidents. The high-frequency causes and three latent chains of radiotherapy incidents found in this study can provide a guide for the development of targeted safety and defense measures.