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.

By analyzing the current application of multi-modal data in the diagnosis of gastric "inflammation-cancer" transformation， this study explored the feasibility and strategies for constructing a disease-syndrome integrated diagnosis and treatment system. Based on traditional Chinese medicine （TCM） phenomics， we proposed utilizing multi-modal data from literature research， cross-sectional studies， and cohort follow-ups， combined with artificial intelligence technology， to establish a multi-dimensional diagnostic and treatment index system. This approach aims to uncover the complex pathogenesis and transformation patterns of gastric "inflammation-cancer" progression. Additionally， by dynamically collecting TCM four-diagnostic information and modern medical diagnostic information through a long-term follow-up system， we developed three major modules including information extraction， multi-modal phenotypic modeling， and information output， to make it enable real-world clinical data-driven long-term follow-up and treatment of chronic atrophic gastritis. This system can provide technical support for clinical diagnosis， treatment evaluation， and research， while also offering insights and methods for intelligent TCM diagnosis.

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 analyze the trajectories of body mass index for age z-score (BAZ) and its influencing factors among children with congenital hypothyroidism (CH) based on latent class growth modeling (LCGM), so as to provide the evidence for improving treatment measures and optimizing growth management among children with CH. Methods Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Methods: Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Results: A total of 299 children with CH were included. There were 140 boys (46.82%) and 159 girls (53.18%). The median of BAZ was 0.50 (interquartile range, 1.68). The LCGM analysis categorized the subjects into three groups: the persistent high-growth pattern group with 24 cases (8.03%), the slow-growth pattern group with 39 cases (13.04%), and the appropriate-growth pattern group with 236 cases (78.93%). Multinomial logistic regression analysis showed that compared to the children with CH in the appropriate-growth pattern group, those who started treatment at the age of 30 to 60 days (OR=0.109, 95%CI: 0.016-0.732; OR=0.166, 95%CI: 0.032-0.852) had a lower risk of persistent high-growth and slow-growth patterns; CH children with TSH levels of 50 to 150 mIU/L at diagnosis (OR=3.554, 95%CI: 1.201-10.514) and those whose paternal had a senior high school/technical secondary school education (OR=2.975, 95%CI: 1.003-8.823) exhibited a higher risk of the persistent high-growth pattern. Conversely, CH children whose paternal reproductive age was 30 to 35 years (OR=0.166, 95%CI: 0.034-0.806) had a lower risk of the persistent high-growth pattern. Conclusions The BAZ trajectory of children with CH aged 0 to 3 years exhibited three patterns: persistent high-growth, slow-growth, and appropriate-growth. The persistent high-growth and slow-growth patterns were associated with treatment timing, TSH levels at diagnosis, paternal reproductive age, and paternal education level. It is recommended to strengthen early treatment interventions and provide family follow-up guidance.

Background::Although overnight fasting is recommended prior to endoscopic retrograde cholangiopancreatography (ERCP), the benefits and safety of high-carbohydrate fluid diet (CFD) intake 2 h before ERCP remain unclear. This study aimed to analyze whether high-CFD intake 2 h before ERCP can be safe and accelerate patients’ recovery.Methods::This prospective, multicenter, randomized controlled trial involved 15 tertiary ERCP centers. A total of 1330 patients were randomized into CFD group ( n = 665) and fasting group ( n = 665). The CFD group received 400 mL of maltodextrin orally 2 h before ERCP, while the control group abstained from food/water overnight (>6 h) before ERCP. All ERCP procedures were performed using deep sedation with intravenous propofol. The investigators were blinded but not the patients. The primary outcomes included postoperative fatigue and abdominal pain score, and the secondary outcomes included complications and changes in metabolic indicators. The outcomes were analyzed according to a modified intention-to-treat principle. Results::The post-ERCP fatigue scores were significantly lower at 4 h (4.1 ± 2.6 vs. 4.8 ± 2.8, t = 4.23, P <0.001) and 20 h (2.4 ± 2.1 vs. 3.4 ± 2.4, t= 7.94, P <0.001) in the CFD group, with least-squares mean differences of 0.48 (95% confidence interval [CI]: 0.26–0.71, P <0.001) and 0.76 (95% CI: 0.57–0.95, P <0.001), respectively. The 4-h pain scores (2.1 ± 1.7 vs. 2.2 ± 1.7, t = 2.60, P = 0.009, with a least-squares mean difference of 0.21 [95% CI: 0.05–0.37]) and positive urine ketone levels (7.7% [39/509] vs. 15.4% [82/533], χ2 = 15.13, P <0.001) were lower in the CFD group. The CFD group had significantly less cholangitis (2.1% [13/634] vs. 4.0% [26/658], χ2 = 3.99, P = 0.046) but not pancreatitis (5.5% [35/634] vs. 6.5% [43/658], χ2 = 0.59, P = 0.444). Subgroup analysis revealed that CFD reduced the incidence of complications in patients with native papilla (odds ratio [OR]: 0.61, 95% CI: 0.39–0.95, P = 0.028) in the multivariable models. Conclusion::Ingesting 400 mL of CFD 2 h before ERCP is safe, with a reduction in post-ERCP fatigue, abdominal pain, and cholangitis during recovery.Trail Registration::ClinicalTrials.gov, No. NCT03075280.

Aim To investigate the effect and role of neuronal restriction silencing factor(REST/NRSF)in epilepsy disorder.Methods Immunohistochemistry,immunofluorescence,Western blot and qPCR tech-niques were used to detect REST/NRSF expression levels in hippocampal tissues of mice induced by kainic acid and human brain tissue.Viral injections,EEG re-cordings and behavioral methods were used to test the effects on epileptic mice after knockdown and overex-pression of REST/NRSF in the hippocampal CA1 re-gion,respectively.Results The positive rate of REST/NRSF in the lesions of epileptic patients was significantly higher compared with that in the control group.The levels of REST/NRSF protein and mRNA in the CA1 region of the hippocampus of mice in the KA model group were significantly higher.Kv7.2 and Kv7.3 potassium channel mRNA expression levels were significantly down-regulated.Significant up-regu-lation of REST/NRSF expression levels was observed in mouse hippocampus after NMDA injection.Knock-down of REST/NRSF in the CA1 region of hippocam-pus significantly elevated the expression levels of Kv7.2 and Kv7.3 potassium channel mRNAs.The fre-quency of EEG spiking and sharp-wave issuance and epileptic seizure grade were significantly lower.Over-expression of REST/NRSF in the CA1 region of hippo-campus significantly reduced the mRNA expression lev-els of Kv7.2 and Kv7.3 potassium channels.The fre-quency of EEG spiking and sharp-wave issuance was significantly higher and epileptic symptoms were exac-erbated.Conclusion REST/NRSF in mouse hipp-ocampal brain regions is involved in epileptic disease development through transcriptional regulation of Kv7.2 and Kv7.3 potassium channels.

Aim To explore the core target,key com-ponents and mechanism of Tibetan medicine Rhodiola crenulata in improving cerebral microcirculation based on literature research,network pharmacology,molecu-lar docking and experimental verification.Methods The chemical components of Rhodiola were collected through literature and database,and the potential tar-gets of Rhodiola crenulata were predicted by reverse pharmacophore matching.The related targets of cere-bral microcirculation disorder were obtained and targets were mapping with Rhodiola crenulata.PPI network was constructed and the core targets were screened.The regulatory network of"herb-component-target-dis-ease"was constructed and key components were screened.GO and KEGG enrichment analysis were conducted,and a"Core target-Pathway-Biological Process"network was constructed.Finally,molecular docking validation was carried out,and RT-qPCR and Western blot were used for animal experiments to fur-ther confirm the results of network pharmacology analy-sis.Results A total of 76 active components of Rhodiola crenulata were obtained and corresponding to 285 targets.Altogether 1074 related targets related to cerebral microcirculation disorder were obtained.A-mong them,there were 97 common targets and the main core targets were 6.The key components were 6.The results of molecular docking showed that the bind-ing activity of three key components to the core target was greater than that of the core target protein and its original ligand.The result of RT-qPCR and Western blot demonstrated that Tibetan medicine Rhodiola cre-nulata could significantly reduce the expression of core target CASP3 and AKT1(P＜0.01).Conclusions Tibetan medicine Rhodiola crenulata can improve the cerebral microcirculation disorder through multi compo-nents,multi targets and multi pathways.This study provides an experimental basis for clinical application of Tibetan medicine Rhodiola crenulata to treat cerebral microcirculation disorder.