[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

ObjectiveTo evaluate the pharmacological effect of Buzhong Yiqitang on brain development in offspring of rats with subclinical hypothyroidism （SCH） during pregnancy and explore its potential mechanism. MethodsForty-eight SPF female SD rats were divided into sham operation group （n=8） and model group （n=40）. The rat model of subclinical hypothyroidism （SCH） was constructed by total thyroidectomy combined with postoperative subcutaneous injection of levothyroxine （L-T₄）. The modeled rats were randomly allocated into model， low-， medium-， and high-dose （5.58， 11.16， 22.32 g∙kg^-1， respectively） Buzhong Yiqitang， and euthyrox （4.5×10^-6 g∙kg^-1） groups， with 8 rats in each group. These rats were co-housed with normal male rats for mating. Drug administration started 2 weeks before pregnancy and continued until delivery. Hematoxylin-eosin staining and Golgi-cox staining were used to observe pathological changes in the hippocampal tissue of offspring rats. Western blot was employed to detect the effects of Buzhong Yiqitang on the protein levels of cytochrome C oxidase subunitⅠ （COX）Ⅰ and COXⅣ in the hippocampal tissue of offspring rats. A colorimetric method was used to measure the mitochondrial adenosine triphosphate （ATP） content in the hippocampal tissue of offspring rats. For in vitro experiments， a hydrogen peroxide （H₂O₂）-induced oxidative damage model was established with rat pheochromocytoma cells （PC12）. Interventions included the DNA methyltransferase inhibitor （SGI-1027）， Buzhong Yiqitang-medicated serum， and euthyrox-medicated serum. The cell counting kit-8 （CCK-8） assay was used to examine the effect of Buzhong Yiqitang on cell proliferation. Immunofluorescence staining was performed to evaluate the effect on tubulin beta 3 class Ⅲ （TUBB3） in PC12 cells. Western blot was employed to assess the effects on the protein levels of DNA methyltransferases （TETs and DNMTs） in PC12 cells. The fluorescent probe 2′，7′-dichlorodihydrofluorescein diacetate （DCFH-DA）， luciferase assay， and JC-1 staining were employed to assess the effects of Buzhong Yiqitang on the levels of reactive oxygen species （ROS） and ATP and the mitochondrial membrane potential in PC12 cells. ResultsCompared with the sham group， the model group showed a reduction in the number of hippocampal neurons， incomplete pyramidal cell bodies， loose arrangement， shortened average dendrite length， decreased dendritic complexity and dendritic spine density， and reduced expression levels of COXⅠ and COXⅣ and content of ATP in the brain tissue （P<0.05， P<0.01）. Compared with the model group， after administration of Buzhong Yiqitang and euthyrox， hippocampal neurons exhibited regular arrangement， complete morphology， extended dendrite， increased dendritic complexity and dendritic spine density， and restored expression levels of COXⅠ and COXⅣ and content of ATP （P<0.05， P<0.01）， with the medium-dose Buzhong Yiqitang group showing the best therapeutic effect. In the PC12 cell model of oxidative damage， Buzhong Yiqitang increased the cell viability （P<0.01）， enhanced neuronal differentiation， down-regulated the expression levels of DNMTs （P<0.05）， up-regulated the expression levels of TETs （P<0.05）， decreased the ROS content （P<0.01）， and restored the ATP content and mitochondrial membrane potential （P<0.01）. ConclusionBuzhong Yiqitang protects brain development in offspring of pregnant rats with SCH. It mainly acts on the oxidative stress and mitochondrial dysfunction resulted from abnormal mtDNA methylation， with DNMTs and TETs as the key proteins for its effects.

BACKGROUND: Biomarkers-based prediction of long-term risk of acute coronary syndrome (ACS) is scarce. We aim to develop a risk score integrating clinical routine information (C) and plasma biomarkers (B) for predicting long-term risk of ACS patients. METHODS: We included 2729 ACS patients from the OCEA (Observation of cardiovascular events in ACS patients). The earlier admitted 1910 patients were enrolled as development cohort; and the subsequently admitted 819 subjects were treated as validation cohort. We investigated 10-year risk of cardiovascular (CV) death, myocardial infarction (MI) and all cause death in these patients. Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was derived using main part of these variables. RESULTS: During 16,110 person-years of follow-up, there were 238 CV death/MI in the development cohort. The 7 most important predictors including in the final model were NT-proBNP, D-dimer, GDF-15, peripheral artery disease (PAD), Fibrinogen, ST-segment elevated MI (STEMI), left ventricular ejection fraction (LVEF), termed as CB-ACS score. C-index of the score for predication of cardiovascular events was 0.79 (95% CI: 0.76-0.82) in development cohort and 0.77 (95% CI: 0.76-0.78) in the validation cohort (5832 person-years of follow-up), which outperformed GRACE 2.0 and ABC-ACS risk score. The CB-ACS score was also well calibrated in development and validation cohort (Greenwood-Nam-D'Agostino: P = 0.70 and P = 0.07, respectively). CONCLUSIONS CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS. This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Objective:To explore the function and mechanism of transcription factor En1 in esophageal squamous cell carcinoma (ESCC).Methods:The correlations of En1 with prognosis were analyzed using the overall survival data of 9 397 pan-cancer patients and progression-free survival data of 4 349 pan-cancer patients from The Cancer Genome Atlas (TCGA) database. The En1 expression data in 53 and 155 cases of ESCC and their paired adjacent tissues were from Gene Expression Omnibus (GEO) database and National Genomics Data Center-Genome Sequence Archive(NGDC-GSA)database. Lentivirus was used to generate En1 stable knockout cell lines KYSE180 and KYSE450. The proliferation ability of the cells was detected by cell counting kit 8 and clone formation assay. The migration ability of the cells was detected by Transwell assay. The effect of En1 on the proliferation of ESCC was detected by xenograft experiment in BALB/c-nu/nu mice. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to detect the expressions of En1, glioma-associated oncogene family zinc finger 1 (GLI1), glioma-associated oncogene family zinc finger 2 (GLI2) and smoothened (SMO).Results:Pan-cancer data from TCGA showed that patients with low En1 expression had longer overall survival and progression-free survival than patients with high En1 expression ( P< 0.001). Data from GEO and GSA databases also showed a high expression level of En1 in ESCC tissues compared with paired tissues ( P＜0.001). Proliferation was inhibited after knockout of En1 in KYSE180 and KYSE450 cells ( P＜0.001). The colony formation numbers decreased. The colony formation numbers of KYSE180 cells in the shEn1#1 group and the shEn1#2 group were 138.33±23.07 and 127.00±19.70, respectively, significantly lower than that of the shNC group 340.67±12.06 ( P<0.001). The colony formation numbers of KYSE450 cells in the shEn1#1 group and the shEn1#2 group were 65.33±2.52 and 9.00±3.00, respectively, significantly lower than that of the shNC group 139.00±13.00 ( P<0.001). The migration numbers was inhibited after knockout of En1 [the Transwell numbers of KYSE180 cells in the shEn1#1 group and the shEn1#2 group were 66.67±12.66 and 71.33±11.02, respectively, significantly lower than that of the shNC group 334.67±16.56 ( P<0.001). The Transwell numbers of KYSE450 cells in the shEn1#1 group and the shEn1#2 group were 112.33±14.57 and 54.33±5.51, respectively, significantly lower than that of the shNC group 253.33±21.03 ( P<0.001)]. Xenograft model showed a slower growth rate of shEn1#1 and shEn1#2 cell lines ( P＜0.001). The tumor weights of KYSE450 cells in the shEn1#1 group and the shEn1#2 group were (0.046±0.026)g and (0.047±0.025)g, respectively, significantly lower than that of the shNC group (0.130±0.038)g ( P＜0.001). After knockdown of En1, the relative expression levels of GLI1 in KYSE180 cells of the shEn1#1 group and the shEn1#2 group were 0.326±0.162 and 0.322±0.133, and the relative expression levels of GLI1 in KYSE450 cells of the shEn1#1 and shEn1#2 groups were 0.131±0.006 and 0.352±0.050, respectively, which were all lower than that in the shNC group ( P＜0.01). After knockdown of En1, overexpression of GLI1 attenuated the inhibitory effect of knockdown of En1 on cell proliferation ( P＜0.001), colony formation[the colony formation numbers of the shEn1#1-GLI1 group were 151.00±9.54, higher than 102.33±10.02 ( P=0.004) of the shEn1#1-vector group] and migration [the migration numbers of the shEn1#1-GLI1 group were 193.67±10.07, higher than 109.33±11.50 ( P<0.001) in the shEn1#1-vector group]. In clinical samples of ESCC, major regulatory factors of the Hedgehog pathway were up-regulated and the pathway was activated. Conclusion:En1 promotes the proliferation and migration of ESCC cells by regulating the Hedgehog pathway and can be used as a new potential target for targeted therapy of ESCC.

Objective To compare the clinical efficacy and pregnancy outcomes of CO2 laser and loop electrosurgical excision procedure(LEEP)surgery in the treatment of cervical high-grade squamous intraepithelial lesion(HSIL)in women of childbearing age.Methods Patients with cervical HSIL of appropriate reproductive age(20-34 years old)who underwent satisfied colposcope,subsequently treated with laser or LEEP between Jan 2019 and Dec 2021 in Obstetrics and Gynecology Hospital,Fudan University were collected.All the patients were interpreted as transformation zone(TZ)type 1/2,and colposcopy biopsy diagnosed as cervical high-grade squamous intraepithelial lesion(HSIL)and received laser or LEEP according to the physician's advice and the patient's wishes.The effectiveness of the two surgeries was evaluated,and postoperative cytology,HPV result,and colposcopic pathology were compared.We monitored the surgical pregnancy rate,pregnancy complications,and pregnancy results after the surgery.Results A total of 566 cases,including 233 laser cases and 333 LEEP cases,were enrolled.There was no significant difference between the two groups in terms of preoperative cytology,HPV distribution,or the length of cervical canal.After 6 months of surgery,91.5%of patients who underwent laser therapy and 86.9%of those who underwent LEEP had normal cytology.The histology cure rate was 91.5%and the HPV conversion rate was 74.6%in both groups.The results of cytology,HPV,and colposcopic pathology had no significant difference between the two groups.The length of postoperative cervical canal was significantly longer in laser group than that in LEEP group[(29.09±3.15)mm vs.(27.05±3.84)mm,P<0.001].There was no statistically significant difference in postoperative pregnancy rate and pregnancy outcome between the two groups.Conclusion Laser ablation and LEEP produce comparable effects pregnancy outcomes in cervical HSIL patients with TZ types 1 and 2,while LEEP has pathologic feedback,so its misdiagnosis of invasive cancer reduce.

Objective To explore the clinical value of vaginoscopy in the diagnosis and treatment of uterine cavity diseases in virgins.Methods We retrospectively reviewed the data of 450 patients who underwent vaginoscopy and traditional hysteroscopy in Obstetrics and Gynecology Hospital,Fudan University from Jan 2020 to Dec 2023,including vaginoscopy group(n=232)and traditional hysteroscopy group(n=218).The average ages of the two groups were 24.9±4.7 years and 25.5±5.4 years,and there was no significant difference between the two groups(P>0.05).The operation time,estimated blood loss,fluid deficit,false passage,surgical failure,incidence of complications and postoperative pain score were compared between vaginoscopy group and traditional hysteroscopy group.Results Compared with the traditional hysteroscopy group,the average operation time in the vaginoscopy group was shorter,the fluid deficit was less,and the VAS pain score was lower,but the rate of surgical failure was higher(7.8%vs.0),all the differences were statistically significant(P<0.05).In terms of complications,the incidence of false passage in the vaginoscopy group was less(0 vs.3.2%)and the rate of hymen injury was lower(0 vs.85.3%),the differences were statistically significant(P<0.05).There was no significant difference in the estimated blood loss between the two groups,and the incidence of postoperative infection was similar in both groups.There were no complications such as uterine perforation and air embolism in both groups.Conclusion Vaginoscopy is safe and effective,more minimally invasive than traditional hysteroscopy,does not damage the hymen,and is suitable for virgins.This technology is worthy of clinical application.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.