AIM:To explore the mechanism of berberine on breast cancer cells based on network pharmacology and in vitro cell experiments.METH-ODS:Firstly,berberine and breast cancer were tak-en as the research objects,the intersection targets of the two were screened by VEEN diagram,GO function and KEGG enrichment analysis were per-formed by R language,and molecular docking and visualization were carried out by Autodock Vina and Pymol software.Then,berberine treated breast cancer MCF-7 cells for 24 h,and then in vi-tro cell experiments were performed.CCK-8 was used to detect cell viability,Edu and plate cloning were used to detect cell proliferation and cloning,and apoptosis was detected by An-nexin V-FITC/PI double staining and Western blot.Laser confocal and CETSA were used to verify the binding effect of berberine and AKT1 protein.RESULTS:The results of network pharmacology showed that berberine had a good binding to the core targets AKT1,AKT2 and MAPK3.Berberine(20,40,80 μmol/L)signifi-cantly inhibited the proliferation and cloning ability of MCF-7 cells in a concentration-dependent man-ner(P＜0.05,P＜0.01).The results of laser confocal and CETSA experiments showed that berberine and AKT1 had a binding effect,and the stability of the two was enhanced after the combination.CONCLU-SION:Berberine inhibits MCF-7 cell proliferation and induces apoptosis in human breast cancer cells by targeting binding to AKT1 protein.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

AIM:To explore the mechanism of berberine on breast cancer cells based on network pharmacology and in vitro cell experiments.METH-ODS:Firstly,berberine and breast cancer were tak-en as the research objects,the intersection targets of the two were screened by VEEN diagram,GO function and KEGG enrichment analysis were per-formed by R language,and molecular docking and visualization were carried out by Autodock Vina and Pymol software.Then,berberine treated breast cancer MCF-7 cells for 24 h,and then in vi-tro cell experiments were performed.CCK-8 was used to detect cell viability,Edu and plate cloning were used to detect cell proliferation and cloning,and apoptosis was detected by An-nexin V-FITC/PI double staining and Western blot.Laser confocal and CETSA were used to verify the binding effect of berberine and AKT1 protein.RESULTS:The results of network pharmacology showed that berberine had a good binding to the core targets AKT1,AKT2 and MAPK3.Berberine(20,40,80 μmol/L)signifi-cantly inhibited the proliferation and cloning ability of MCF-7 cells in a concentration-dependent man-ner(P＜0.05,P＜0.01).The results of laser confocal and CETSA experiments showed that berberine and AKT1 had a binding effect,and the stability of the two was enhanced after the combination.CONCLU-SION:Berberine inhibits MCF-7 cell proliferation and induces apoptosis in human breast cancer cells by targeting binding to AKT1 protein.

Post-stroke spasticity is a series of symptoms after stroke,such as hand and foot urgency,unflexion and extension of muscles,etc.In order to deeply understand the cognition of post-stroke spasticity of ancient Chinese physicians and comb out their therapeutic thoughts,this study took the General Catalogue of Chinese Ancient Books of Traditional Chinese Medicine as a bibliographic reference,all the ancient Chinese literature on spasms after stroke was retrieved manually and by computer,and then sorted and analyzed,and classified them by longitudinal time,and extracted the description about post-stroke spasticity,including medical classics,prescriptions,clinical evidence,medical records and so on.And this paper verified and summarized the etiology,pathogenesis,functional and indications and prescription characteristics of spasticity after stroke,in order to deeply understand systematic theories and treatment ideas of the ancient medical practitioners in the bud,development and mature stages of their understanding of spasticity after stroke,and provide the theoretical basis for the later doctors to understand this disease and the modern clinical treatment of traditional Chinese medicine.

This study was aimed at developing an in vitro fluorescent substrate assay for the activity of leucyl aminopeptid-ase(LAP)from Echinococcus multilocularis and comparing it with the chemical chromogenic substrate enzyme activity assay.Through the establishment of reaction conditions for the fluorescent substrate-based in vitro enzyme activity assay,we com-pared the differences between the fluorescent substrate L-Leucine-7-amido-4-methylocoumarin(Leu-AMC)and the chemical chromogenic substrate L-Leucine-4-nitroanilide(Leu-pNA)through molecular docking,inhibition rates,and precision measures.Molecular docking revealed that the fluorescent substrate Leu-AMC had higher affinity for the protein than the chemical chromogenic substrate Leu-pNA.Through analysis of the effects of varying reaction conditions on fluorescence intensi-ty,we optimized the fluorescent substrate enzyme activity assay to demonstrate favorable performance at a reaction temperature of 37℃,a pH of 9.0,a protein concentration of 800 nmol/L,and a reaction duration of 60 minutes.Leu-AMC exhibited significant and distinct responses at a 5 μmol/L substrate concentration,under varying substrate conditions.The fluo-rescent substrate assay demonstrated more significant intergroup differences than the chemical chromogenic substrate assay when various inhibitors were added.This study established a fluorescence-based enzyme activity assay for leucyl aminopeptidase from Echinococcus multilocularis by using Leu-AMC as the substrate;this method demonstrated a more significant intergroup difference and sensitivity than the chemical chromogenic substrate assay.

Objective:To explore the morphological characteristics of the corpus callosum (CC) in patients with unilateral medial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS), and their correlation with hippocampal volume and clinical indicators.Methods:This was a cross-sectional study. Clinical (age of onset, disease duration, seizure frequency, seizure duration, etc.) and imaging data of 44 patients mTLE with unilateral HS confirmed by postoperative pathology and 42 healthy controls (HCs) recruited at West China Hospital of Sichuan University from June 2017 to May 2023 were analyzed retrospectively. Among the 44 patients, 19 had left-sided HS and 25 had right-sided HS. All subjects underwent high-resolution 3D T 1WI. Hippocampal volumes were obtained using FreeSurfer. ART was used to measure the morphological parameters of the CC for each participant, including total CC area, volume, perimeter, length, thickness, circularity, and the area of seven CC subregions defined by Witelson: rostrum, genu, body, anterior midbody, posterior midbody, isthmus and splenium. Differences in these metrics between two or three groups were compared using independent samples t-test or one-way ANOVA. Pearson or Spearman correlation analysis was used to observe the correlation between morphological features of the CC and hippocampal volume and other clinical indicators in patients with mTLE with unilateral HS. Results:Compared with HCs, patients with mTLE with unilateral HS had significantly reduced total CC area, CC circularity, as well as the area and thickness of the genu, anterior midbody, posterior midbody, isthmus, splenium, and the area of the rostrum ( P<0.05). Significant differences were observed in the total area, circularity, and subregional areas (genu, rostrum, anterior midbody, posterior midbody, splenium), as well as thickness (genu, anterior midbody, posterior midbody, isthmus) of the CC among mTLE with left-sided HS, mTLE with right-sided HS, and HCs ( P<0.05). When compared to HCs, the total area of the CC, circularity and the areas of the genu, rostrum, anterior midbody, posterior midbody, and splenium, and the thicknesses of the genu, anterior midbody, posterior midbody, and isthmus of the CC were significantly reduced in patients with mTLE with right-sided HS ( P<0.05), and the thicknesses of the midbody and isthmus of the CC were significantly reduced in patients with mTLE with left-sided HS compared to HCs ( P<0.05), and the two-by-two comparison of the rest of the indicators did not show statistically significant differences ( P>0.05). Correlation analysis showed that some morphological abnormalities in the CC in mTLE with unilateral HS patients were significantly correlated with age of onset, disease duration, seizure frequency, seizure duration, and hippocampal volume. Conclusions:mTLE with unilateral HS patients can exhibit morphological abnormalities in the CC, particularly in those with right-sided lesions. These abnormalities are significantly associated with seizure-related factors and hippocampal atrophy.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*