Alzheimer's disease (AD) is a common neurodegenerative disorder among the elderly, and BuChE has emerged as a potential therapeutic target. In this study, we reported the development of compound 8e, a selective reversible BuChE inhibitor (eqBuChE IC₅₀ = 0.049 μmol/L, huBuChE IC₅₀ = 0.066 μmol/L), identified through extensive virtual screening and lead optimization. Compound 8e demonstrated favorable blood-brain barrier permeability, good drug-likeness property and pronounced neuroprotective efficacy. Additionally, 8e exhibited significant therapeutic effects in zebrafish AD models and scopolamine-induced cognitive impairments in mice. Further, 8e significantly improved cognitive function in APP/PS1 transgenic mice. Proteomics analysis demonstrated that 8e markedly elevated the expression levels of very low-density lipoprotein receptor (VLDLR), offering valuable insights into its potential modulation of the Reelin-mediated signaling pathway. Thus, compound 8e emerges as a novel and potent BuChE inhibitor for the treatment of AD, with significant implications for further exploration into its mechanisms of action and therapeutic applications.

OBJECTIVES: To explore the active components that mediate the therapeutic effect of Centella asiatica on psoriasis and their therapeutic mechanisms. METHODS: TCMSP, TCMIP, PharmMapper, Swiss Target Prediction, GeneCards, OMIM and TTD databases were searched for the compounds in Centella asiatica and their targets and the disease targets of psoriasis. A drug-active component-target network and the protein-protein interaction network were constructed, and DAVID database was used for pathway enrichment analysis. In a RAW264.7 macrophage model of LPS-induced inflammation, the anti-inflammatory effect of 7.5, 15, 30, and 60 μmol/L quercetin, asiaticoside, and asiatic acid, which were identified as the main active components in Centella asiatica, were tested by measuring cellular production of NO, TNF‑α and IL-6 using Griess method and ELISA and by detecting mRNA expressions of IL-23, IL-17A, TNF-α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727) with RT-qPCR and Western blotting. RESULTS: A total of 139 targets of Centella asiatica and 4604 targets of psoriasis were obtained, and among them CASP3, EGFR, PTGS2, and ESR1 were identified as the core targets. KEGG analysis suggested that quercetin, asiaticoside, and asiatic acid in Centella asiatica were involved in cancer and IL-17 and MAPK signaling pathways. In the RAW264.7 macrophage model of inflammation, treatment with quercetin significantly reduced cellular production of NO, TNF‑α and IL-6, and lowered mRNA expressions of IL-23, IL-17A, TNF‑α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727). CONCLUSIONS Quercetin, asiaticoside and asiatic acid are the main active components in Centella asiatica to mediate the therapeutic effect against psoriasis, and quercetin in particular is capable of suppressing cellular production of NO, TNF‑α and IL-6 and regulating the IL-23/IL-17A inflammatory axis by mediating STAT3 phosphorylation to inhibit inflammatory response.
Quercetin/pharmacology* ; Psoriasis/metabolism* ; STAT3 Transcription Factor/metabolism* ; Mice ; Animals ; Centella/chemistry* ; Triterpenes/pharmacology* ; Phosphorylation ; Interleukin-17/metabolism* ; Interleukin-23/metabolism* ; RAW 264.7 Cells ; Pentacyclic Triterpenes/pharmacology* ; Macrophages/drug effects* ; Signal Transduction ; Plant Extracts

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective:To analyze the distribution of tumor necrosis factor-alpha（TNF-α）308 gene loci polymorphism in children with febrile seizures（FS）and to explore the correlation between TNF-α 308 gene polymorphisms and FS in children.Methods:A total of 320 children diagnosed with FS in the Department of Emergency，Shanghai Children's Hospital from September 1st，2020 to June 30th，2021 were enrolled as the study subjects，which were divided into simple febrile seizures（SFS）group（232 cases）and complex febrile seizures（CFS）group（88 cases）based on their clinical characteristics，and the clinical characteristics and laboratory indexes of the two groups were compared. Children with no history of convulsions were selected as the control group（160 cases）. The high-resolution melting and gene sequencing technology were used to analyze the polymorphism of TNF-α 308 gene in each group and the distribution of different gene types and allele frequencies among the groups was compared. A multivariate Logistic regression model was constructed to analyze the relationship between TNF-α 308 gene polymorphism and FS.Results:The age，mean corpuscular volume，mean corpuscular hemoglobin and platelet distribution width of the CFS group were significantly higher than those in the SFS group，and the difference was statistically significant（all P<0.05）.There was no significant difference in gender distribution，family history of FS，history of FS，body temperature at time of convulsions，WBC，Hb，CRP and PLT between the two groups（all P>0.05）.The genotype frequency distribution of TNF-α 308 polymorphism in the three groups was in line with the Hardy-Weinberg equilibrium（ P>0.05）.The AA genotype of TNF-α 308 locus was not detected in the study.Compared with the control group［17 cases（10.6%）］，the distribution proportion of GA genotype in the CFS group［22cases（25.0%）］and the SFS group［52cases（22.4%）］was increased，and the difference was statistically significant（ χ2＝11.126， P＝0.004）；Compared with the control group［17 frequencies（5.3%）］，the frequency distribution proportion of allele A in the CFS group［22 frequencies（12.5%）］and SFS group［52 frequencies（11.2%）］was also increased，and the difference was statistically significant（ χ2＝9.960， P＝0.007）. Adding control factors such as gender，age，family history of FS，body temperature at time of convulsions and blood routine markers，the multivariate Logistic regression model was constructed to show that there was no statistically significant association between TNF-α 308 genotype and CFS in children（ OR＝1.805，95% CI：0.926~3.519， P＝0.083）. Conclusion:In this study，there was no significant correlation between TNF-α 308 gene loci polymorphism and CFS in children.

Objective:To explore the influencing factors of early death (within 3 months) in adult glioma patients, and to construct a risk prediction model.Methods:Retrospective analysis was performed on the clinical data of 228 adult glioma patients admitted to the 909th Hospital (Dongnan Hospital of Xiamen University) from June 2020 to June 2024. Patients were divided into a death group ( n=32) and a survival group ( n=196) based on whether death occurred within 3 months, and the clinical data between the two groups were compared. Multivariate logistic regression was used to analyze the influencing factors of death within 3 months, a logistic regression prediction model was constructed, and receiver operator characteristic (ROC) curve was plotted to analyze the predictive value of the model. Results:There were no statistically significant differences between the two groups in age, gender, hypertension, diabetes, tumor location, tumor involvement, neurological impairment, maximum tumor diameter, chemotherapy, or radiotherapy (all P>0.05). The death group showed higher proportions of cerebral herniation ( χ2=20.74, P<0.001), hospital admission Karnofsky performance status (KPS) score ≤70 ( χ2=26.66, P<0.001), tumor grade Ⅲ-Ⅳ ( χ2=28.70, P<0.001), MGMT promoter unmethylation ( χ2=10.25, P=0.001), IDH wild-type ( χ2=6.18, P=0.013), and incomplete tumor resection ( χ2=10.37, P=0.001) compared with the survival group. Multivariate analysis revealed that cerebral herniation ( OR=19.78, 95% CI: 5.33-73.41, P<0.001), hospital admission KPS score ≤70 ( OR=19.64, 95% CI: 5.54-69.59, P<0.001), tumor grade Ⅲ-Ⅳ ( OR=9.40, 95% CI: 3.02-29.27, P<0.001), MGMT promoter unmethylation ( OR=4.28, 95% CI: 1.18-15.54, P=0.027), and incomplete tumor resection ( OR=9.50, 95% CI: 2.72-33.23, P<0.001) were independent risk factors for early death in glioma patients. The risk prediction model for early death in glioma patients constructed based on these indicators was logit ( P) =-18.04+2.96×cerebral herniation (with=1, without=0) +2.98×hospital admission KPS score (≤70=1, >70=0) +2.24×tumor grade (Ⅲ-Ⅳ=1, Ⅰ-Ⅱ=0) +1.45×MGMT promoter methylation (no=1, yes=0) +2.25×complete tumor resection (no=1, yes=0). ROC curve analysis demonstrated that this model had predictive value for early death in glioma patients, with an area under the curve of 0.920 (95% CI: 0.868-0.972), a sensitivity of 0.842, and a specificity of 0.906. Conclusions:Cerebral herniation, hospital admission KPS score ≤70, tumor grade Ⅲ-Ⅳ, MGMT promoter unmethylation, and incomplete tumor resection are independent risk factors for early death in adult glioma patients. The risk prediction model constructed based on these indicators has good predictive value.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

BACKGROUND:There are differentially expressed genes in acute intracerebral hemorrhage,which are related to the occurrence and development of intracerebral hemorrhage. OBJECTIVE:To screen differentially expressed genes and key genes in brain tissue of a rat model with acute intracerebral hemorrhage,to validate them through qPCR,and to analyze the relationships between key genes and the neurological function and brain tissue water content after intracerebral hemorrhage. METHODS:Seventy-eight Sprague-Dawley rats were randomly divided into two groups:in intracerebral hemorrhage group,a rat model of acute intracerebral hemorrhage was made using collagenase injection at the right caudate nucleus;and in sham-operated group,rats were injected with equal amount of saline at the same site.RNA was extracted from rat brain tissues of both groups using the TRIzol method and transcriptome sequencing technology was used to identify differentially expressed genes in brain tissues of acute intracerebral hemorrhage,which were then verified by qPCR and analyzed for the relationships between the genes and neurological function and brain tissue water content after intracerebral hemorrhage.And the key genes were analyzed by GO and KEGG functional enrichment analysis in combination with bioinformatics. RESULTS AND CONCLUSION:Ten key genes were identified,including CXCL8,SERPINE1,TFPI2,CXCR4,GDA,KCNQ5,ERICH3,SCN3B,CACNA1E,and CCL20.The contents of GDA,KCNQ5,ERICH3,SCN3B,and CACNA1E in the intracerebral hemorrhage group were lower than those in the sham-operated group(P＜0.05).The contents of CXCL8,SERPINE1,TFPI2,CXCR4 and CCL20 in the intracerebral hemorrhage group were higher than those in the sham-operated group(P＜0.05).The contents of GDA,KCNQ5,ERICH3,SCN3B,and CACNA1E were positively correlated with brain tissue water content and neurologic deficit score(P＜0.05),while the contents of CXCL8,SERPINE1,TFPI2,CXCR4 and CCL20 were negatively correlated with brain tissue water content and neurologic deficit score(P＜0.05).GO analysis indicated that differentially expressed genes were mainly enriched in two biological processes(leukocyte chemotaxis and chemokine-mediated signaling pathways),two cell components(cation channel complexes and ion channel complexes),and two molecular functions(gated channel activity and ion channel activity).KEGG analysis indicated that differentially expressed genes were concentrated in tumor necrosis factor signaling pathway,glutamatergic synapses and GABAergic synapses.To conclude,the differentially expressed genes in intracerebral hemorrhage include CXCL8,SERPINE1,TFPI2,CXCR4,GDA,KCNQ5,ERICH3,SCN3B,CACNA1E,and CCL20,and these genes are related to brain tissue water content and neurological function after intracerebral hemorrhage.These genes are mainly enriched in cell components,binding functions,cellular protrusions,and other related biological functions.

Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.

It is believed that retention due to deficient qi is an important pathogenesis of endometriosis （EMs）. Deficient qi is the root of the disease， mainly manifested as spleen deficiency， while retention is the branch pathogenesis of the disease， mainly with blood stasis， complicated with constraint， phlegm， heat， toxin and other pathological factors. Therefore， it is proposed to follow the treatment principle of supplementing deficiency and unblocking stagnation， and take the methods of replenishing qi and fortifying the spleen， removing stasis and eliminating concretions. Self-made Fuzheng Huayu Formula （扶正化瘀方） is taken as the basic formula， and can be modified with the symptoms in menstrual and non-menstrual periods. Additionally， the methods of moving qi， dispelling phlegm， clearing heat， relieving toxin and others can be combined， and it is recommended to treat the root and the branch simultaneously.

Objective:To study the clinical characteristics，diagnosis，treatment and prognosis of neonatal appendicitis.Methods:From January 2019 to December 2022，19 neonates with appendicitis（appendicitis group）and 38 neonates with sepsis（sepsis group）admitted to the Neonatal Department of Xi'an Children's Hospital Affiliated to Xi'an Jiaotong University were studied.The characteristics of clinical manifestation，imaging，treatment and prognosis of neonates in two groups were analyzed,retrospectively.Results:Among 19 neonates with appendicitis，31.6% were premature，the mean birth weight was（2 927.9±796.2）g，male∶female=2.17∶1.Abdominal distention（8/19，42.1%）and fever（8/19，42.1%）were the first symptoms of appendicitis，and the first symptoms of sepsis were mainly fever（20/38，52.6%）and poor reaction（7/38，18.4%）.In the appendicitis group，the proportions of abdominal distension（89.5% vs. 5.3%），vomiting（36.8% vs. 2.6%），breast resistance（84.2% vs. 39.5%），mental reaction changing（94.7% vs. 71.1%）and abdominal positive signs（84.2% vs. 5.3%）were significantly higher than those in sepsis group（ P<0.05）.C-reactive protein（CRP）was elevated in 16 neonates with appendicitis and 13 neonates with sepsis，and elevated gradually in 14 neonates with appendicitis. Compared with sepsis group，CRP was higher in appendicitis group（ P<0.05）.Fifteen（78.9%）neonates with appendicitis were diagnosed only by ultrasound，mainly manifested as low echo area or liquid dark area in the right abdomen，thickening of the appendix wall or effusion in the cavity，and liquid exudation.Three（15.8%）neonates with appendicitis were diagnosed by ultrasound and CT.Eight（42.1%）neonates with appendicitis were complicated appendiceal perforation.Fifteen neonates with appendicitis were treated by conservative treatment，four cases were treated by operation，and all of them were cured and discharged. Conclusion:Abdominal ultrasonography should be improved as soon as possible in neonates with fever and septicemia，especially those with abdominal symptoms or signs，or CRP increased during treatment，and CT or surgical exploration if necessary,to confirm the diagnosis of neonatal appenditis and early treatment.