ObjectiveTo investigate the influence of hepatitis B virus (HBV) infection on intrahepatic natural killer (NK) cells and innate lymphoid cell 22 (ILC22), and to provide theoretical and experimental bases for clarifying mechanisms of HBV infection in inducing innate immune response. MethodsA total of 10 male BALC/c aged 6-8 weeks were divided into experimental group and control group, with 5 mice in each group. The mice in the experimental group were treated with the hydrodynamic injection of normal saline containing 10 μg plasmids of complete HBV genome (the volume equaled to 9% of the body weight of the mouse) via the caudal vein, and those in the control group were only treated with normal saline. The mice were scarified 4 days later, and intrahepatic lymphocytes (IHLs) were isolated. Flow cytometry was used to determine the proportions of NK cells and ILC22 subset in IHLs, and the t-test was used for comparison between groups. ResultsHydrodynamic injection of the plasmids containing complete HBV genome induced high levels of HBsAg and HBeAg in mice, with an increase in the serum level of alanine aminotransferase. After HBV infection, the experimental group showed a significant increase in the proportion of intrahepatic NK cells compared with the control group (25.90%±4.92% vs 12.98%±2.13%, t=3.811, P=0.003), while there were no significant differences in the proportions of CD127+ and CD127- NK subsets in NK cells between the two groups. Moreover, after HBV infection, the experimental group showed a significant increase in the proportion of intrahepatic NKp46+ILC22 subset compared with the control group (36.05%±6.85% vs 10.22%±3.54%, t=7.372, P＜0001); however, there was no significant difference in the proportion of NKp46-ILC22 between the two groups. ConclusionHBV infection induces increased levels of intrahepatic NK cells and NKp46+ILC22 cells and thus promotes the innate immune response in the liver.

Objective Given the extensive application of machine learning(ML)in medical models and its remarkable learning and generalization capabilities,this study employed automated ML(AutoML)combined with patient demographics and clinical conditions to early assess the risk of failure in bowel preparation prior to colonoscopy.Methods A retrospective analysis was conducted on patients who underwent colonoscopy examinations in Hospital 1 and Hospital 2 from January 2022 to January 2023,and their general and clinical information was collected.According to the Boston bowel preparation scale(BBPS),a BBPS of≤5 was defined as a failure in bowel preparation,>5 was deemed satisfactory.From the data of the two hospitals,we randomly divided the dataset into a training set(n=303)and a validation set(n=76)at an 8∶2 ratio.Least absolute shrinkage and selection operator(LASSO)logistic regression(LR)model was used for feature selection,a nomogram scoring system was constructed,and models were established using AutoML based on five algorithms.Model performance was evaluated through receiver operator characteristic curve(ROC curve),calibration curves,LR-based decision curve analysis(DCA),SHAP plots,and force plots.Results Among the 379 patients,105 cases(27.7%)experienced bowel preparation failure(BBPS≤5).21 study variables were narrowed down to 10 through LASSO with 5-fold cross-validation,resulting in the development of a Nomogram chart with demonstrated reliability via calibration curves.Using the H2O platform and five algorithms[gradient boosting machine(GBM),deep learning(DL),generalized linear model(GLM),Stacked Ensemble and distributed random forest(DRF)],67 models were developed.Stacked Ensemble outperformed the others with an area under the curve(AUC)of 0.871,LogLoss of 0.403,and RMSE of 0.354,surpassing traditional LR model and other models.Variable importance contribution plots indicated significant predictive influences from factors such as the interval between laxative ingestion and examination,history of constipation,completion of laxative regimen,age,and presence of a companion during the procedure.Finally,SHAP plots and force plots revealed variable distribution patterns in binary classification predictions and the impact of variables on predictive outcomes.Conclusion The AutoML model based on the Stacked Ensemble algorithm exhibits clear clinical utility in early prediction of bowel preparation failure risk.Moreover,a clinically applicable column chart scoring tool is constructed.

Objective To understand the driver gene mutation status in patients with non-small cell lung cancer (NSCLC) in Xi'an City, and to analyze the association with environmental exposure factors. Methods A total of 305 NSCLC patients admitted to the First Affiliated Hospital of the Air Force Medical University from January 2019 to December 2023 were included. The driver gene mutation status was observed, and the relationship with environmental exposure factors was analyzed. Results The driver gene mutation rate of 305 patients was 46.89%, with EGFR gene mutation accounting for the highest proportion, and 4 cases of gene co-mutations were detected. There was a difference in gender among patients with different single drive gene mutations (P<0.05), and the proportion of EGFR in women was significantly higher (P<0.05). Univariate analysis showed that there were statistical differences in family history, smoking history, long-term cooking history, and fried smoked food intake between patients with driver gene mutation and patients without driver gene mutation (P<0.05). Logistic regression analysis suggested that long-term cooking history (OR=2.392), and fried smoked food intake (OR=2.849) were the environmental exposure factors affecting EGFR gene mutation (P<0.05), and smoking history (OR=1.377) was an environmental exposure factor of KRAS gene mutation (P<0.05). Conclusion EGFR gene mutation accounts for the highest proportion of NSCLC patients in Xi'an City, and is mainly female. Long-term cooking history, and fried smoked food intake are related to EGFR gene mutation. There is a certain association between smoking history and KRAS gene mutation.

Anaplastic lymphoma kinase (ALK) fusion gene, as a tumor driver gene, was crucial for the occurrence and development of non-small cell lung cancer (NSCLC). Recently, targeted ALK fusion gene has become the main treatment method for ALK-positive NSCLC. The first and second generation ALK inhibitors (ALKi), such as crizotinib, ceritinib, alectinib and ensartinib have been approved in China. However, there was no guidance for the management of ALKi adverse reactions. Therefore, this "Recommendations from experts in the management of adverse reactions to ALK inhibitors (2021 version)" has been summarized, led by Lung Cancer Professional Committee of Sichuan Cancer Society and Sichuan Medical Quality Control Center for Tumor Diseases, to provide practical and feasible strategies for clinical ALKi management specification of adverse reactions.
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Carcinoma, Non-Small-Cell Lung ; Crizotinib ; Humans ; Lung Neoplasms/genetics* ; Protein Kinase Inhibitors/adverse effects* ; Receptor Protein-Tyrosine Kinases/antagonists & inhibitors*

AIM: To analyze the chemical ingredients of Qingxin-zishen prescription decoction (QZPD) and predict its main pharmacodynamic substances and mechanism in the prevention and treatment of menopause syndrome (MPS) with the help of high performance liquid chromatography-quadrupole-time of flight mass spectrometry (HPLC-Q-TOF/MS) combined with network pharmacology. METHODS: The chemical ingredients of QZPD were identified after analyzing the retention time, exact mass, secondary mass spectrometry fragmentation and other information obtained from HPLC-Q-TOF/MS and comparing them with the established chemical ingredients database and the literatures. The targets of ingredients in QZPD were predicted by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction database. The disease targets of MPS were obtained through Online Mendelian Inheritance in Man (OMIM) and GeneCards Database. Gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of potential targets were analyzed with the Metascape database. Cytoscape 3.7.2 software was used to construct the network of active components-key targets-pathways. AutoDockTools 4.2.5 software was applied in the molecular docking verification between the key active components and key targets. RESULTS: A total of 83 components were identified in QZPD and 847 drug targets were predicted. After intersection them with 3 050 disease targets, 395 common targets were obtained. After network topology analysis, 74 key targets were obtained, involving mitogen-activated protein kinase (MAPK), phosphatidylinositol-3 kinase/protein kinase B (PI3K/Akt), transforming growth factor-β (TGF-β) and other signaling pathways. Molecular docking analysis results indicated that 23 key active components, such as berberine, epiberberine, coptisine, geissoschizine methyl ether, liensinine, norcoclaurine, palmatine, quercetin, and luteolin, had good binding activity with several of the key targets. CONCLUSION: This study preliminarily identifies the potential effective chemical ingredients of QZPD, predicts its targets in the prevention and treatment of MPS, which provides supporting information for the further study of the pharmacodynamic substances and mechanisms of QZPD.

BACKGROUND: Anaplastic lymphoma kinase (ALK) rearrangement is a common driver gene of non-small cell lung cancer (NSCLC). Ceritinib is a second-generation ALK inhibitor, which can bring survival benefits to ALK-positive metastatic NSCLC. However, few studies focus on the safety and efficacy of ceritinib in China. Therefore, this study intends to investigate the safety and preliminary efficacy of ceritinib 450 mg with meals in Chinese patients with ALK-positive NSCLC through a real world study. METHODS: From October 2018 to December 2019, patients with ALK-positive NSCLC from 8 medical centers in Sichuan province were recruited in this study. All of these participants received ceritinib 450 mg/d with food. The basic characteristics, adverse effects (AEs) and responses were collected and analyzed in order to evaluate the safety and efficacy of ceritinib. RESULTS: A total of 109 patients were included in this study. Data cutoff was January 23, 2020. The median duration of treatment exposure was 5.87 mon (range: 0.4 mon-15.7 mon). Total AEs were reported in 98 (89.9%) of 109 patients and grade 3 or 4 AEs were reported in 22.9% of patients. Most common AEs (mainly grade 1 or 2) were diarrhea (60.6%), elevated alanine aminotransferase (ALT)(38.5%) and aspartate aminotransferase (AST)(37.6%). As of data cutoff, 45 patients discontinued ceritinib. The overall response rate (ORR) was 37.6% (95%CI: 28.5%-47.4%) and disease control rate (DCR) was 86.2% (95%CI: 78.3%-92.1%). CONCLUSIONS The treatment of ceritinib 450 mg with food for Chinese ALK-positive NSCLC patients had a good safety profile and favorable DCR in real-world setting. However, this conclusion needs to be further verified by large sample, prospective trials.