Objective To construct and verify a risk prediction model of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer,and to screen the optimal model by using machine learning algorithm,so as to provide references for clinical formulation of a nursing risk management plan.Methods The convenience sampling method was used to retrospectively select 476 patients who underwent thoracoscopic radical resection of lung cancer in a tertiary hospital in Hangzhou,Zhejiang Province from January to December 2023 as a construction group.Logistic regression,decision tree,random forest and naive Bayesian model were constructed by SPSS 29.0 and R 4.3.0 software.The prediction performance of each model was compared by accuracy,precision,recall,F1 score and area under the receiver operating characteristic curve,and the optimal model was screened.From January to June 2024,204 patients in the unit were prospectively selected as the research subjects of an external validation group.The discrimination and calibration of the optimal model were evaluated by AUC value and calibration curve.Results A total of 680 patients completed the survey.All 4 models showed that multimodal analgesia,thoracic drainage tube type,pain score,tracheal intubation type,state anxiety and catheter indwelling time were the influencing factors of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer(P＜0.05).The 4 risk prediction models showed that the random forest prediction model had the best comprehensive performance.The external verification results showed that the AUC value was 0.913,and the calibration curve fitted well with the 45° ideal line.Conclusion Among the 4 risk prediction models,the random forest prediction model has the best performance,which is more suitable for the assessment of the risk of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer,and has good generalization and clinical application value.

OBJECTIVE To systematically evaluate the efficacy and safety of tislelizumab in the treatment of advanced non- small cell lung cancer （NSCLC）. METHODS Computerized searches were conducted in PubMed， Embase， the Cochrane Library， CNKI， Wanfang and other Chinese and English databases to collect randomized controlled trials （RCTs） on tislelizumab for advanced NSCLC. The search period was from the establishment of the databases to December 2024. After strictly screening the literature， extracting data and conducting quality evaluations in accordance with the inclusion and exclusion criteria， a meta-analysis was performed using RevMan 5.3 and Stata 16.0 software. RESULTS A total of 18 RCTs involving 2 337 patients were included， with 1 283 in the experimental group and 1 054 in the control group. The meta-analysis results showed that the objective response rate [RR＝1.61， 95%CI （1.48， 1.75）， P＜0.000 01]， disease control rate [RR＝1.21， 95%CI （1.13， 1.29）， P＜0.000 01]， progression free survival [HR＝0.55， 95%CI （0.45， 0.66）， P＜0.000 01]， and overall survival [HR＝0.78， 95%CI（0.62， 0.97）， P＝0.03] were significantly better in the experimental group than in the control group. There was no statistically significant difference in the incidence of adverse reactions between the two groups [RR＝1.00， 95%CI （0.73， 1.37）， P＝1.00]； among the common adverse reactions， only the incidence of liver function impairment was significantly higher in the experimental group than in the control group [RR＝1.30， 95%CI （1.10， 1.54）， P＜0.01]. CONCLUSIONS Tislelizumab in combination with chemotherapy or targeted drugs significantly improves the efficacy in patients with advanced NSCLC without increasing the risk of adverse reactions overall. However， liver function should be closely monitored during treatment.

Objective To construct and verify a risk prediction model of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer,and to screen the optimal model by using machine learning algorithm,so as to provide references for clinical formulation of a nursing risk management plan.Methods The convenience sampling method was used to retrospectively select 476 patients who underwent thoracoscopic radical resection of lung cancer in a tertiary hospital in Hangzhou,Zhejiang Province from January to December 2023 as a construction group.Logistic regression,decision tree,random forest and naive Bayesian model were constructed by SPSS 29.0 and R 4.3.0 software.The prediction performance of each model was compared by accuracy,precision,recall,F1 score and area under the receiver operating characteristic curve,and the optimal model was screened.From January to June 2024,204 patients in the unit were prospectively selected as the research subjects of an external validation group.The discrimination and calibration of the optimal model were evaluated by AUC value and calibration curve.Results A total of 680 patients completed the survey.All 4 models showed that multimodal analgesia,thoracic drainage tube type,pain score,tracheal intubation type,state anxiety and catheter indwelling time were the influencing factors of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer(P＜0.05).The 4 risk prediction models showed that the random forest prediction model had the best comprehensive performance.The external verification results showed that the AUC value was 0.913,and the calibration curve fitted well with the 45° ideal line.Conclusion Among the 4 risk prediction models,the random forest prediction model has the best performance,which is more suitable for the assessment of the risk of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer,and has good generalization and clinical application value.

Recent innovations in nanomaterials inspire abundant novel tumor-targeting CRISPR-based gene therapies. However, the therapeutic efficiency of traditional targeted nanotherapeutic strategies is limited by that the biomarkers vary in a spatiotemporal-dependent manner with tumor progression. Here, we propose a self-amplifying logic-gated gene editing strategy for gene/H₂O₂-mediated/starvation multimodal cancer therapy. In this approach, a hypoxia-degradable covalent-organic framework (COF) is synthesized to coat a-ZIF-8 in which glucose oxidase (GOx) and CRISPR system are packaged. To intensify intracellular redox dyshomeostasis, DNAzymes which can cleave catalase mRNA are loaded as well. When the nanosystem gets into the tumor, the weakly acidic and hypoxic microenvironment degrades the ZIF-8@COF to activate GOx, which amplifies intracellular H⁺ and hypoxia, accelerating the nanocarrier degradation to guarantee available CRISPR plasmid and GOx release in target cells. These tandem reactions deplete glucose and oxygen, leading to logic-gated-triggered gene editing as well as synergistic gene/H₂O₂-mediated/starvation therapy. Overall, this approach highlights the biocomputing-based CRISPR delivery and underscores the great potential of precise cancer therapy.

OBJECTIVE:To investigate the characteristics of pediatric drug-induced liver injury (DILI) in China,and to provide reference for reducing ADR.METHODS:Using"liver injury liver damage hepatotoxicity hepatitis liver disease""drug induced children" as keywords,related domestic literatures were retrieved from CNKI and Wanfang database during 2007-2016,clinical information of DILI children in literatures were recorded in detail and analyzed comprehensively.RESULTS:A total of 363 literatures were retrieved,including 13 effective literatures and 665 children in total.There were 424 boys (63.76％)and 241 girls (36.24％),with ratio of 1.76 ∶ 1.The youngest child was 1 month old,the oldest child was 14 years old;the average age was 7.87 years,337 children aged more then 7 years old,accounting for 50.68％.Top 3 primary diseases were respiratory tract infection (40 cases,31.50％),hematologic diseases (29 cases,22.83％) and tumor (14 cases,11.02％).Top 3 pediatric DILI-inducing drug types were antibiotics (245 cases,34.41％),TCM (143 cases,20.08％) and antipyretic analgesics (113 cases,15.87％).DILI usually happened within 4 weeks (332 cases,82.18％).The most common clinical classification was hepatocellular type (382 cases,65.30％).The severity of liver injury was mainly mild and moderate (505 cases,86.32％),and 80 cases were severe (13.68％),including 31 cases of hepatic failure (5.30％).Clinical symptoms mainly manifested as anorexia,jaundice,nausea,vomiting,hypodynamia and abdominal discomfort.After drug withdreawd and treatment,96.54％ of the patients were recovered or cured,and 4 cases died (0.60％).CONCLUSIONS:Under the premise of rational use of drugs,it is necessary to carry out medication education and supervision for antibiotics,TCM and antipyretic analgesics which mainly induce pediatric DILI,pay attention to allergic history and evaluate the progress of extrahepatic symptoms.When ADR occurred,the timely and drug withdrawal intervention are conducted to improve good prognosis.