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 analyze the prevalence of latent tuberculosis infection(LTBI)among relevant healthcare workers(HCWs)in designated tuberculosis medical institutions(MIs)in Yantai City,and explore its influencing factors.Methods The cluster random sampling method was adopted to select two county-and district-level desig-nated tuberculosis MIs.All HCWs underwent questionnaire survey and creation tuberculin skin test(C-TST)at the same time,and the influencing factors for LTBI were analyzed.Results A total of 215 HCWs from designated tu-berculosis MIs were included for analysis,37 were diagnosed with LTBI,with an infection rate of 17.21％(95％CI:12.42％-22.93％).Multivariate logistic regression analysis showed that clinicians(OR=3.19,95％CI:1.05-9.69),laboratory technician(OR=5.90,95％CI:1.21-28.77),working years≥10 years(OR=3.31,95％CI:1.39-7.90),and tuberculosis history of family members(OR=6.49,95％CI:1.01-41.46)were independent risk factors for LTBI.Conclusion The infection risk of clinicians and laboratory technicians who directly contact with tuberculosis patients or Mycobacterium tuberculosis is higher than that of other HCWs,and is related to the length of working years.It is suggested that healthcare-associated infection control measures should be streng-thened,and tuberculosis active screening should be carried out regularly for HCWs in key departments.

Objective:To correlate the expression of interleukin-6 (IL-6), CD40 ligand (CD40L), and soluble triggering receptor expressed on myeloid cells 1 (sTREM-1) with regulatory T lymphocyte (Treg)/helper T lymphocyte 17 (Th17) in children with immune thrombocytopenic purpura (ITP) and to assess their combined diagnostic value.Methods:A retrospective study was conducted involving 80 children with ITP (ITP group) and 80 healthy children undergoing routine health screenings (control group) at Jinhua Central Hospital from February 2023 to February 2024. The levels of IL-6, CD40L, and sTREM-1, along with the Treg/Th17 ratio, were compared between the two groups and among children with different degrees of ITP severity. The correlations between IL-6, CD40L, and sTREM-1 and the severity of ITP, as well as the Treg/Th17 ratio were analyzed. Additionally, the diagnostic value of the combined tests was evaluated.Results:The levels of IL-6, CD40L, and sTREM-1 in the ITP group were (46.53 ± 8.69) ng/L, (6.51 ± 1.51), and (18.76 ± 3.72) ng/L, respectively. These values in the ITP group were significantly higher than those in the control group [(11.72 ± 1.58) ng/L, (4.31 ± 1.25), (8.07 ± 1.34) ng/L, t = 35.25, 10.04, 24.18, all P < 0.001]. The Treg/Th17 ratio in the ITP group was (1.34 ± 0.41), which was significantly lower than that in the control group [(13.56 ± 2.87), t = 37.70, P < 0.001]. In children with severe ITP, the levels of IL-6, CD40L, and sTREM-1 were (56.39 ± 11.70) ng/L, (9.62 ± 1.78), and (24.72 ± 4.81) ng/L, respectively. For those with moderate ITP, the levels were (45.41 ± 8.27) ng/L, (6.38 ± 1.40), and (17.69 ± 3.56) ng/L, respectively. In children with mild ITP, the levels were (37.04 ± 6.32) ng/L, (5.11 ± 1.32), and (14.50 ± 3.04) ng/L. The Treg/Th17 ratio in children with mild ITP was (0.95 ± 0.26), which was significantly lower than that in the moderate (1.37 ± 0.40) and severe (2.88 ± 0.56) groups. All differences were statistically significant among the three groups ( F = 23.98, 42.57, 37.15, 122.23, all P < 0.001). The levels of IL-6, CD40L, and sTREM-1 were positively correlated with the severity of ITP ( r = 0.565, 0.542, 0.538) and negatively correlated with the Treg/Th17 ratio ( r = -0.572, -0.536, -0.532), with all correlations being statistically significant (all P < 0.001). The receiver operating characteristic curve analysis indicated that the area under the curve values for the individual diagnoses of IL-6, CD40L, and sTREM-1 were 0.779, 0.750, and 0.763, respectively. In contrast, the area under the curve value for the combined diagnosis was 0.951, which was greater than that for each individual marker ( Z = 1.924, 2.137, 2.015, P = 0.037, 0.026, 0.031). Conclusions:The levels of IL-6, CD40L, and sTREM-1 in children with ITP are substantially correlated with the severity of ITP and the Treg/Th17 ratio, demonstrating notable diagnostic efficacy for ITP. Notably, the combination of IL-6, CD40L, and sTREM-1 greatly enhances diagnostic value.

BACKGROUND:Tumor microenvironment can participate in the occurrence and development of gastric cancer and promote chemotherapy resistance in various ways.Among them,the tumor microenvironment crosstalk mediated by exosomal miRNAs can induce matrix reprogramming,participate in tumor heterogeneity,and form a microenvironment conducive to tumor proliferation,migration,invasion,immune escape,and chemotherapy resistance.OBJECTIVE:To review the mechanism of action of exosomal miRNAs in the microenvironment of gastric cancer and its application in the diagnosis and prognosis assessment of gastric cancer in recent years.METHODS:"Exosomal miRNAs,gastric cancer,angiogenesis,apoptosis,proliferation,migration,autophagy,invasion,immune response,chemotherapy resistance,biomarker"for English search terms and"exosomal miRNAs,gastric cancer"for Chinese search terms were searched in PubMed and CNKI databases.The search period was from 2017 to 2024.After preliminary screening by reading the title and abstract,the articles with poor correlation and repeated content were excluded,and 77 articles were finally included for induction and discussion.RESULTS AND CONCLUSION:(1)Exosomes,as important carriers of intercellular information exchange,can carry a variety of information substances such as miRNA,and realize intercellular signal transmission through three ways:activation of cell surface receptors on target cells,fusion with the plasma membrane of recipient cells,and endocytosis.(2)Exosomal miRNAs play an important role in the progression of gastric cancer by regulating the proliferation,apoptosis,autophagy,angiogenesis,invasion and metastasis,immune response,and the formation of drug resistance of gastric cancer cells.(3)The interaction between miRNAs and target mRNA and its regulatory network are widely found in tumorigenesis and human cancer development.Different types of exosomal miRNAs have different effects on the regulation of apoptosis of gastric cancer cells,and the effects of different exosomal miRNAs on apoptosis related proteins and pathways of gastric cancer cells are screened.Rational use of its inducers or inhibitors can regulate the apoptosis level of gastric cancer cells.(4)Exosomal miRNAs of different cell origin play an important role in the establishment of tumor microenvironment,angiogenesis,immune response,and chemotherapy resistance by inducing M1-polarized macrophages to M2 type.(5)Exosomal miRNAs exist extensively and stably in blood and other body fluids,and their differential expression in patients with gastric cancer can be used as a basis for diagnosis,prognosis,and treatment of patients with gastric cancer.Currently,exosomal miRNAs widely studied as biomarkers include miR-379-5p,miR-590-5p,miR-29s,miR-21,etc.Among them,the sensitivity and specificity of miR-590-5p are 63.7％and 86％,respectively.The expression level of miR-590-5p is closely related to the overall survival rate and the depth of invasion of gastric cancer patients.(6)The design of exosomal miRNAs mimics or inhibitors and their targeted delivery to the tumor site using nano-delivery vectors(such as exosomes and liposomes)to restore the normal level of miRNAs may be a new strategy for the treatment of gastric cancer.(7)Although exosomal miRNAs have great application prospects in the diagnosis and treatment of gastric cancer patients,there are still some problems to be solved.For example,the potential targets and mechanisms of exosomal miRNAs have not been fully explored,and their effectiveness and safety need to be further confirmed.The extraction and purification of exosomes lack standardized large-scale preparation processes.

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 explore the molecular epidemiological characteristics of human metapneumovirus (HMPV) in children with acute respiratory infection (ARI) in Beijing from 2023 to 2024.Methods:In the longitudinal study, 9 834 children with ARI were enrolled from August 2023 to December 2024, including the influenza-like illness (ILI) group from emergency and outpatient department receiving influenza virus (Flu) and HMPV test and the ARI inpatient group for 13 common respiratory pathogen screening test including HMPV, Flu, respiratory syncytial virus, and so on. All respiratory samples positive with HMPV were genotyped by amplifying and sequencing of G gene and further phylogenetic analysis. The χ2 test and Wilcoxon rank-sum test were used to compare the positive rate and basic clinical data of the 2 groups. Results:Among 9 834 enrolled patient, there were 5 276 male and 4 558 female children, with age 5.4 (1.9, 8.2) years. In ILI group of 1 460 patients, there were 83 cases (5.7%) positive for HMPV, with the age 4.9 (3.6, 6.6) years and children under 6.0 years old 59 cases (71.1%). Among 8 374 ARI inpatients, there were 256 cases (3.1%) positive for HMPV, with age 3.5 (1.3, 6.4) years and children under 6.0 years old 188 cases (73.4%). The HMPV positive rate and the age of children positive for HMPV in ARI inpatient group were significantly lower than that in ILI group (both P<0.001). In December, 2024, the HMPV positive rates of ILI and ARI inpatient group (21.3% (17/80), 15.0% (47/314)) were significantly higher than the total positive rates of each group (both P<0.001). Among 279 subtyped specimens, there were 155 cases (55.6%) belonging to genotype A and 124 cases (44.4%) belonging to genotype B. Sub-lineage A2.2.2 containing 111nt-insertions was predominate one in 2023 with positive ratio 89.2% (91/102), and B2 was predominate in 2024 with positive ratio 64.4% (114/177). Conclusions:From 2023 to 2024, the positive rate of HMPV in the ILI group was higher than that in the ARI inpatient group, suggesting a common epidemic of HMPV infection. Children positive for HMPV in the ARI inpatient group were younger than that in the ILI group. A severe epidemic of HMPV was observed in the winter of 2024, which requires attention. Sub-lineage A2.2.2 with 111nt-duplicate insertions and B2 were the predominant epidemic strains in 2023 and 2024, respectively.

Objective To analyze the prevalence of latent tuberculosis infection(LTBI)among relevant healthcare workers(HCWs)in designated tuberculosis medical institutions(MIs)in Yantai City,and explore its influencing factors.Methods The cluster random sampling method was adopted to select two county-and district-level desig-nated tuberculosis MIs.All HCWs underwent questionnaire survey and creation tuberculin skin test(C-TST)at the same time,and the influencing factors for LTBI were analyzed.Results A total of 215 HCWs from designated tu-berculosis MIs were included for analysis,37 were diagnosed with LTBI,with an infection rate of 17.21％(95％CI:12.42％-22.93％).Multivariate logistic regression analysis showed that clinicians(OR=3.19,95％CI:1.05-9.69),laboratory technician(OR=5.90,95％CI:1.21-28.77),working years≥10 years(OR=3.31,95％CI:1.39-7.90),and tuberculosis history of family members(OR=6.49,95％CI:1.01-41.46)were independent risk factors for LTBI.Conclusion The infection risk of clinicians and laboratory technicians who directly contact with tuberculosis patients or Mycobacterium tuberculosis is higher than that of other HCWs,and is related to the length of working years.It is suggested that healthcare-associated infection control measures should be streng-thened,and tuberculosis active screening should be carried out regularly for HCWs in key departments.

Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks