Objective To systematically evaluate the efficacy and safety of traditional Chinese medicine (TCM) compound in treating pulmonary nodules, providing evidence-based medical evidence for TCM intervention in pulmonary nodules. Methods Computer search of PubMed, CNKI, Wanfang, VIP, and SinoMed was conducted to select randomized controlled trials (RCTs) of TCM compound intervention in pulmonary nodules, with the retrieval time from the inception to November 29, 2023. The Cochrane bias risk assessment tool was used to evaluate the quality of the included studies, and Review Manager 5.4 was used for Meta-analysis. Results A total of 18 RCTs were included, covering 8 provinces across the country, with a total sample size of 1301 patients. The TCM compounds used in the included studies all incorporated the method of dissolving phlegm and dissipating nodules. There was a high risk of bias uncertainty in the included studies. Meta-analysis results suggested that TCM compound could significantly reduce the diameter of pulmonary nodules [MD=−1.41, 95%CI (−1.70, −1.13), P<0.001], decrease the number of nodules [MD=−0.37, 95%CI (−0.73, −0.01), P=0.05], alleviate clinical symptoms [MD=−4.84, 95%CI (−6.04, −3.64), P<0.001], and improve lung function [forced expiratory volume in one second (FEV1), MD=0.55, 95%CI (0.09, 1.01), P=0.02; FEV1/forced vital capacity, MD=6.12, 95%CI (4.47, 7.78), P<0.001]. However, there was no statistically significant difference in the probability of malignancy between the experimental group and the control group [MD=−0.01, 95%CI (−0.01, 0.00), P=0.09]. Conclusion TCM compound can significantly reduce the diameter of pulmonary nodules, decrease the number of nodules, alleviate clinical symptoms, and improve lung function, but future multicenter, large-sample, high-quality RCTs are still needed to further explore and verify this conclusion.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

P53 is a key tumor suppressor gene,which is regulated in many ways.Zinc finger 148(ZNF148)and SP5,as zinc finger transcription factors(TFs),play important roles in tumor suppression and carcinogenesis.The regulatory relationship between these two TFs and p53 has not been reported.In this paper,Ishikawa and A549 cell lines with different p53 expression levels were used as research mod-els to explore the transcriptional regulation of the P53 gene by ZNF148 and SP5.The data showed that there were differences in the expression of ZNF148 and SP5 in the two cell lines.The mRNA expression of ZNF148 in Ishikawa was 1.9 times higher than that of A549,and the mRNA expression of SP5 in A549 was 802.4 times that of ZNF148.Data showed that in Ishikawa cells,the expression of P53 de-creased(81.8％)after ZNF148 knockdown,and increased(2.6 times)after SP5 overexpression.Transfection of si-SP5 and ZNF148 expression plasmids into A549 cells increased the mRNA expression of P53 by 6.6 times and 14.6 times,respectively.These results indicate that ZNF148 could activate,whereas SP5 could inhibit,P53 expression.The conserved cis-element of ZNF148 and SP5 TFs was found in the region of the P53 promoter by bioinformatics methods.The data from dual luciferase reporter gene assay showed that the luciferase activity of ZNF148 in Ishikawa and A549 cells was increased by 2.1-fold and 4.2-fold compared with the control group(P＜0.05).Compared with the control group,the normalized relative luciferase activity of transfected SP5 decreased by 77.1％and 35.7％(P＜0.05).However,when the cis-element of ZNF148 and SP5 was mutated,the effect disappeared.Further trans-fection of ZNF148 and SP5 with different ratios revealed that SP5 could reverse the transcriptional activa-tion of P53 by ZNF148.Studies have shown that ZNF148 shares a common site with SP5,and the ratio of the two TFs may influence the transcriptional activity of P53.The expression of the Wnt pathway and the cell proliferation rate after knockdown of ZNF148 and SP5 were further studied to explore the role of the two TFs.Our data show that ZNF148 and SP5 could regulate the transcriptional activity of P53,and their expression levels and interaction may be the key factors regulating P53 expression.

Objective To compare the differences of endoscopic ultrasound (EUS)-guided non-invasive measurement of portal venous pressure and EUS-guided portal pressure gradient(EUS-PPG) in measurement of portal venous pressure on animals and their correlation. Methods Twenty-four miniature pigs were selected and fed with carbon tetrachloride and phenobarbital sodium combined with high-fat,low-protein and low-choline diet for 16 weeks to establish a liver cirrhotic portal hypertension model. The changes of biochemical indexes of liver function and liver pathology in the experimental pigs were observed to evaluate whether the model was successful. After the model was successfully established,the hemodynamic parameters of the portal venous trunk were measured non-invasively under EUS guidance,including portal venous blood flow and splenic artery pulsatility index,thereby calculating portal venous pressure. Then,taking EUS-PPG,the portal vein,hepatic vein,and inferior vena cava were punctured with an 18G puncture needle under general anesthesia guided by the translinear endoscopic ultrasound,and the PPG was calculated through the central venous pressure monitoring system.The Pearson correlation analysis,Kappa test,ICC intraclass correlation coefficient and Bland-Altman plot were used for consistency analysis. Results All the 24 pigs survived 16 weeks after modeling.The serum levels of alanine transaminase (ALT),aspartate transaminase (AST),albumin (ALB),globulin (GLB),total bilirubin (TBIL) and indirect bilirubin (IBIL)after modeling were higher than those before modeling(P<0.05). HE staining and Sirius red staining showed abnormal liver morphology and increased collagen fibers after modeling,suggesting that the experimental pig model of liver cirrhotic portal hypertension was successfully established. The results of EUS-guided non-invasive measurement of portal venous pressure showed that the mean splenic artery pulsatility index was (2.03±0.68),the mean portal vein flow was (17.27±4.31)cm/s,and the mean portal venous pressure was (15.97±3.65)mmHg. The measurement results of the mean portal venous pressure,hepatic venous pres-sure and PPG of EUS-PPG were (20.68±4.71)mmHg,(4.07±2.14)mmHg and (16.38±4.28)mmHg respectively. Pearson correlation analysis showed that there was a significant positive correlation between the portal venous pressures measured by the two methods (r=0.902,P<0.001);the consistency tests of Kappa test and ICC intraclass correlation coefficient showed that the measurement results of the two methods were highly consistent (Kappa=0.699,P<0.001;ICC=0.945);Bland-Altman plot analysis showed that most of the points fell within 95％ limits of agreement. Conclusion EUS-guided non-invasive measurement of portal venous pressure has a high correlation and consistency with the measurement results by EUS-PPG,which has high success rate,and accurate reflection of portal venous pressure,with low cost and good safety.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To investigate the changes of nitric oxide(NO),endothelin-1(ET-1),thromboxane B2(TXB2)levels in patients with chronic heart failure(CHF)and their correlation with plasma brain natriuretic peptide(BNP).Methods:We enrolled 110 CHF patients admitted in the Second Hospital of Qinhuangdao between January and December 2022.According to the New York Heart Association(NYHA)cardiac function classification,the patients were divided into cardiac function class Ⅱ group(n=22),cardiac function class Ⅲ group(n=50),and cardiac function class Ⅳ group(n=38).Baseline data,levels of NO,ET-1,TXB2,BNP and other related laboratory indexes were compared among three groups.Pearson correlation analysis was used to analyze the association of NO,ET-1,TXB2 with plasma BNP in CHF patients.Multivariate linear regression was employed to analyze related factors of elevated BNP in CHF patients.Results:Compared with patients in class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher uric acid[(467.39±32.60)μmol/L vs.(367.25±22.39)μmol/L vs.(421.42±28.34)μmol/L],total bilirubin[(17.36±3.10)μmol/L vs.(10.65±1.39)μmol/L vs.(11.12±2.01)μmol/L],BNP[(897.60±50.11)ng/L vs.(381.37±31.25)ng/L vs.(527.60±47.84)ng/L],NO[(50.12±5.95)μmol/L vs.(25.36±2.14)μmol/L vs.(37.92±4.84)μmol/L],ET-1[(114.10±10.53)pg/L vs.(80.25±7.38)pg/L vs.(97.03±8.40)pg/L],TXB2[(417.98±29.35)pg/ml vs.(302.63±19.63)pg/ml vs.(381.29±26.44)pg/ml](P＜0.001 all).Compared with those in class Ⅱ group,those in class Ⅲ group had significant higher above-mentioned indexes(except total bilirubin)(P＜0.001 all).Pearson correlation analysis indicated that plasma NO,ET-1,TXB2 were positively associated with BNP(r=0.828,0.750,0.720,P＜0.001 all).Multivariate linear regression analysis indicated that uric acid,total bilirubin,plasma NO,ET-1 and TXB2 levels were independent risk factors for elevated BNP level in CHF patients(B=0.555～20.550,P＜0.05 or＜0.01),while the use of angiotensin converting enzyme inhibitors(ACEI)/angiotensin Ⅱ receptor blockers(ARB)was an independent protective factor(B=-46.222,P=0.027).Conclusion:The levels of NO,ET-1,and TXB2 are closely related to the occurrence and development of CHF,and they show an increasing trend with the progression of CHF,and is closely related to BNP.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Varicella, often referred to as chickenpox, is a widespread acute infectious condition triggered by the varicella-zoster virus (VZV). It manifests with systemic symptoms and distinct skin and mucosal eruptions, including macules, papules, and vesicles. Although it mainly affects children, the disease is typically more severe in teenagers and adults. Following the adoption of vaccine-based control measures in China, there has been a noticeable trend of varicella affecting older demographics, leading to an uptick in cases among teenagers and adults. This review synthesizes the latest research on the clinical symptoms, epidemiological trends, and immunization strategies for varicella in these age groups, both domestic and aboard. The goal is to enhance strategies for prevention and control, support the development of tailored immunization policies, and underscore the critical role of the varicella vaccine in comprehensive health management across all ages.

Objective:To understand the tobacco use situation and related factors among the occupational population in Beijing in 2018.Methods:The data were obtained from a special survey on “Healthy Beijing People—Ten-year Action Plan for Health Promotion (2009—2018)”. From June to September 2018, 12 908 participants were recruited from 16 districts of Beijing using the stratified sampling method combined with the typical sampling method. Questionnaires were conducted to collect basic information, work situations, tobacco exposure in public places, and smoking cessation. The Chi-square test was used to compare the differences in current smoking rates, smoking cessation rates and second-hand smoke exposure rates among participants with different characteristics. Multivariate logistic regression analysis was used to analyze the related factors of tobacco use.Results:The present smoking rate of the occupational population in Beijing was 18.0% (95% CI: 17.3%-18.7%), and the passive smoking exposure rate of non-smokers was 61.0% (95% CI: 60.1%-61.9%). The smoking cessation rate among the working population was 22.2% (95% CI: 20.7%-23.7%), and the proportion of current smokers with the intention to quit was 63.9% (1 485/2 325). The present smoking rate of employees of different genders, ages, education levels and occupational nature showed statistically significant differences ( P<0.05). The results of multivariate logistic regression analysis showed that in males, compared with the age group of 16-29 years old, the risk of smoking increased in the age groups of 30-39 years old, 40-49 years old and over 50 years old [ OR (95% CI) values were 1.49 (1.27-1.74), 1.34 (1.13-1.59) and 1.30 (1.06-1.59)]. Compared with those with junior high school or lower education, participants with high school/vocational/college education group had a higher risk of smoking ( OR=1.38, 95% CI: 1.13-1.69), while those with master′s degree or above had a lower risk of smoking ( OR=0.30, 95% CI: 0.22-0.42). The physical workers ( OR=1.28, 95% CI: 1.11-1.47) had a higher risk of smoking than the mental workers. Conclusion:The current smoking rate among the occupational population in Beijing is lower than the national level during the same period, but the exposure rate to second-hand smoke is relatively high. Age, educational level, job nature and gender are related factors of smoking.