Objective To construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

Objective To investigate the current situation of off-label use of immune checkpoint inhibitors(ICIs)in China and the cognition of medical staff.Methods From August 31 to September 9,2022,a nationwide survey questionnaire was sent to medical staff in the form of electronic questionnaire.The questionnaire included 13 questions,covering four dimensions:Drug allocation,current situation of medication beyond the instructions,cognition of medication beyond the instructions and current situation of medication beyond the instructions.Results A total of 745 questionnaires were collected.75.70％of respondents reported off-label use of ICIs in their hospitals,with the most common type being off-label indications.The primary reasons for such practices included support from authoritative domestic and international guidelines,clinical research data validation,and approved indications in foreign regulatory documents.85.37％of respondents believed off-label use could offer new hope for patients,while 68.86％considered it unlikely to increase adverse reactions.44.97％of respondents' hospitals had not established off-label use registration systems for ICIs.88.86％of respondents emphasized the need for stricter regulations governing off-label use of immunotherapeutic agents.Conclusion Off-label use of ICIs is common,and there is a lack of unified guidance in clinical practice.It is urgent to form norms and consensus on the management of off-label use of ICIs.

Objective·To investigate the role of caspase recruitment domain-containing protein 9(CARD9)in regulating macrophage polarization in a rat model of severe acute pancreatitis(SAP).Methods·SD rats were divided into 4 groups:Control group,SAP group,SAP+CARD9 shRNA group,and SAP+Control shRNA group with six rats in each group.The SAP rats transfected with CARD9 shRNA were established by injecting CARD9 shRNA adenovirus 48 hours before the SAP model was induced.The pancreatic tissues,peripheral blood,and peritoneal macrophages were collected 12 hours after the model was established.The expressions of CARD9 gene and CARD9 protein in peritoneal macrophages and pancreatic tissues were measured by real-time PCR and Western blotting.The expressions of TNF-α,IL-6,IL-10 and Arg-1 mRNA were detected by real-time PCR and the polarization types of peritoneal macrophages were detected by flow cytometry.Results·The expressions of CARD9 gene and CARD9 protein in peritoneal macrophages in CARD9 shRNA rats were significantly lower than those in SAP rats and interference control rats,which confirmed the success of CARD9 interference model.Compared with SAP rats,CARD9 shRNA rats had significantly reduced degree of inflammation and pathological scores;the mRNA levels of TNF-α,and IL-6 in peritoneal macrophages were significantly decreased;meanwhile,the mRNA levels of IL-10 and Arg-1 were increased,and the changes in TNF-α and IL-6 were significantly higher than those of IL-10 and Arg-1.The proportion of M1 macrophages was significantly reduced,and the ratio of M1/M2 was significantly decreased.The expression level of CARD9 mRNA in peritoneal macrophages was positively correlated with the proportion of M1 macrophages and the mRNA levels of TNF-α and IL-6.Conclusion·CARD9 is involved in regulating macrophage polarization in SAP rats,and it mainly regulates M1 polarization.Inhibition of CARD9 expression can reduce M1 macrophage polarization and reduce the inflammatory response in SAP rats.

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 investigate the role of caspase recruitment domain-containing protein 9(CARD9)in regulating macrophage polarization in a rat model of severe acute pancreatitis(SAP).Methods·SD rats were divided into 4 groups:Control group,SAP group,SAP+CARD9 shRNA group,and SAP+Control shRNA group with six rats in each group.The SAP rats transfected with CARD9 shRNA were established by injecting CARD9 shRNA adenovirus 48 hours before the SAP model was induced.The pancreatic tissues,peripheral blood,and peritoneal macrophages were collected 12 hours after the model was established.The expressions of CARD9 gene and CARD9 protein in peritoneal macrophages and pancreatic tissues were measured by real-time PCR and Western blotting.The expressions of TNF-α,IL-6,IL-10 and Arg-1 mRNA were detected by real-time PCR and the polarization types of peritoneal macrophages were detected by flow cytometry.Results·The expressions of CARD9 gene and CARD9 protein in peritoneal macrophages in CARD9 shRNA rats were significantly lower than those in SAP rats and interference control rats,which confirmed the success of CARD9 interference model.Compared with SAP rats,CARD9 shRNA rats had significantly reduced degree of inflammation and pathological scores;the mRNA levels of TNF-α,and IL-6 in peritoneal macrophages were significantly decreased;meanwhile,the mRNA levels of IL-10 and Arg-1 were increased,and the changes in TNF-α and IL-6 were significantly higher than those of IL-10 and Arg-1.The proportion of M1 macrophages was significantly reduced,and the ratio of M1/M2 was significantly decreased.The expression level of CARD9 mRNA in peritoneal macrophages was positively correlated with the proportion of M1 macrophages and the mRNA levels of TNF-α and IL-6.Conclusion·CARD9 is involved in regulating macrophage polarization in SAP rats,and it mainly regulates M1 polarization.Inhibition of CARD9 expression can reduce M1 macrophage polarization and reduce the inflammatory response in SAP rats.

Objective To explore the effects of physical activity(PA)on carotid intima-media thickness(CIMT)thickening in patients with type 2 diabetes mellitus(T2DM)with poor HbA1c control.Methods A total of 2505 T2DM inpatients with poor HbA1c control who visited the Endocrinology Department of the Integrated Traditional Chinese and Western Medicine Hospital Affiliated with Nanjing University of Traditional Chinese Medicine and Changzhou Second People's Hospital Affiliated with Nanjing Medical University from January 2019 to February 2022 were enrolled in this study.The patients were categorized into the mild-intensity PA(1～599 MET-min/w,n=795),moderate-intensity PA(600～1499 MET-min/w,n=1107)and high-intensity PA(≥1500 MET-min/w,n=603)based on the metabolic equivalence level(MET).Logistic regression was performed to analyze the relationship between PA and CIMT thickening.Statified analysis was used to investigate the effects of blood pressure(BP)and low-density lipoprotein cholesterol(LDL-C)target status on CIMT thickness in different PA groups.Results Restricted cubic spline plots showed a nonlinear relationship between PA levels and the risk of CIMT thickening(P<0.05).Logistic regression showed that with moderate-intensity PA as the control,while high-intensity PA was the influence factor of CIMT thickening(OR 1.581,95%CI 1.244～2.010,P<0.05).Statified analyses showed that under mild-intensity PA,using neither BP nor LDL-C on target as reference,achievement of either index and both were the influence factor of CIMT thickening.Using BP on target as reference,achievements of both indexes,achievements of neither of both indexes,and achievement of LDL-C only were the influence factor of CIMT thickening.Using LDL-C on target as reference,achievement of neither of them and achievement of BP only were the influence factor of CIMT thickening.Under high-intensity PA,achievement of both indexes was negatively associated with CIMT thickening.Conclusions Moderate-intensity PA is the recommended scheme to prevent CIMT thickening in T2DM patients whose HbA1c is not up to standard.Mild-intensity PA is not related to the risk of CIMT thickening,while high-intensity PA is positively related to the risk of CIMT thickening,and focusing on LDL-C compliance is particularly important to avoid CIMT thickening.

Objective:To observe the application value of Yifei Fumai Tang in the treatment of difficulty offline and intensive care unit-acquired weakness(ICU-AW).Methods:Used random number table method,60 patients with difficulty offline and ICU-AW who were admitted to our hospital from June 2022 to June 2024 were divided into control group(received conventional Western medicine comprehensive treatment,n=30)and study group(received Yifei Fumai Tang treatment in addition to the control group,n=30).Medical Research Council(MRC),Barthel Index(BI)scores,clinical indicators(total mechanical ventilation time,weaning success rate,secondary intubation rate,ICU stay,and mobilization time),diaphragm thickness,diaphragm mobility,and incidence of adverse reactions between two groups were compared.Results:Compared with the control group at 7 d after treatment and 14 d after treatment,the MRC and BI scores in the study group were higher(P＜0.05).Compared with the control group,the study group had lower rate of secondary intubation rate,shorter total mechanical ventilation time,ICU stay time,and mobilization time,higher weaning success rate,and greater diaphragm thickness,diaphragm mobility(P＜0.05).There was no significant difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Yifei Fumai Tang combined with Western Medicine in the treatment of Difficulty Offline and ICU-AW,can effectively improve the prognosis of patients,shorten total mechanical ventilation time,ICU stay,mobilization time,increase weaning success rate,and improve respiratory function.

Objective:To explore the incidence status,influencing factors and short-term prognosis characteristics of early onset coronary heart disease in women in Wansheng District of Chongqing,and to provide scientific basis for formulating regional prevention and treatment strategies.Methods:This study was a single-center retrospective study,100 coronary heart disease in women from January 2022 to December 2023 at Chongqing Wansheng Economic and Technological Development Zone People's Hospital were prospective selected,and they were divided into early onset group of 40 cases(≤ 65 years old)and late onset group of 60 cases(＞65 years old)based on their age of onset.Another 60 healthy women who underwent physical examinations during the same period to exclude coronary heart disease were selected as the control group.Univariate factor and multiple factor logistic regression analysis were used to identify independent risk factors for early onset coronary heart disease in women.Draw receiver operating characteristic(ROC)curve for the subjects,the efficacy of risk factors in predicting early onset coronary heart disease based on the area under the curve(AUC)of ROC curve were evaluated.Patients were followed up for 1 year to observe the occurrence of major adverse cardiovascular events(MACE).Result:Among 100 fcoronary heart disease in women,the early onset group accounted for 40.00％(40/100).Univariate analysis showed that age,hyperlipidemia history,smoking history,hypertension history,family history,diabetes history,total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C)were related to the early onset coronary heart disease.Multivariate analysis showed that,hyperlipidemia history(OR=4.124,95％CI:2.343-7.217),smoking history(OR=3.564),hypertension(OR=3.253),family history(OR=2.981),diabetes history(OR=2.873)were independent risk factors.ROC curve analysis results showed that joint evaluation had the best predictive value,with AUC of 0.829,which was higher than the AUC of individual evaluation for each factor.The incidence of MACE in the early onset group(45.00％)was significantly higher than that in the late onset group(P＜0.05).Conclusion:Early onset coronary heart disease in women in Wansheng District of Chongqing is related to the hyperlipidemia history,smoking,hypertension history,family history and diabetes history.The incidence of MACE in early-onset patients followed up for 1 year is higher than that in late-onset patients.

Objective:To investigate the relationship between nerve growth factor (NGF) concentration in follicular fluid and abnormal menstrual cycle in infertile patients with polycystic ovary syndrome (PCOS).Methods:A retrospective cohort study was conducted on 100 infertile patients with PCOS who underwent in vitro fertilization and embryo transfer (IVF-ET) at Department of Obstetrics and Gynecology, Peking University Third Hospital from March 2017 to June 2019. For comparison, the 100 patients with PCOS were divided into low NGF group ( n=50) and high NGF group ( n=50) based on the median NGF concentration (1 644.03 ng/L) in follicular fluid. Baseline characteristics, menstrual status and clinical outcomes of assisted reproductive technology were compared. We performed multiple linear regression analysis to examine the effect of NGF in follicular fluid on menstrual cycle length for multivariate analysis. Results:1) PCOS patients in the low NGF group had significantly higher body mass index [(27.24±5.17) kg/m 2] and white blood cell count [7.31(5.99, 8.43)×10 9/L ] than those in the high NGF group [(25.03±4.46) kg/m 2, P=0.024; 5.95(5.08,7.01)×10 9/L, P=0.001], while high-density lipoprotein cholesterol [1.15 (0.98, 1.36) mmol/L] and basic follicle-stimulating hormone level [6.51 (5.10,7.95) U/L] in the low NGF group were significantly lower than those in the high NGF group [1.36 (1.09,1.52) mmol/L, P=0.039;6.51 (5.10,7.95)U/L, P=0.040]. 2) PCOS patients in the low NGF group had significantly higher menstrual cycle length [60.00 (35.00, 180.00) d] than the high NGF group [32.50 (27.00,67.50) d, P=0.001]. 3) Multiple linear regression analysis revealed that after adjustment for body mass index, age, infertility duration, infertility type, and glucose and lipid metabolic parameters, the NGF concentration in the follicular fluid independently and negatively correlated with menstrual cycle length ( P<0.05). 4) The NGF concentration in follicular fluid was not correlated with assisted reproductive outcomes. Conclusion:NGF concentration in follicular fluid is closely related to the degree of menstrual cycle abnormalities in patients with PCOS.