Objective To discuss the value of clinical radiomic nomogram(CRN)and deep convolutional neural network(DCNN)in distinguishing atypical pulmonary hamartoma(APH)from atypical lung adenocarcinoma(ALA).Methods A total of 307 patients were retrospectively recruited from two institutions.Patients in institu-tion 1 were randomly divided into the training(n=184:APH=97,ALA=87)and internal validation sets(n=79:APH=41,ALA=38)in a ratio of 7∶3,and patients in institution 2 were assigned as the external validation set(n=44:APH=23,ALA=21).A CRN model and a DCNN model were established,respectively,and the performances of two models were compared by delong test and receiver operating characteristic(ROC)curves.A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification.Results The areas under the curve(AUCs)of DCNN model were higher than those of CRN model in the training,internal and external validation sets(0.983 vs 0.968,0.973 vs 0.953,and 0.942 vs 0.932,respectively),however,the differences were not statistically significant(p=0.23,0.31 and 0.34,respectively).With a radiologist-AI com-petition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS cat-egories in ALA than radiologists.Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA,with the former performing better.AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.

Objective:To describe the epidemiological characteristics of respiratory syncytial virus (RSV) infection in preschool children and explore the risk factors for severe pneumonia.Methods:Epidemiological data of 279 preschool children with RSV infection were investigated. The children were screened for severe pneumonia and separated into ordinary and severe types. General data and laboratory test data from both groups were compared, and binary logistic regression model analysis was applied to determine the risk factors for severe pneumonia.Results:Preschool children with RSV infection were mostly male (63.08%), <6 months old (65.95%) and had poor living environment (53.05%), with main symptoms of cough (91.04%) and wheezing (69.18%), the lung auscultation was mainly characterized by wheezing (86.74%), and imaging findings were mainly patchy shadows (76.34%), the onset season was concentrated in autumn (31.18%) and winter (43.37%). The detection rate of severe pneumonia in 279 pediatric patients was 20.27% (56/279). The proportions of onset season being autumn or winter, low birth weight infants, history of respiratory infections within 3 months, delayed treatment, neutrophils count <10×10 9/L, C-reactive protein≥10 mg/L, procalcitonin≥1.5 ng/mL, albumin<30 g/L, CD4 + /CD8 + <1.2 in the severe types were higher than those in the normal types ( P<0.05). Logistic regression analysis showed that the onset season was autumn or winter ( OR=2.316, 95% CI: 1.235-4.345), low birth weight infants ( OR=2.679, 95% CI: 1.442-4.977), history of respiratory infections within 3 months ( OR=2.815, 95% CI: 1.539-5.148), delayed treatment ( OR=2.869, 95% CI: 1.581-5.206), low albumin<30 g/L ( OR=2.756, 95% CI: 1.495-5.080), and low CD4 + /CD8 + <1.2 ( OR=3.016, 95% CI: 1.695-5.366) were risk factors for severe RSV pneumonia in preschool children ( P<0.05). Conclusions:Autumn and winter, low birth weight infants, history of respiratory infections within 3 months, delayed treatment, low albumin, and low CD4 + /CD8 + are related to the occurrence of severe RSV pneumonia in preschool children. Therefore, it is necessary to strengthen the attention to the condition of preschool RSV infected children with the above risk factors, and actively intervene in controllable factors to reduce the risk of severe pneumonia.

Objective:To construct a nomogram model predicting the occurrence of acute kidney injury (AKI) in patients with sepsis in the intensive care unit (ICU), and to verify its validity for early prediction.Methods:Sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to December 2021 were retrospectively included, and those who met the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with sepsis, and a nomogram was constructed based on the independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model.Results:741 patients with sepsis were included in the study, 335 patients developed AKI within 7 d of ICU admission, with an AKI incidence of 45.1%. Randomization was performed in the training set ( n=519) and internal validation set ( n=222). Multivariate logistic analysis revealed that acute physiology and chronic health status score Ⅱ, sequential organ failure score, serum lactate, calcitoninogen, norepinephrine dose, urea nitrogen, and neutrophil percentage were independent factors influencing the occurrence of AKI, and a nomogram model was constructed by combining these variables. In the training set, the AUC of the nomogram model ROC was 0.875 (95% CI: 0.767-0.835), the calibration curve showed consistency between the predicted and actual probabilities, and the DCA showed a good net clinical benefit. In the internal validation set, the nomogram model had a similar predictive value for AKI (AUC=0.871, 95% CI: 0.734-0.854). Conclusions:A nomogram model constructed based on the critical care score at admission combined with inflammatory markers can be used for the early prediction of AKI in sepsis patients in the ICU. The model is helpful for clinicians early identify AKI in sepsis patients.

Objective : To investigate risk factors related to coronary artery lesion( CAL) in children with Kawasaki disease． Methods : Retrospective analysis was conducted on clinical data of 144 children with Kawasaki disease. The cases were divided into two groups according to whether they had CAL or not．There were 50 children in CAL group and 94 children in non-coronary artery lesion(NCAL) group．The clinical and laboratory indicators in the two groups were analyzed and compared，and the diagnostic efficacy of these indicators for Kawasaki disease combined with CAL was analyzed by drawing the receiver operating characteristic (ROC) curve. Results : There was no statistical significant difference in age，gender，duration of fever，use time of immunoglobulin，erythrocyte sedimentation rate，C-reactive protein，serum amyloid A，N-terminal pro brain natriuretic peptide，white blood cell，hemoglobin and platelet between the two groups (P＞0. 05) ．In CAL group，the serum levels of 25-( OH) D3 and albumin were lower，while the levels of alanine transaminase，interleukin-6 and procalcitonin were higher than those in NCAL group (P＜0. 05) ．Multivariate logistic regression analysis showed that the levels of serum 25-( OH) D3 ( OR = 0. 984，95% CI:0. 974 ～0. 995) ，albumin ( OR = 0. 857，95% CI:0. 771－0. 951) ，alanine transaminase ( OR = 1. 011，95% CI : 1. 005 －1. 017) and interleukin-6 ( OR = 1. 002，95% CI : 1. 000 －1. 005 ) were significantly related with coronary artery lesion in children with kawasaki disease (P＜0. 05) ．The levels of 25-( OH) D3 ，albumin，alanine transaminase and interleukin-6 in serum had diagnostic value for Kawasaki disease combined with CAL，and the area under the curve (AUC) were 0. 660，0. 652，0. 711，and 0. 700，respectively．The AUC of combined diagnosis of four indicators was 0. 816 ． Conclution Decrease of serum 25-( OH) D3 and albumin levels，increase of serum interleukin-6 and alanine transaminase levels in children with Kawasaki disease are risk factors for CAL ，combined detection on these multi-indicators have diagnostic value for Kawasaki disease combined with CAL.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To explore the value of different radiomics models based on machine learning in predicting the risk of distant recurrence and metastasis of triple-negative breast cancer after neoadjuvant therapy.Methods:The clinical and imaging data of 150 patients with triple-negative breast cancer (TNBC) confirmed by histopathology were retrospectively analyzed. All patients underwent neoadjuvant chemotherapy and surgical resection from August 2011 to May 2017 in Fudan University Shanghai Cancer Center and Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. One hundred and nine patients from Shanghai Fudan University Shanghai Cancer Center were used as the training group, and 41 patients from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine were used as the validation group. The features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) before treatment and were added with time domain features innovatively. Least absolute shrinkage and selection operator cross validation and recursive feature elimination were applied to select features. Six different supervised machine learning algorithms (logistic regression, linear discriminant analysis, k-nearest neighbor, naive bayesian, decision tree, support vector machine) were used to predict the prognosis. ROC curve, accuracy and F1 measure were used to evaluate the performance of the six algorithms, and also verified by the validation group.Results:The support vector machine algorithm had the best predictive effect in the recurrence and metastasis model based on 15 features, with the highest area under curve (training group was 0.917, validation group was 0.859), and the highest accuracy rate (training group was 87.5%, validation group was 82.9%) and the highest F1 measure (training group was 0.800, validation group was 0.741). In addition, of the 15 imaging features, 12 were the time domain features and 3 were spatial features.Conclusion:With the help of the time domain features and machine learning algorithms, radiomics signatures based on preoperative DCE-MRI can help predict the distant prognosis for TNBC after neoadjuvant chemotherapy and provide support for clinical decision making and follow-up management.

Objective:To evaluate the application value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and dynamic contrast enhancement MRI (DCE-MRI) in the prediction of the early efficacy of concurrent chemoradiotherapy (CCRT) for cervical squamous cell carcinoma.Methods:Fifty patients with cervical squamous cell carcinoma confirmed by pathology were included. Before CCRT, IVIM-DWI and DCE-MRI were scanned, and the values of quantitative parameters including ADC, D, D * and f of IVIM-DWI and K trans, K ep, V e and V p of DCE-MRI before treatment were measured for all patients. MRI reexamination was performed 1 month after the end of CCRT, and all patients were divided into the cure group and the residual group according to the tumor remission. The parameters of IVIM-DWI and DCE-MRI before treatment were statistically compared between two groups. The optimal predictive parameters and predictive thresholds were determined by drawing the receiver operating characteristic (ROC) curve. Results:Twenty-four patients were assigned into the cure group and twenty-six patients in the residual group. The ADC, D and V e values before treatment in the cure group were significantly lower than those in the residual group (all P<0.05), whereas the f and K trans values were significantly higher than those in the residual group (both P<0.05). The other parameters did not significantly differ between two groups (all P>0.05). The area under ROC curve (AUC=0.823) of D value was the largest, followed by K transvalue (AUC=0.754). The combined prediction efficacy of D and K trans (AUC=0.867) was higher than that of either D or K trans alone. The sensitivity was 88.5%, 85.8% and 88.8%, and the specificity was 70.8%, 66.7% and 79.2%, respectively. Conclusions:Quantitative parameters of IVIM-DWI and DCE-MRI before treatment have certain predictive value for the early efficacy of CCRT in cervical squamous cell carcinoma, among which the predictive efficacy of D value is the highest, and the combined application of D and K trans can improve the predictive efficacy.

Objective: To understand the occupational hazard and distribution of silica dust (free SiO₂≥10%) in the workplace environment of the enterprises in Fengxian District, and to provide scientific basis for improving the working environment and protecting the physical and mental health of the workers. Methods: Individual sampling monitoring and on-site labor hygiene investigation were conducted on 421 workers involved in 87 silicon dust enterprises in the jurisdiction from 2014 to 2018, and measured concentration-time weighted average (C_TWA) . Results: The results showed that the range of the C_TWA was (0.021~17.000) mg/m³, the median was 1.600 mg/m³, and the qualified rate of 30.88%. The difference of total dust concentration was statistically significant in different years (Z=38.831, P<0.05) . The qualified rate of small-scale enterprises is higher than that of medium-scale enterprises (χ²=9.472, P<0.05) . The qualified rate of other domestic enterprises is higher than that of private enterprises and foreign enterprises (χ²=10.089, P<0.05) . The acceptance rate of metal products manufacturing is lower than that of general equipment manufacturing and other manufacturing enterprises (χ²=64.626, P<0.05) . The qualification rate of natural ventilation is higher than that of mechanical ventilation (χ²=6.278, P<0.05) . Conclusion The enterprises involved in silicon production in Fengxian District need to further strengthen the production process reform and improve the ventilation and dust removal protection measures. Widely carry out the publicity of occupational disease prevention and control law, conduct targeted pre-job training, improve workers' awareness of self-protection, and protect the occupational health of workers in many ways.

Objective To explore the efficacy and safety of daclatasvir (DCV ) combined with asunprevir (ASV) for chronic genotype 1b (GT1b) hepatitis C .Methods Twenty-nine GT1b hepatitis C patients who were treated with DCV combined ASV in Henan Provincial People′s Hospital from September 2017 to November 2017 were included .Hepatitis C virus (HCV ) RNA levels were tested before treatment ,1 week ,2 weeks ,3 weeks ,4 weeks ,8 weeks ,12 weeks and 24 weeks after treatment , and 12 weeks after the end of the treatment .The comorbidities ,combined use of drugs and adverse clinical events were registered .T test was used to compare the measurement data with normal distribution and M (P25,P75) was used for measurement data with non-normal distribution .Results A total of 29 patients with GT1b were included ,with 4 cirrhosis cases and 25 non cirrhotic cases .Seven patients had history of previous interferon and ribavirin combination treatment .There were 9 patients with comorbidity and 7 patients with combined medication . Finally , 25 patients completed a 24-week course of antiviral treatment ;3 patients were lost to follow-up ,and 1 patient withdrew after 16weeks of antiviral treatment because of a virus rebound .Of the 26 followed up patients ,25 achieved sustained virological response at 12-week (SVR12 ) , and one patient failed .And the HCV RNA NS5A resistance-associated variants (RAV) were detected in the patients with treatment failure .No severe adverse clinical events occurred in 26 patients .Conclusions DCV combined with ASV is effective and safe in the treatment of GT 1b chronic hepatitis C .However , the effect of RAV on therapeutic efficacy should be concerned during the treatment .

Professor , as the famous and veteran physician of TCM, has practiced TCM for more than 50 years, and had unique experience for the treatment of encephalopathy. Professor applied the theory of skin to guide the treatment of shoulder-hand syndrome after stroke. On the basis of the ancient acupuncture method of , combined with modern acupuncture method and new materials, with characteristics of shoulder-hand syndrome after stroke at different time points, he proposed to use floating needling and acupoint catgut embedding to treat patients with stageⅠ, and to use picking therapy and penetration needle to treat patients with stageⅡ, and to use fire needles, penetration needle and acupoint catgut embedding to treat patients with stageⅢ, combined with conventional acupuncture and rehabilitation treatment. As a result, the superior efficacy was achieved.