ObjectiveTo develop a classification model based on complete blood count （CBC） parameters combined with clinical factors to predict severe respiratory infections caused by Human adenovirus （HAdV） in pediatric patients. MethodsFrom September 2023 to September 2024， the CBC parameters and related clinical data from pediatric patients diagnosed with HAdV infection were collected. Principal component analysis and random forest models were used to identify potential predictors of severe cases. ResultsA total of 668 pediatric patients were included， with 564 cases assigned to the training cohort and 104 cases to the validation cohort. Severe cases were defined as pneumonia and/or fever lasting ≥5 days （pneumonia or prolonged fever， PorPF）. Principal component analysis and feature importance analysis （Mean Decrease Gini value） identified the monocytosis ratio （PMono）， red blood cell count （RBC）， and platelet count （PLT） as the most critical CBC parameters. Logistic regression analysis revealed that oxygen therapy （OR = 4.367， 95% CI： 1.568–12.161） and increased work of breathing （OR = 3.904， 95% CI： 2.146–7.101） were relative risk factors for PorPF. Meanwhile， higher PMono （OR = 0.696， 95% CI： 0.640–0.757）， RBC （OR = 0.201， 95% CI： 0.124–0.325）， and PLT （OR = 0.990， 95% CI： 0.987–0.994） were protective factors. When PMono was used as a predictive marker for PorPF， the area under the receiver operating characteristic curve （AUC） was 0.648 and 0.705， respectively. A random forest model incorporating four risk factors ［PMono， RBC， PLT， and hematocrit （HCT）］ was constructed to classify PorPF and general cases， achieving AUCs of 0.688 and 0.768， respectively. ConclusionsPMono， RBC， and PLT may serve as characteristic CBC indicators for predicting pneumonia or prolonged fever in children with HAdV infection. A risk factor model built using PMono， RBC， PLT， and HCT offers a relatively simple and accurate approach to predicting severe cases in pediatric HAdV infections.

Objective:To assess the performance of machine learning(ML),and integrate the clinical parameters with coronary artery calcium(CAC)score of computed tomography(CT)and quantification of automated epicardial adipose tissue(EAT),so as to predict the long-term risk of myocardial infarction(MI)and cardiogenic death in asymptomatic patients.Methods:A total of 1 058 subjects with cardiovascular risk factors and without symptoms of coronary heart disease who underwent physical examination at the Fifth Medical Center of Chinese PLA General Hospital from January 2013 to October 2015 were selected as this study subjects.A long-term follow-up was conducted on them after CAC score.EAT volume and density were quantified using a fully automated deep learning method.ML extreme gradient boosting was trained by using clinical data,risk score of atherosclerotic cardiovascular disease,CAC score and automated EAT measure,and the repeated 10-fold cross validation was used to verify the model.Results:During the 8-year follow-up period,61 cases of 1 058 subjects occurred events of MI and(or)cardiac death.The area under curve(AUC)value of ML was significantly higher than that of the atherosclerotic cardiovascular disease(ASCVD)risk and the predicting events of CAC score(ML:0.82,ASCVD:0.77,CAC:0.77).Compared with ML with only clinical variable,machine learning based on ASCVD,CAC and EAT had more predictive ability for MI and cardiac death[AUC 0.82(95%CI:77-87)vs.0.78(95%CI:0.72-0.84),P=0.02].The survival rate of subjects with high ML scores had a greater decline degree with the increasing of time,therefore,the subjects with higher ML scores were more likely to experience events.Conclusion:ML,which integrated clinical and quantitative imaging variables,can provide long-term risk prediction for patients with cardiovascular risk factors.

Objective: To investigate the epidemiological characteristics of 45 norovirus clusters in Baoshan District, Shanghai Municipality from 2019 to 2021, so as to provide the evidence for improving the management of norovirus clusters. Methods: The epidemiological characteristics and laboratory testing of 45 norovirus clusters were collected in Baoshan District from 2019 to 2021, and the temporal distribution, population distribution, distribution of places of norovirus clusters, clinical manifestations, laboratory testing results, origin of norovirus infection and transmission routes of norovirus clusters were analyzed using a descriptive epidemiological method. Results: A total of 45 norovirus clusters involving 29 379 individuals were reported in Baoshan District from 2019 to 2021, and 435 cases with norovirus infections were identified, with an average attack rate of 1.48%. Norovirus infection most often occurred from November to April of the following year, and the infection predominantly occurred among students (408 cases, 93.79%). There were 25 norovirus clusters in kindergartens (55.56%), 18 clusters in primary and middle schools (40.00%), one cluster in a social care facility and one cluster in a geriatric ward. Vomiting was the predominant clinical symptom (416 cases, 95.63%). The positive rate of norovirus was 37.35% in 597 samples. There were 40 norovirus clusters caused by the norovirus GⅡ genotype (88.89%), and the median duration of the norovirus cluster was 6 days (interquartile range, 1 day). In addition, 82.22% of the norovirus clusters reported to centers for disease control and prevention within 1 day,and 88.89% of the clusters were transmitted through human to human transmission or vomitus of cases. Conclusion The norovirus clusters predominantly occur in winter/spring and among students in Baoshan District from 2019 to 2021. Kindergartens and primary and middle schools are main places of norovirus infections, and the norovirus GⅡ genotype is the main type of pathogens. Human-to-human transmission and exposure to patients' vomitus are the main routes of transmission.

Objective:To explore the status and influencing factors of stigma in patients with pituitary adenoma.Methods:From January 2020 to May 2021, a total of 106 patients with pituitary adenoma in the Affiliated Huai'an No.1 People 's Hospital of Nanjing Medical University were selected using the convenience sampling method. Internalized Stigma of Mental Illness, Medical Coping Style Questionnaire and Connor-Davidson Resilience Scale were used to investigate them. Binary Logistic regression analysis was used to evaluate the influencing factors of stigma in patients with pituitary adenoma. Results:Among 106 patients with pituitary adenoma, 87 (82.08%) had stigma. The stigma score of patients with pituitary adenoma was (2.58±0.61) . Binary Logistic regression analysis results showed that family monthly income, coping style and mental elasticity were the influencing factors of stigma in patients with pituitary adenoma ( P<0.05) . Conclusions:Stigma is common in patients with pituitary adenoma. Medical staff should take targeted intervention measures according to the influencing factors of stigma in patients with pituitary adenoma, so as to help patients reduce stigma.

Objective: To investigate the epidemiological characteristics of cluster vomiting diarrhea outbreaks caused by Sapovirus infection in Baoshan District, Shanghai, and to provide reference for the standardized management of cluster vomiting diarrhea events in schools. Methods: The cluster of vomiting and diarrhea outbreaks in Baoshan District from 2017 to 2019 were collected, and the basic information and scene of each cluster were summarized. Sample results were collected and descriptive epidemiological methods were used to analyze the epidemiological characteristics of the outbreak. Results: From 2017 to 2019, a total of 100 cluster vomiting and diarrhea events were reported in Baoshan District, including 9 cases caused by Sapovirus, 120 cases of cluster vomiting diarrhea caused by Sapovirus, with an average incidence rate of 2.37%, including 39 laboratory confirmed cases, the epidemic epidemic time was mainly concentrated in September to November, the places were distributed in 4 primary schools and 5 kindergartens, the main clinical manifestations were vomiting (112/120, 93.33%), abdominal pain (27/120, 22.50%), diarrhea (26/120, 21.67%), compared with Norovirus events, the positive rate, vomiting and fever ratios of Zarovirus were lower than those of Norovirus( χ 2=8.32, 4.52, 14.20, P <0.05). Conclusion In school cluster vomiting and diarrhea events, the positive detection rate of Sapovirus is second enteric vinus behind Norovirus, and it s recommended to formulate a prevention and control plan for Sapovirus infection diarrhea to provide a basis for the on site treatment of school cluster vomiting diarrhea events.

Objective:To investigate the current status of the traditional Chinese medicine (TCM) nursing knowledge-attitude-practice of newly recruited nurses in TCM hospitals, so as to provide evidence for improving the standardized training of TCM nursing among newly-recruited nurses.Methods:From March to April in 2020, a total of 800 newly-recruited nurses from 6 TCM hospitals above the municipal level in Hunan Province were selected as research objects by convenient sampling method. The investigation was conducted by TCM Nursing Knowledge-Attitude-Practice Questionnaire. A total of 800 questionnaires were distributed and 768 valid questionnaires were collected, with an effective recovery rate of 96%.Results:The total score of the Knowledge-Attitude-Practice Questionnaire of the newly recruited nurses of the TCM hospital was (191.61±41.80) ; the scores of knowledge and attitude dimension were (73.93±17.21) and (78.17±15.81) , at a middle level; the score of practice dimension was (39.51±8.78) , at a low level. There were significant differences in the total score of the questionnaire among the new nurses with different hospital levels, working years, whether they graduated from a college of TCM and whether they had received the training of TCM nursing knowledge-attitude-practice ( P<0.01) . Conclusions:The newly recruited nurses in TCM hospitals have a high level of TCM nursing knowledge and a positive attitude towards TCM nursing. However, in clinical work, TCM nursing has poor application ability and insufficient use of clinical skills. In-depth study of TCM nursing knowledge should be strengthened, focusing on the cultivation of TCM nursing thinking. It is also important to improve laws, regulations and related rules, and to conduct training according to the different characteristics of new nurses.

ObjectiveTo investigate the clinical effect and safety of transcatheter arterial chemoembolization (TACE) combined with microwave ablation (MWA) in the treatment of advanced primary liver cancer. MethodsA total of 186 patients with advanced primary liver cancer who were treated in The Fifth Medical Center of Chinese PLA General Hospital from April 2015 to June 2019 were enrolled and divided into study group and control group using a random number table, with 93 patients in each group. Both groups of patients underwent TACE, and the patients in the study group were treated with ultrasound-guided percutaneous MWA. The two groups were compared in terms of clinical outcome and complications. Quantitative real-time PCR was used to measure the serum level of microRNA-202 (miR-202), ELISA was used to measure the serum levels of fragile histidine triad (FHIT) and P16 protein, and the changes in the above three indices at 3 months after treatment were compared. The two-independent-samples t test was used for comparison of continuous data between two groups, and the paired t-test was used for comparison within one group before and after treatment; The chi-square testwas used for comparison of categorical data between groups. ResultsThe study group had a significantly higher objective response rate than the control group (47.32% vs 27.96%, χ2=7.422, P=0.006), and there was no significant difference in disease control rate between the two groups(P＞0.05). Both groups had significant increases in the serum levels of miR-202, FHIT, and P16 protein at 3 months after treatment (all P＜0.05), and compared with the control group, the study group had significantly higher serum levels of miR-202 (0.84±0.14 vs 0.58±017, t=11.385, P＜0.001), FHIT (1126.35±73.05 pg/ml vs 762.87±56.71 pg/ml, t=37.904, P＜0.001), and P16 protein (52.86±651 pg/ml vs 39.06±5.37 pg/ml, t=15.770, P＜0.001). ConclusionUltrasound-guided MWA in addition to TACE can improve the short-term response of patients with advanced primary liver cancer and increase the serum levels of miR-202, FHIT, and P16 protein, with relatively high safety.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Objective: To examine the value and clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer. Methods: Totally 124 patients with advanced gastric cancer who underwent radical gastrectomy plus D2 lymphadenectomy at Affiliated Hospital of Qingdao University from July 2016 to December 2018 were selected in the study. According to the chronological order, the first 80 cases were served as learning group. The remaining 44 cases were served as verification group. There were 45 males and 35 females in the study group, with average age of 57.6 years. There were 29 males and 15 females in the validation group, with average age of 9.2 years. The pre-training convolutional neural network architecture Resnet50 was trained and fine-tuned by 21 352 patches with cancer areas and 14 997 patches without cancer areas in the training group. A total of 78 whole-slide image served as a test dataset including positive (n=38) and negative (n=40) lymph nodes. The convolutional neural network computer-aided detection (CNN-CAD) system was used to analyze the ability of convolutional neural network system to screen metastatic lymph nodes at the level of slice by setting threshold, and evaluate the system′s classification accuracy by calculating its sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Results: The classification accuracy of CNN-CAD system at slice level was 100%.The AUC for the CNN-CAD system was 0.89. The sensitivity was 0.778, specificity was 0.995, overall accuracy was 0.989. Positive and negative predictive values were 0.822 and 0.994, respectively. The CNN-CAD system achieved the same classification results as pathologists. Conclusions The CNN-CAD system has been constructed to distinguished benign and malignant lymph node slides with high accuracy and specificity. It could achieve the similar classification results as pathologists.