Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective:To analyze pathogenic characteristics of viral diarrhea in children aged <5 years in Hebei Province and provide reference for the prevention and control of viral diarrhea in children.Methods:Stool samples were collected from in-patients with diarrhea under five years old from sentinel hospitals in Lulong County of Hebei between 2010 and 2020. ELISA detected rotavirus antigen, and then positive samples were genotyped by semi nested reverse transcription PCR of two rounds. Calicivirus, genotyping astrovirus, and adenovirus were detected by real-time fluorescence quantification PCR. The data were analyzed by using software SPSS 20.0.Results:In 2 925 detected stool samples, 1 919 (65.61%) were positive. The positive rates of rotavirus, calicivirus, adenovirus, and astrovirus were 42.80% (1 252/2 925), 22.12% (647/2 925), 6.19% (181/2 925), 3.56% (104/2 925). Viral diarrhea was mainly caused by rotavirus infection, accounting for 59.30% (1 017/1 715) between 2010 and 2017, and by calicivirus infection accounting for 53.43% (109/204) between 2018 and 2020. The peak positive rate of rotavirus occurred in winter, with the highest rate in infants aged 12 to 17 months (52.96%,483/912). In the rotavirus positive samples, G9P[8] was mainly detected strains (58.31%,730/1 252), followed by G3P[8] (8.15%,102/1 252). The calicivirus-positive samples were mainly infected with norovirus GⅡ. Sequence analysis indicated that the main type was GⅡ.4 [P31] between 2011 and 2016 and GⅡ.3 [P12] in 2018.Conclusions:Rotavirus and calicivirus were the main pathogens causing infant diarrhea in children under five years old in Hebei from 2010 to 2020. Winter was the main epidemic season.

Objective:To construct the clinical practice pathway for narrative medicine in traditional Chinese medicine(TCM),with a view to providing clinical practice guidelines for narrative medicine in TCM for frontline practitioners.Methods:Using the realistic literature review and the Nominal Group Technique(NGT),the paper systematically sorted out the practices of humanistic care in ancient Chinese medical books and famous medical cases,as well as constructed the first draft of the clinical practice pathway and details for narrative medicine in TCM.Subsequently,experts from multiple fields were invited to demonstrate by using the NGT.After in-depth discussion and collective voting,various operational modules,and their detailed rules and supporting tools were determined,thus completing the construction of the entire practical pathway.Results:A complete set of clinical practice pathways for narrative medicine in TCM had been established.It encompassed six core modules,including"start of diagnosis and treatment","communication of disease conditions","diagnosis and explanation","joint decision-making","end of diagnosis and treatment",and"reflection and summary".Besides,detailed operating rules and supporting tools were also provided.Conclusion:The clinical practice pathway for narrative medicine in TCM integrates the humanistic spirit of TCM,the core concepts of narrative medicine,and the communication skills of psychology,providing medical workers with standardized,procedural,and operationally flexible practice guidance,which helps both doctors and patients to better communicate,empathize,and make joint decisions throughout the entire process of diagnosis and treatment.

【Objective:】 To explore the practice experience of narrative medicine among postgraduates majoring in Traditional Chinese Medicine (TCM) and its impact on their professional identity. 【Methods:】 A total of 56 first-year master’s and doctoral degree students majoring in TCM at a university were selected through purposive sampling for a 16-hour narrative medicine course training and 4 weeks of clinical practice in narrative medicine. They were asked to complete an open-ended writing assignment on their practical experience after this clinical practice. Subsequently, the topic analysis method in qualitative research was used to analyze their writing contents. 【Results:】 After learning narrative medicine, the students’ practical experience in clinical practice included 3 major themes and 9 sub-themes: wide application range (all diagnosis and treatment stages, multiple departments, and disease types), integration of multiple communication skills and diagnosis and treatment methods (communication skills, psychological techniques, reflection, and TCM humanistic diagnosis and treatment methods), and increasing professional identity (achieving mutual healing, building the harmonious doctor-patient relationship, and enhancing confidence in TCM). The themes were further integrated to form the relationship between the narrative medical practice cycle and professional identity, and presented in the form of charts. 【Conclusion:】 By exploring the narrative medical practice experience among TCM postgraduates, it was found that through the use of multiple methods and techniques in the entire diagnosis and treatment stage and multi-disciplinary diseases, it is possible to harmonize the doctor-patient relationship, achieve mutual healing, and enhance confidence in TCM. These internalized recognition and sense of achievement contributes to improving professional identity, which may provide new ideas for the development of professional identity for medical students in China and the exploration of the localization of narrative medical practice.

Objective:To explore the lag effect and correlation between daily average temperature and the incidence of other infectious diarrhea in Hebei Province.Methods:The data of meteorological factors (including temperature and average daily temperature) and the daily incidence of other infectious diarrhea in Zhangjiakou City, Chengde City, Tangshan City, Qinhuangdao City, Baoding City, Langfang City, Cangzhou City, Shijiazhuang City, Hengshui City, Xingtai City, and Handan City in Hebei Province from 2017 to 2020 were collected. Eleven prefecture-level cities were divided into four regions, including east, north, middle and south regions. Distributed lag non-linear model was applied to examine the non-linear associations and the lag effect of daily mean temperature on daily incidence of other infectious diarrhea. Meanwhile, the cold and hot effects were used to estinuate the lag-response relationship on the incidence of other infectious diarrhea.Results:A total of 231 008 cases of other infectious diarrhea were reported in Hebei Province. The seasonal distribution was obvious, showing a bimodal distribution of large peaks in summer and small peaks in winter. An inverse S-shaped association between average daily temperature and cumulative risk ratio ( RR) of other infectious diarrhea was observed in Hebei Province. Both high temperature (higher than 27.50 ℃) and low temperature (less than 13.67 ℃) could increase the risk of other infectious diarrhea. When the temperature was lower than 13.00 ℃, the lag time and RR had a U-shaped association (lag four to seven days, 23 to 30 days). Meanwhile, when it was higher than 13.00 ℃, it had an inverted U-shaped association (lag 5 to 21 days). A comparison of four regions of Hebei Province showed that the lag time from south to north was extended from six days to 30 days at low temperature effects with temperature P5=-7.24 ℃. When the temperature getting hot ( P95=28.25 ℃), the risk occurred at lag 0 days, and the lag time gradually got short from north to south. Thus, the high temperature effect reached maximum quickly with a relative short duration. Conclusions:The inverse S-shaped non-linear association between daily average temperature and the incidence of other infectious diarrhea in Hebei Province is observed. Both low temperature and high temperature are associated with increased risk of other infectious diarrhea. But the impact of low temperature is more notable, which has a relative long duration.

Objective:To explore the self-management trajectory of patients with percutaneous coronary intervention (PCI) , in order to provide better guidance for disease self-management of patients with PCI.Methods:From April to December in 2019, a total of 15 patients with PCI who were treated and followed up in a Class Ⅲ Grade A hospital in Harbin and a Class Ⅲ Grade A hospital in Tianjin were selected as the research objects. All 15 patients were interviewed and recorded. Then the records were transcribed and analyzed with open coding, axial coding and selective coding according to the grounded theory put forward by Strass and Corbin, to identify the categories and core category.Results:The core category of disease self-management of patients undergoing PCI was "crisis-conscious path of PCI patient self-management", that was, the path of PCI patient self-management presented a dynamic process with the change of disease risk cognition. When patients were aware of the disease risk, they would enter the path of active health management, and when patients were unaware of the disease risk or risk cognition declines, they would enter the path of ignoring the disease self-management.Conclusions:Medical staff should pay attention to and strengthen the early postoperative risk awareness education of PCI patients; quantify patients' cognitive needs of health education and give targeted health education intervention measures; strengthen supervision and management outside the hospital, and establish the concept of lifelong self-management for patients.

Objective:To understand the influence and lag effect of meteorological factors on the incidence of hand, foot and mouth disease (HFMD) in Shijiazhuang.Methods:The daily incidence data of HFMD in Shijiazhuang during 2017-2019 were collected from Chinese Information System for Disease Control and Prevention. The hourly meteorological data were collected form meteorological stations of Shijiazhuang of Chinese meteorological data network. The distributed lag nonlinear model was built for statistical analysis by software R 3.6.2.Results:When the daily average temperature was 15-26 ℃, the risk of incidence of HFMD increased at lag 3-6 days. However, the risk was highest when the temperature was 25 ℃ at lag 3 days ( RR=1.03,95% CI:1.00-1.06). When the daily average relative humidity was more than 80%, the risk of incidence of HFMD increased at lag 5-18 days. However, the risk was highest at lag 9 days ( RR=1.04, 95% CI: 1.02-1.06).When the daily average air pressure ranged from 999 hPa to 1 007 hPa, the risk of incidence of HFMD increased at lag 5-8 days. However, the risk was highest at lag 6 days ( RR=1.01, 95% CI: 1.00-1.02).When the daily average precipitation ranged from 15 to 32 mm, the risk of incidence of HFMD increased at lag 3-18 days. However, the risk was highest at lag 6 days ( RR=1.11, 95% CI: 1.02-1.19). Conclusions:Meteorological factors increased the risk of incidence of HFMD such as higher daily average temperature (15-26 ℃), higher daily average humidity (>80%), lower daily average air pressure (999-1 007 hPa) and higher daily average precipitation (15-32 mm) in Shijiazhuang during 2017-2019. They were all correlated with the incidence of HFMD with certain lag days. It is suggested to use these meteorological indicators for the early warning of HFMD.

Objective:To understand the epidemiological characteristics of cases with severe and fatal hand, foot, and mouth disease (HFMD) caused by other enterovirus in Hebei province, 2013-2017. Genetic characteristics of the main pathogen cosackie virus A6 (CoxA6) were also analyzed to further clarifying the characteristics and rules of genetic evolution on this virus.Methods:Descriptive epidemiological methods were used to analyze the distribution of severe and fatal cases with HFMD caused by other enterovirus in Hebei, 2013-2017. The VP1 sequences of CoxA6 were phylogenetically analyzed, using the Mega 5.2 software package.Results:A total of 86 severe and fatal cases with HFMD caused by other enterovirus were reported, accounting for 1.12%, comparing to all the HFMD caused by other enterovirus. Cases began to rise in April, and peaked in May-July. 65.12% of the cases occurred in children between 1 and 5 years old. The sex ratio between male and female was 1.39∶1. A total of 93.02% of the cases were children outside the child care settings. A total of 39 positive strains were identified, with positive isolation rate as 45.35%. Phylogenetic analysis on the VP1 sequences of CoxA6 strains in this study revealed that CoxA6 strains belonged to sub-genotypes D3a and D3b.Conclusions:Severe and fatal HFMD cases that caused by other enterovirus in Hebei province was with seasonal feature, consistent with the overall trend of this disease, 2013-2017. No new evolutionary branch appeared in the CoxA6 strain.

Objective To preliminarily analyze the prediction efficiency of multimodal MRI?based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery?proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray?level co?occurrence matrix(GLCM), the run?length matrix(RUN), the absolute gradient(GRA),the auto?regressive model(ARM) and wavelets transform (WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross?validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI?based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI?based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI?based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10?fold cross?validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s?4) of T2WI, S(4, 4) inverse difference of gray uniformity measurement (InvDfMom), S(5, 0) Entropy (entropy), WavEnHH_s?4 of T1WI enhancement, S(1, 1) InvDfMom, S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity,sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI?based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI?based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.