Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective:To construct an early warning model for gestational diabetes mellitus (GDM) based on baseline data, vitamin D (VitD) , and thyroid function status.Methods:A prospective study was conducted to select 126 patients with GDM (GDM group) and 126 pregnant women without GDM (control group) admitted to the Obstetrics Department of Qinhuangdao First Hospital from Jan. 2022 to May. 2024. The single-factor and multi-factor LASSO Logistic regression analysis was used to analyze the influencing factors of the risk of GDM. Based on the results of the multi-factor analysis, an early warning model for GDM was constructed, evaluated, and validated.Results:Age, pre-pregnancy body mass index (BMI) , family history of diabetes, thyroid function, low density lipoprotein (LDL-C) , triglyceride (TG) , VitD, fasting plasma glucose (FPG) , glycated hemoglobin (HbA1c) and blood uric acid were compared, and the differences were statistically significant ( P < 0.05) . LASSO Logistic regression analysis showed that family history of diabetes, hypothyroidism, pre-pregnancy BMI, TG, VitD, FPG, HbA1c and blood uric acid were independently correlated with the risk of GDM ( P < 0.05) . A GDM early warning model was constructed based on the results of multiple factors, with a C-index of 0.876, indicating good predictive performance; The model evaluation and validation results show that the model has good internal and external calibration, high consistency between predicted values and actual observed values, and good predictive value and discrimination in external data sets. Conclusions:Baseline data such as hypothyroidism, VitD, and pre-pregnancy BMI are independent factors that affect the occurrence of GDM. The early warning model for GDM based on these indicators has good predictive performance and clinical applicability, and can be used as an effective model for early prediction of GDM in clinical practice, as well as guiding clinical prevention and treatment efforts.

Objective:To construct an early warning model for gestational diabetes mellitus (GDM) based on baseline data, vitamin D (VitD) , and thyroid function status.Methods:A prospective study was conducted to select 126 patients with GDM (GDM group) and 126 pregnant women without GDM (control group) admitted to the Obstetrics Department of Qinhuangdao First Hospital from Jan. 2022 to May. 2024. The single-factor and multi-factor LASSO Logistic regression analysis was used to analyze the influencing factors of the risk of GDM. Based on the results of the multi-factor analysis, an early warning model for GDM was constructed, evaluated, and validated.Results:Age, pre-pregnancy body mass index (BMI) , family history of diabetes, thyroid function, low density lipoprotein (LDL-C) , triglyceride (TG) , VitD, fasting plasma glucose (FPG) , glycated hemoglobin (HbA1c) and blood uric acid were compared, and the differences were statistically significant ( P < 0.05) . LASSO Logistic regression analysis showed that family history of diabetes, hypothyroidism, pre-pregnancy BMI, TG, VitD, FPG, HbA1c and blood uric acid were independently correlated with the risk of GDM ( P < 0.05) . A GDM early warning model was constructed based on the results of multiple factors, with a C-index of 0.876, indicating good predictive performance; The model evaluation and validation results show that the model has good internal and external calibration, high consistency between predicted values and actual observed values, and good predictive value and discrimination in external data sets. Conclusions:Baseline data such as hypothyroidism, VitD, and pre-pregnancy BMI are independent factors that affect the occurrence of GDM. The early warning model for GDM based on these indicators has good predictive performance and clinical applicability, and can be used as an effective model for early prediction of GDM in clinical practice, as well as guiding clinical prevention and treatment efforts.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

BACKGROUND: China bears the biggest atrial fibrillation (AF) burden in the world. However, little is known about the incidence and predictors of AF. This study aimed to investigate the current incidence of AF and its electrocardiographic (ECG) predictors in general community individuals aged over 60 years in China. METHODS: This was a prospective cohort study, recruiting subjects who were aged over 60 years and underwent annual health checkups from April to July 2015 in four community health centers in Songjiang District, Shanghai, China. The subjects were then followed up from 2015 to 2019 annually. Data on sociodemographic characteristics, medical history, and the resting 12-lead ECG were collected. Kaplan-Meier curve was used for showing the trends in AF incidence and calculating the predictors of AF. Associations of ECG abnormalities and AF incidence were examined using Cox proportional hazard models. RESULTS: This study recruited 18,738 subjects, and 351 (1.87%) developed AF. The overall incidence rate of AF was 5.2/1000 person-years during an observation period of 67,704 person-years. Multivariable Cox regression analysis indicated age (hazard ratio [HR], 1.07; 95% confidence interval [CI]: 1.06-1.09; P < 0.001), male (HR, 1.30; 95% CI: 1.05-1.62; P = 0.018), a history of hypertension (HR, 1.55; 95% CI: 1.23-1.95; P < 0.001), a history of cardiac diseases (HR, 3.23; 95% CI: 2.34-4.45; P < 0.001), atrial premature complex (APC) (HR, 2.82; 95% CI: 2.17-3.68; P < 0.001), atrial flutter (HR, 18.68; 95% CI: 7.37-47.31; P < 0.001), junctional premature complex (JPC) (HR, 3.57; 95% CI: 1.59-8.02; P = 0.002), junctional rhythm (HR, 18.24; 95% CI: 5.83-57.07; P < 0.001), ventricular premature complex (VPC) (HR, 1.76; 95% CI: 1.13-2.75, P = 0.012), short PR interval (HR, 5.49; 95% CI: 1.36-22.19; P = 0.017), right atrial enlargement (HR, 6.22; 95% CI: 1.54-25.14; P = 0.010), and pacing rhythm (HR, 3.99; 95% CI: 1.57-10.14; P = 0.004) were independently associated with the incidence of AF. CONCLUSIONS The present incidence of AF was 5.2/1000 person-years in the studied population aged over 60 years in China. Among various ECG abnormalities, only APC, atrial flutter, JPC, junctional rhythm, short PR interval, VPC, right atrial enlargement, and pacing rhythm were independently associated with AF incidence.
Humans ; Male ; Middle Aged ; Aged ; Atrial Fibrillation/epidemiology* ; Prospective Studies ; Incidence ; Atrial Flutter/complications* ; Risk Factors ; China/epidemiology* ; Electrocardiography

Objective:To understand the cognition and attitudes of Emergency Department nurses towards palliative care, and to explore the factors that hinder or promote the development of palliative care in the Emergency Department, so as to provide basic data for emergency palliative care.Methods:From March to June 2021, semi-structured interviews were conducted with 11 nurses in the Emergency Department of Peking Union Medical College Hospital using the purposeful sampling. The interview data were organized and analyzed using Colaizzi's 7-step analysis method.Results:A total of three themes were induced. The themes were that Emergency Department patients and their families were often caught in decision-making dilemmas, and Emergency Department nurses had a high degree of acceptance of palliative care, but there were barriers to implementation in the Emergency Department, and the implementation methods of emergency palliative care needed to be further explored, and Emergency Department nurses were willing to participate in palliative care training.Conclusions:Emergency palliative care needs to consider the characteristics of the Emergency Department work environment and explore appropriate implementation methods. Training related to palliative care can be increased, the palliative care literacy of Emergency Department nurses can be improved, so as to provide effective support for life-limited patients and their families.

Objective:To understand the cognition and attitudes of Emergency Department nurses towards palliative care, and to explore the factors that hinder or promote the development of palliative care in the Emergency Department, so as to provide basic data for emergency palliative care.Methods:From March to June 2021, semi-structured interviews were conducted with 11 nurses in the Emergency Department of Peking Union Medical College Hospital using the purposeful sampling. The interview data were organized and analyzed using Colaizzi's 7-step analysis method.Results:A total of three themes were induced. The themes were that Emergency Department patients and their families were often caught in decision-making dilemmas, and Emergency Department nurses had a high degree of acceptance of palliative care, but there were barriers to implementation in the Emergency Department, and the implementation methods of emergency palliative care needed to be further explored, and Emergency Department nurses were willing to participate in palliative care training.Conclusions:Emergency palliative care needs to consider the characteristics of the Emergency Department work environment and explore appropriate implementation methods. Training related to palliative care can be increased, the palliative care literacy of Emergency Department nurses can be improved, so as to provide effective support for life-limited patients and their families.

BACKGROUND: Chromosomal abnormalities are important causes of ventriculomegaly (VM). In mild and isolated cases of fetal VM, obstetricians rarely give clear indications for pregnancy termination. We aimed to calculate the incidence of chromosomal abnormalities and incremental yield of chromosomal microarray analysis (CMA) in VM, providing more information on genetic counseling and prognostic evaluation for fetuses with VM. METHODS: The Chinese language databases Wanfang Data, China National Knowledge Infrastructure, and China Biomedical Literature Database (from January 1, 1991 to April 29, 2020) and English language databases PubMed, Embase, and Cochrane Library (from January 1, 1945 to April 29, 2020) were systematically searched for articles on fetal VM. Diagnostic criteria were based on ultrasonographic or magnetic resonance imaging (MRI) assessment of lateral ventricular atrium width: ≥10 to <15 mm for mild VM, and ≥15 mm for severe VM. Isolated VM was defined by the absence of structural abnormalities other than VM detected by ultrasonography or MRI. R software was used for the meta-analysis to determine the incidence of chromosomal abnormalities and incremental yield of CMA in VM, and the combined rate and 95% confidence interval (CI) were calculated. RESULTS: Twenty-three articles involving 1635 patients were included. The incidence of chromosomal abnormalities in VM was 9% (95% CI: 5%-12%) and incremental yield of CMA in VM was 11% (95% CI: 7%-16%). The incidences of chromosomal abnormalities in mild, severe, isolated, and non-isolated VM were 9% (95% CI: 4%-16%), 5% (95% CI: 1%-11%), 3% (95% CI: 1%-6%), and 13% (95% CI: 4%-25%), respectively. CONCLUSIONS Applying CMA in VM improved the detection rate of abnormalities. When VM is confirmed by ultrasound or MRI, obstetricians should recommend fetal karyotype analysis to exclude chromosomal abnormalities. Moreover, CMA should be recommended preferentially in pregnant women with fetal VM who are undergoing invasive prenatal diagnosis. CMA cannot completely replace chromosome karyotype analysis.
Chromosome Aberrations ; Chromosomes ; Female ; Fetus ; Humans ; Hydrocephalus ; Karyotyping ; Microarray Analysis ; Pregnancy ; Prenatal Diagnosis ; Retrospective Studies ; Ultrasonography, Prenatal

Objective To review the failure patterns and clinical outcomes for patients with cervical esophageal carcinoma (CEC) undergoing definitive radiotherapy (RT).Methods Medical records,clinical characteristics and outcomes of patients with CEC treated by definitive RT from August 2008 to May 2017 were retrospectively reviewed and analyzed.Results A total of 97 patients with squamous cell CEC were enrolled in this study with a median age of 59 years old (range 18-78 years old).There were 34 patients with limited cervical esophagus,and 63 patients with diseases beyond cervical region,respectively.There were 69,7,and 6 patients with Bronchi invasion,thyroid lobes involvement and aortic involvement,respectively.There were 11,80 and 6 patients with stage Ⅱ,Ⅲ and Ⅳ (non-regional lymph node metastases),respectively.The median dose to the gross tumor volume (GTV) was 66 Gy,in which 46 patients received above 66 Gy and 51 patients received less than 66 Gy,respectively.The median progression free survival (PFS) and overall survival (OS) were 16.03 and 23.30 months,respectively,with a median follow-up of 14.90 months.The 1,2,3-year PFS and OS were 56.86％,30.35％,26.34％,and 72.54％,47.94％,40.81％,respectively.Sixty-one patients had treatment failure at their last follow-up,in which 40,27,and 18 patients developed local failure,regional failure,and distant metastasis,respectively.Univariate analysis revealed that thyroid lobes involvement resulted in lower PFS (x2 =5.773,P＜0.05) and OS (x2 =13.461,P＜0.05),and bronchi involvement (x2 =4.283,P＜0.05) was associated with lower OS.Multivariate analysis indicated that aortic involvement and thyroid lobes involvement were associated with lower PFS (x2 =6.796,4.548,P＜0.05) and OS (x2 =13.421,10.581,P＜0.05),and GTV dose above 66 Gy was associated with higher OS (x2=5.296,P＜0.05).Conclusions Local-regional recurrence was the main failure pattern for patients with CEC after definitive RT.Aortic,thyroid lobes,and/or bronchi involvement were associated with poor prognosis,and GTV dose ≥66 Gy tended to improve OS.Prospective studies with larger population were needed to further confirm this study.

Objective? To investigate the current status and influencing factors on career decision-making self-efficacy among near-employment internship nursing students. Methods? By convenience sampling, the near-employment internship nursing students who voluntarily participant in this research were selected from April to May of 2018 from a ClassⅢ Grade A hospital in Beijing. The Career Decision-Making Self-Efficacy Scale (CDMSE) was used in the study to investigate the participants. A total of 178 questionnaires were distributed with 170 valid ones retrieved. Results? The total score of CDMSE of 170 near employment internship nursing students was (137.62±2.240). The linear-regression analysis showed that gender and satisfaction on hospitals were the influencing factors of their career decision-making self-efficacy. Conclusions? The career decision-making self-efficacy of internship nursing students is at an intermediate level. Hospitals and schools can develop individualized training methods for them in order to improve their career decision-making self-efficacy.