Eye-tracking technology monitors eye movement trajectories to reveal the cognitive mechanisms underlying visual behavior.With advantages like objectivity and real-time capability,it is increasingly applied in nursing.However,research and application in China are still in the early stages.This article reviews the development,measurement metrics,methods,and impact of eye-tracking in nursing,analyzes current challenges,and suggests solutions to aid its development in the field of nursing in China.

Eye-tracking technology monitors eye movement trajectories to reveal the cognitive mechanisms underlying visual behavior.With advantages like objectivity and real-time capability,it is increasingly applied in nursing.However,research and application in China are still in the early stages.This article reviews the development,measurement metrics,methods,and impact of eye-tracking in nursing,analyzes current challenges,and suggests solutions to aid its development in the field of nursing in China.

Purpose: This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods: This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results: PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P<0.05). For each 1 m/s increase in left, right, and bilateral mean PWV-ES, the risk of subAS increased by 23% (95% confidence interval [CI], 1.04 to 1.46), 26% (95% CI, 1.07 to 1.52), and 38% (95% CI, 1.12 to 1.72), respectively. According to ROC analyses, predictive potential was found for left PWV-ES (cutoff value=7.910 m/s, P=0.002), right PWV-ES (cutoff value=6.615 m/s, P=0.003), and bilateral mean PWV-ES (cutoff value=7.415 m/s, P<0.001), but not for PWV-BS (all P>0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS.

Purpose: This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods: This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results: PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P<0.05). For each 1 m/s increase in left, right, and bilateral mean PWV-ES, the risk of subAS increased by 23% (95% confidence interval [CI], 1.04 to 1.46), 26% (95% CI, 1.07 to 1.52), and 38% (95% CI, 1.12 to 1.72), respectively. According to ROC analyses, predictive potential was found for left PWV-ES (cutoff value=7.910 m/s, P=0.002), right PWV-ES (cutoff value=6.615 m/s, P=0.003), and bilateral mean PWV-ES (cutoff value=7.415 m/s, P<0.001), but not for PWV-BS (all P>0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS.

Purpose: This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods: This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results: PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P<0.05). For each 1 m/s increase in left, right, and bilateral mean PWV-ES, the risk of subAS increased by 23% (95% confidence interval [CI], 1.04 to 1.46), 26% (95% CI, 1.07 to 1.52), and 38% (95% CI, 1.12 to 1.72), respectively. According to ROC analyses, predictive potential was found for left PWV-ES (cutoff value=7.910 m/s, P=0.002), right PWV-ES (cutoff value=6.615 m/s, P=0.003), and bilateral mean PWV-ES (cutoff value=7.415 m/s, P<0.001), but not for PWV-BS (all P>0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS.

To develop a blue cap anticoagulant tube blood volume measuring card of to solve the problem of insufficient or excessive blood collection in clinical coagulation specimens.The device was composed of a measuring card,a transparent housing with a base and a tube holder.The measuring card was divided into qualified and unqualified areas,the housing was used to insert the card,the tube holder was used to place blood collection tubes.The device was used by clinical nurses to judge the adequacy of blood collection volume in blue cap anticoagulant tube.After the use of the device,the failure rate of clinical blue cap anticoagulation tube specimens submission was reduced from 6.71‰ to 2.73‰,shortened the time limit for specimen submission.At the same time,the device made the rejection judgment of department specimens more standardized and avoided the acceptance of unqualified specimens caused by subjective judgment errors.The device has simple structure,convenient operation and strong practicability,and has promotion value.

Purpose: This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods: This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results: PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P<0.05). For each 1 m/s increase in left, right, and bilateral mean PWV-ES, the risk of subAS increased by 23% (95% confidence interval [CI], 1.04 to 1.46), 26% (95% CI, 1.07 to 1.52), and 38% (95% CI, 1.12 to 1.72), respectively. According to ROC analyses, predictive potential was found for left PWV-ES (cutoff value=7.910 m/s, P=0.002), right PWV-ES (cutoff value=6.615 m/s, P=0.003), and bilateral mean PWV-ES (cutoff value=7.415 m/s, P<0.001), but not for PWV-BS (all P>0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS.

Purpose: This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods: This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results: PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P<0.05). For each 1 m/s increase in left, right, and bilateral mean PWV-ES, the risk of subAS increased by 23% (95% confidence interval [CI], 1.04 to 1.46), 26% (95% CI, 1.07 to 1.52), and 38% (95% CI, 1.12 to 1.72), respectively. According to ROC analyses, predictive potential was found for left PWV-ES (cutoff value=7.910 m/s, P=0.002), right PWV-ES (cutoff value=6.615 m/s, P=0.003), and bilateral mean PWV-ES (cutoff value=7.415 m/s, P<0.001), but not for PWV-BS (all P>0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS.

Objective:To explore the effect of early essential newborn care technology during cesarean section on early health outcomes of mothers and infants and breast feeding.Methods:This study was a randomized controlled study. From October 2020 to August 2021, 104 delivery women and their newborns who were scheduled for full-term cesarean section in Peking University First Hospital were selected as the study subject. The subjects were randomly divided into control group and intervention group, 52 cases in each group. The control group received routine nursing of the newborn after cesarean section, while the intervention group received early essential newborn care technology after cesarean section. The amount of postpartum hemorrhage, hospital stay, Apgar score at one and five minutes after birth, the incidence of abnormal conditions within 90 minutes after birth, the time of umbilical cord shedding, the time of the first feeding sign of the newborn, the success rate of early initiation of breastfeeding and the implementation of breast feeding at discharge were compared between the two groups.Results:There was no statistical difference between the two groups in the amount of postpartum hemorrhage within two hours, the amount of postpartum hemorrhage within two to 24 hours and the number of days in hospital ( P>0.05) . There was no statistical difference between the two groups in Apgar scores at one and five min after birth ( P>0.05) . There was no statistical difference between the two groups in the occurrence of re-attraction, respiratory abnormalities, resuscitation, Neonatal Intensive Care Unit (NICU) transfer and other abnormalities within 90 minutes after birth ( P>0.05) . There was no statistical difference in the time of umbilical cord shedding between the two groups ( P>0.05) , and no umbilical infection occurred. The first foraging reflex in the intervention group was earlier than that in the control group, and the success rate of early initiation of breastfeeding was higher than that in the control group, and the differences was statistically significant ( P<0.05) . There was no statistical difference in the implementation of breast feeding between the two groups at discharge ( P>0.05) . Conclusions:It is safe and feasible to implement early essential newborn care technology during cesarean section, and it can effectively promote early initiation of breastfeeding, which is of great significance for breast feeding and has the value of clinical promotion.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms