Objective: To analyze the results of colorectal cancer screening among residents in Baoshan District, Shanghai Municipality from 2013 to 2021, so as to provide the basis for promoting colorectal cancer screening and prevention. Methods: Permanent residents aged 50 to 74 years in Baoshan District from 2013 to 2021 were selected as the screening population. The initial screening was conducted using a risk assessment form and fecal occult blood test. Positive results on either the risk assessment form or fecal occult blood test were considered positive for the initial screening. Participants with positive initial screening results were invited to undergo colonoscopy. The positive rate of the initial screening, colonoscopy compliance rate, and colonoscopy results were analyzed. Results: A total of 264 907 individuals underwent the initial colorectal cancer screening in Baoshan District from 2013 to 2021, with 65 333 individuals (24.66%) testing positive. Among them, the positive rate of the risk assessment form was 12.16%, and the positive rate of fecal occult blood test was 14.64%. A total of 14 473 individuals completed colonoscopy, with a compliance rate of 22.15%. A total of 1 284 precancerous lesions were detected, with a detection rate of 8.87%, and 386 cases of colorectal cancer were identified, with a detection rate of 2.67%. The positive rate of the initial screening, colonoscopy compliance rate, precancerous lesion detection rate, and colorectal cancer detection rate were higher in males than in females (25.55% vs. 24.06%, 23.12% vs. 21.45%, 11.60% vs. 6.74%, 3.62% vs. 1.93%, all P<0.05). With increasing age, the positive rate of the initial screening increased, the colonoscopy compliance rate decreased, the precancerous lesion detection rate and colorectal cancer detection rate increased (all P<0.05). From 2013 to 2021, the positive rate of the initial screening among residents showed a downward trend, while the colonoscopy compliance rate showed an upward trend (both P<0.05). Conclusions The detection rate of precancerous lesions in colorectal cancer was 8.87%, and the detection rate of colorectal cancer was 2.67% in Baoshan District from 2013 to 2021. Male and older individuals were the key populations for screening, and the colonoscopy compliance among residents needs to be improved.

Objective:To investigate the correlation relationship between self-esteem and imposter phenomenon among nurses,and to provide reference for optimizing nurse team.Methods:A total of 836 nurses were selected from February to March 2023 in the Yongzhou Central Hospital of Hunan Province and the People′s Hospital of Longhua, Shenzhen. A cross-sectional survey was conducted on nurses using the General Data Questionnaire, Impostor Phenomenon Scale and Self-Esteem Scale.Results:The imposter phenomenon score of nurses was (48.97 ± 12.58) points and the self-esteem score was (28.93 ± 3.86) points. The total score of self-esteem was negatively correlated with the total score of imposter phenomenon ( r= -0.433, P<0.01). Multivariate linear regression analysis showed that with the increase of self-esteem score, the score of imposter phenomenon decreased ( B=-1.402, P<0.01). Self-esteem was an important factor affecting the imposter phenomenon among clinical nurses, accounting for 18.9% of the total variation. Conclusions:The self-esteem and imposter phenomenon of clinical nurses are both at a moderate level, the improvement of self-esteem is beneficial to decreased their imposter phenomenon.

Objective:To evaluate the relationship between hemoglobin(Hb) level and the risk of diabetic retinopathy(DR) in patients with type 2 diabetes mellitus(T2DM).Methods:This study was a prospective cohort study. A total of 1 730 T2DM patients without DR, who received regular management at the Li′s Clinic in Taiwan, China starting from 2002, were selected as the study population. All patients underwent annual dilated fundus examination by professional ophthalmologists. General patient information and laboratory results were collected and analyzed. Based on the occurrence of DR during patient follow-up, patients were divided into the DR group and the non-DR(NDR) group. The impact of Hb levels on DR was explored using a generalized linear mixed model, and the relationship between Hb levels and DR was studied using Cox proportional hazards regression model.Results:After an average follow-up of 9.79 years, 481 patients with DR were detected. Compared with NDR group, DR group displayed a longer course of diabetes, higher rates of cataract, insulin use, and anemia, and higher systolic blood pressure, HbA 1C, and UACR as well as lower Hb. The results of the generalized linear mixed model showed a negative correlation between Hb and the occurrence of DR( β=-0.015, P<0.001). The Cox proportional hazards regression model showed that, after adjusting for confounding variables and based on quartiles of average Hb levels during follow-up, the risk of developing DR increased by 56.9% in the Q1 group(Hb≤127 g/L) compared to the Q4 group(Hb≥142 g/L). The cumulative risk plot showed that, after adjusting for confounding variables, the Q1 group had the highest cumulative risk of developing DR, and the difference was statistically significant( P<0.05). Conclusion:Hb was negatively correlated with DR, and the lower Hb levels were associated with the occurrence of DR, independent of other influencing factors.

Objective: This study used cardiac magnetic resonance imaging (MRI) to compare the characteristics of left ventricular remodeling in patients with primary aldosteronism (PA) with those of patients with essential hypertension (EH) and healthy controls (HCs). Materials and Methods: This prospective study enrolled 35 patients with PA, in addition to 35 age- and sex-matched patients with EH, and 35 age- and sex-matched HCs, all of whom underwent comprehensive clinical and cardiac MRI examinations. The analysis of variance was used to detect the differences in the characteristics of left ventricular remodeling among the three groups. Univariable and multivariable linear regression analyses were used to determine the relationships between left ventricular remodeling and the physiological variables. Results: The left ventricular end-diastolic volume index (EDVi) (mean ± standard deviation [SD]: 85.1 ± 13.0 mL/m2 for PA, 75.9 ± 14.3 mL/m2 for EH, and 77.3 ± 12.8 mL/m2 for HC; p = 0.010), left ventricular end-systolic volume index (ESVi) (mean ± SD: 35.2 ± 9.8 mL/m2 for PA, 30.7 ± 8.1 mL/m2 for EH, and 29.5 ± 7.0 mL/m2 for HC; p = 0.013), left ventricular mass index (mean ± SD: 65.8 ± 16.5 g/m2 for PA, 56.9 ± 12.1 g/m2 for EH, and 44.1 ± 8.9 g/m2 for HC; p < 0.001), and native T1 (mean ± SD: 1224 ± 39 ms for PA, 1201 ± 47 ms for EH, and 1200 ± 44 ms for HC; p = 0.041) values were higher in the PA group compared to the EH and HC groups. Multivariable linear regression demonstrated that log (plasma aldosteroneto-renin ratio) was independently correlated with EDVi and ESVi. Plasma aldosterone was independently correlated with native T1. Conclusion Patients with PA showed a greater degree of ventricular hypertrophy and enlargement, as well as myocardial fibrosis, compared to those with EH. Cardiac MRI T1 mapping can detect left ventricular myocardial fibrosis in patients with PA.

Objective: This study used cardiac magnetic resonance imaging (MRI) to compare the characteristics of left ventricular remodeling in patients with primary aldosteronism (PA) with those of patients with essential hypertension (EH) and healthy controls (HCs). Materials and Methods: This prospective study enrolled 35 patients with PA, in addition to 35 age- and sex-matched patients with EH, and 35 age- and sex-matched HCs, all of whom underwent comprehensive clinical and cardiac MRI examinations. The analysis of variance was used to detect the differences in the characteristics of left ventricular remodeling among the three groups. Univariable and multivariable linear regression analyses were used to determine the relationships between left ventricular remodeling and the physiological variables. Results: The left ventricular end-diastolic volume index (EDVi) (mean ± standard deviation [SD]: 85.1 ± 13.0 mL/m2 for PA, 75.9 ± 14.3 mL/m2 for EH, and 77.3 ± 12.8 mL/m2 for HC; p = 0.010), left ventricular end-systolic volume index (ESVi) (mean ± SD: 35.2 ± 9.8 mL/m2 for PA, 30.7 ± 8.1 mL/m2 for EH, and 29.5 ± 7.0 mL/m2 for HC; p = 0.013), left ventricular mass index (mean ± SD: 65.8 ± 16.5 g/m2 for PA, 56.9 ± 12.1 g/m2 for EH, and 44.1 ± 8.9 g/m2 for HC; p < 0.001), and native T1 (mean ± SD: 1224 ± 39 ms for PA, 1201 ± 47 ms for EH, and 1200 ± 44 ms for HC; p = 0.041) values were higher in the PA group compared to the EH and HC groups. Multivariable linear regression demonstrated that log (plasma aldosteroneto-renin ratio) was independently correlated with EDVi and ESVi. Plasma aldosterone was independently correlated with native T1. Conclusion Patients with PA showed a greater degree of ventricular hypertrophy and enlargement, as well as myocardial fibrosis, compared to those with EH. Cardiac MRI T1 mapping can detect left ventricular myocardial fibrosis in patients with PA.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

AIM: To analyze the clinical characteristics of anticoagulant rat poisoning and vitamin K