Objective:To construct univariate and multivariate relapse free survival (RFS) prediction models for breast cancer patients with diabetes mellitus type 2 (T2DM) and to compare and select the model with higher predictive performance.Methods:A total of 912 breast cancer patients treated at the First Affiliated Hospital of Dalian Medical University from January 2010 to December 2016 were included, of which 202 patients had T2DM and 710 patients did not. Kaplan-Meier survival curve was drawn based on whether patients had T2DM, and log-rank test was performed based on whether patients had T2DM. All patients were randomly divided into a training set ( n=640) and a validation set ( n=272) at a ratio of 7∶3. Univariate and multivariate Cox proportional risk regression models were used to analyze RFS in breast cancer patients with the survival package. The "rms" package was employed to construct univariate and multivariate RFS prediction models for breast cancer patients with T2DM. Clinical decision curves and calibration curves were used to validate the models. The receiver operator characteristic (ROC) curve was used to compare and analyze the prediction performance of the two models. Results:There were no statistically significant differences between the training set and the validation set patients in terms of age, T2DM, surgical approach, axillary management methods, T stage, N stage, molecular sub-type, estrogen receptor (ER) 1, ER2, progesterone receptor (PR) , ER and PR consistency, Ki67, human epidermal growth factor receptor 2 (HER2) (all P>0.05) . There was a statistically significant difference in histological grade ( χ2=7.59, P=0.022) . Survival analysis showed that the 5-year RFS rate was 83.7% in patients with T2DM and 92.3% in patients without T2DM ( χ2=16.61, P<0.001) . Univariate analysis revealed that age ( HR=1.04, 95% CI: 1.03-1.06, P<0.001) , T2DM ( HR=2.31, 95% CI: 1.49-3.55, P<0.001) , surgical approach ( HR=2.39, 95% CI: 1.20-4.77, P=0.013) , axillary management methods ( HR=2.62, 95% CI: 1.72-3.98, P<0.001) , T stage (T 2: HR=2.13, 95% CI: 1.36-3.31, P<0.001; T 3: HR=6.90, 95% CI: 3.35-14.22, P<0.001) , N stage (N 2: HR=3.87, 95% CI: 2.12-7.07, P<0.001; N 3: HR=8.61, 95% CI: 4.71-15.75, P<0.001) , molecular sub-type (Luminal B: HR=2.74, 95% CI: 1.17-6.36, P=0.019; HER2 +: HR=3.64, 95% CI: 1.38-9.58, P=0.009; TNBC: HR=4.40, 95% CI: 1.71-11.34, P=0.002) , ER1 (>10%: HR=0.57, 95% CI: 0.37-0.90, P=0.016) , ER2 ( HR=0.57, 95% CI: 0.37-0.89, P=0.015) , and PR ( HR=0.56, 95% CI: 0.37-0.86, P=0.008) were all factors influencing RFS in breast cancer patients. Multivariate analysis demonstrated that age ( HR=1.04, 95% CI: 1.02-1.06, P<0.001) , T2DM ( HR=1.82, 95% CI: 1.16-2.85, P=0.009) , T stage (T 2: HR=1.60, 95% CI: 1.01-2.54, P=0.046; T 3: HR=2.64, 95% CI: 1.22-5.72, P=0.014) , N stage (N 2: HR=3.72, 95% CI: 2.01-6.88, P<0.001; N 3: HR=5.34, 95% CI: 2.78-10.25, P<0.001) , and ER1 (>10%: HR=0.63, 95% CI: 0.39-0.99, P=0.046) were independent factors influencing RFS in breast cancer patients. Based on the 10 and 5 variables with P<0.05 in the univariate and multivariate analyses respectively, the nomograms of the univariate and multivariate prediction models were constructed to evaluate the influence of factors such as T2DM on the postoperative RFS of breast cancer patients. Clinical decision curves and calibration curves indicated that both models had high predictive value for RFS in breast cancer patients, and the predictive results were highly consistent with the actual observed results. ROC curve analysis showed that there was no statistically significant difference in the area under the curve (AUC) of the two models for predicting the RFS rates of breast cancer patients in the training set and validation set at 36, 60, and 84 months (all P>0.05) , indicating that the predictive efficacy of the two models was comparable. The multivariate model is more suitable for clinical application because it uses fewer variables. Conclusions:Breast cancer patients with T2DM have poorer prognosis. Age, T2DM, T stage, N stage, and ER1 are independent factors influencing postoperative RFS in breast cancer patients. The multi-factor prediction model of RFS in breast cancer patients based on T2DM is more suitable for clinical application due to its higher predictive efficacy and fewer variables.

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 explore the relationship between normal serum creatinine(Scr) level and diabetic kidney disease(DKD) in patients with type 2 diabetes mellitus(T2DM).Methods:This was a prospective cohort study. Patients with yet not DKD who were regularly followed up at six centers of Li′s United Clinic in Taiwan, China from January 1, 2002 to December 31, 2018 were selected. At baseline, clinic information and lab tests were collected. According to whether the patients developed DKD during the follow-up period, they were divided into DKD group and non-DKD(NDKD) group. The exposure factor was the Scr(μmol/L) value, and it was used as a categorical variable. According to the quartiles of Scr, they were divided into 4 groups: Q1 group(Scr<57.68 μmol/L), Q2 group(57.68 μmol/L≤Scr<68.51 μmol/L), Q3 group(68.51 μmol/L≤Scr<80.44 μmol/L) and Q4 group(Scr≥80.44 μmol/L). The Cox regression model was used to explore the relationship between Scr level and the incidence of DKD. Receiver operating characteristic(ROC) curve was used to analyze the predictive effect of normal level Scr on DKD. Results:A total of 2 202 T2DM patients without DKD at baseline were included. After a follow-up period of(5.2±2.17) years, there were 966 patients in the DKD group and 1 236 patients in the NDKD group. Compared with the NDKD group, the DKD group had older age, longer duration of diabetes, higher BMI, SBP, DBP, LDL-C, Scr, and UACR(all P<0.05). Cox regression analysis results showed that compared with the Q1 group as the reference, the risk of developing DKD in the Q2, Q3, and Q4 groups after adjusting for confounding factors was 1.394, 1.688, and 2.821 times higher, respectively(all P<0.05). ROC curve analysis results showed that the area under the curve(AUC) for predicting DKD occurrence using normal serum creatinine level was 0.70(95% CI 0.678-0.722), with an optimal cutoff value of 74.27 μmol/L, sensitivity of 0.54, and specificity of 0.76. The cumulative risk plot showed that after adjusting for confounding factors, patients in the Q4 group had a higher cumulative risk of developing DKD compared to the Q1, Q2, and Q3 groups(all P<0.05). Conclusion:Scr is an independent risk factor for developing DKD in patients with T2DM. The higher the Scr level, the greater the risk, especially when Scr is greater than 74.27 μmol/L.

Objective:To construct an evidence-based prediction model for early recurrence after surgery of hepatocellular carcinoma (HCC) based on Meta-analysis and to do external validation study.Methods:The literatures in Chinese National Knowledge Infrastructure, Wanfang, VIP, Chinese Science Citation Database (CSCD), Chinese Social Science Citation System (CCSCI), PubMed, Web of Science and IEEE databases between January 2019 and December 2023 were searched based on the subject words. According to the inclusion and exclusion criteria, 9 literatures were included to screen the risk factors affecting the early recurrence of HCC. When the same risk factor was found in ≥5 included literatures, Meta-analysis was performed by using Review Manager 5.4.1 software. External validation data were collected from 401 patients with primary HCC who underwent surgery in Liaoning Cancer Hospital between March 2014 and March 2017. The patients were divided into early recurrence group (176 cases) and early non-recurrence group (225 cases) according to whether they relapsed 2 years after surgery. The OR values of all risk factors obtained in the Meta-analysis were converted into modeling, and postoperative early recurrence rate of HCC in the Meta-analysis was used to calculate β 0, and finally the logistic model was obtained. The OR value was incorporated into the logit (P) model, and the morbidity (P) of the external validation data was calculated. Taking the recurrence 2 years after surgery or not as the dependent variable and P as the independent variable, the receiver operating characteristic (ROC) curve was drawn to calculate the area under the curve (AUC). Results:A total of 8 risk factors for early HCC recurrence were screened out from 9 literatures (x 1: alpha-fetoprotein ≥ 400 ng/ml; x 2: tumor number ≥ 2; x 3: the longest tumor diameter ≥ 5 cm; x 4: Barcelona staging B-C; x 5: microvascular invasion; x 6: moderate to low differentiation; x 7: incomplete capsule; x 8: nonanatomic hepatectomy). The Meta-analysis included 1 757 HCC cases, with 960 postoperative early recurrences and an early recurrence rate of 45.36%, finally the β 0 value was -0.201. The predictive model for 2-year recurrence of HCC was constructed and calculated as logit (P) = -0.201+0.835x 1+0.905x 2+0.783x 3+1.008x 4+0.765x 5+0.831x 6+1.533x 7+0.940x 8. Analysis of variance by external validation data showed that the differences in ascites, alpha-fetoprotein, tumor number, tumor diameter, Barcelona staging, microvascular invasion, tumor differentiation degree, capsule invasion, resection type, and systemic inflammation index were statistically significant between early recurrence group and early non-recurrence group (all P < 0.05). ROC curve analysis showed that AUC of postoperative early recurrence of HCC predicted by the model was 0.718, (95% CI: 0.689-0.753), the optimal cut-off value was 3.11, the Yoden index was 0.288, the sensitivity was 69.32%, and the specificity was 69.56%. Conclusions:The evidence-based prediction model constructed based on Meta-analysis for postoperative early recurrence of HCC has a high predictive value. However, further verification and optimization with big data is still needed.

Objective To explore the predictive effect of empowerment and theory of planned behavior(TPB)on the four core behaviors of type 2 diabetes mellitus(T2DM)self-management,and analyze the influence path of TPB model and behavioral intention on the four core behaviors.Methods A total of 500 T2DM patients who were hospitalized in the Endocrinology Department of three tertiary general hospitals in different provinces from December 2022 to May 2023 were selected for investigation.Hierarchical multiple regression analysis was used to explore the predictive effects of empowerment and TPB models on self-management behaviors.Bootstrap method was used to analyze the influence path of TPB model and behavior intention on self-management behaviors.Results Empowerment had a predictive effect on self-management behavior,which was improved after the addition of TPB,with medication R2=0.194,blood glucose monitoring R2=0.308,regular diet R2=0.337 and regular exercise R2=0.343,respectively.Mediation effect analyses revealed that attitude,subjective norm,and perceived behavioral control were observed to predict behavior intention across the four behaviors,and the three except through the behavior intention indirectly affect behavior,can also directly affect the behavior.Conclusions Empowerment can predict self-management behavior,and adding TPB to this can improve the prediction effect.Attitude,subjective norms and perceived control can indirectly or directly affect self-management behavior through behavioral intention.

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.

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.

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