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 mortality risk factors in patients with severe pneumonia and investigate their varying influences across different time periods.Methods:A total of 134 patients with severe pneumonia admitted to the Emergency Department of Xiang’an Hospital, Xiamen University, between June 2019 and February 2020 were enrolled. All patients were treated in the EICU and followed up for four years. Based on outcomes, they were categorized into a death group ( n=77) and a survival group ( n=57). COX regression analysis was employed to identify mortality risk factors at different time points, while logistic regression analysis was used to assess risk factors influencing mortality during hospitalization, ICU stay, 1-month, and 1-year follow-up periods. Results:Mortality rates were 11.9% ( n=16) during ICU admission, 20.8% ( n=28) during hospitalization, 16.4% ( n=22) within 1 month, and 31.3% ( n=42) within 1 year. By the end of the follow-up, 57.4% ( n=77) of patients had died. Ten mortality risk factors were identified, with the number increasing over time. During ICU admission and hospitalization, significant risk factors included total bilirubin levels, APACHE-II score, invasive ventilation, ARDS, and vasopressor use in the ICU. One-month mortality risk additionally involved bacterial infection. One-year mortality risk further incorporated advanced age and chronic heart failure. By the end of follow-up, acute kidney injury (AKI) during ICU admission also emerged as a contributing factor, while higher body weight was identified as a protective factor. Conclusions:The number of mortality risk factors in severe pneumonia patients increases progressively over time. Early-stage factors during hospitalization and ICU admission exert a stronger impact on short-term mortality, whereas bacterial infection, advanced age, and chronic heart failure become increasingly significant in later stages. These findings highlight the dynamic nature of risk factors and underscore the importance of tailored monitoring and intervention strategies at different disease phases.

Objective To evaluate the prognostic value of blood lactate (Lac) level in sepsis patients with or without diabetes.Methods 106 patients admitted to intensive care unit (ICU) of Zhongshan Hospital Affiliated to Fudan University from April 2015 to November 2016 were enrolled. The patients with age > 18 years and the length of hospital stay > 24 hours were included. Records including blood Lac, serum creatinine (SCr), white blood cell count (WBC), platelet count (PLT), sequential organ failure assessment (SOFA) on the first day of admission; minimum oxygen index (PaO2/FiO2) in 3 days after admission; mechanical ventilation, whether there was a history of diabetes, usage of biguanides, etiology control treatment, usage of continuous renal replacement therapy (CRRT) were collected. According to the level of blood Lac patients were divided into high Lac group (Lac > 2 mmol/L) and low Lac group (Lac ≤ 2 mmol/L);based on their diabetic history, sepsis patients were divided into the diabetes group and non-diabetes group. The survival curve of each group was analyzed by Kaplan-Meier regression analysis, and the factors influencing the prognosis were analyzed by multivariate Cox regression analysis.Results There were 76 males and 30 females sepsis patients, with an average age of (68.1±14.7) years old. In the 51 patients of low Lac group, there were 7 patients who suffered from diabetes. While in the 55 patients of high Lac group, there were 12 patients who suffered from diabetes. Compared with low Lac group, high Lac group had a higher age, higher SOFA score, and a lower proportion of patients who had the treatment of etiology control (allP < 0.05). There was no significant difference of blood Lac in sepsis patients with diabetes and those without diabetes (mmol/L: 3.03±2.73 vs. 2.81±2.40,P > 0.05). Kaplan-Meier survival curve analysis showed that the 90-day survival rate in the high Lac group was significantly lower than that in the low Lac group (56.36% vs. 90.20%,χ2 = 0.697,P = 0.008). The high Lac group without diabetes had lower survival rate, and the 90-day survival rate was significantly lower than that of the low Lac group without diabetes (58.14% vs. 90.90%,χ2 = 7.152,P = 0.007); there was no significant difference in 90-day survival rate between the high Lac group and the low Lac group with diabetes (50.00% vs. 85.71%,χ2 = 0.012,P = 0.914). Multivariate Cox regression analysis showed that blood Lac was an independent risk factor for the prognosis of sepsis patients [odds ratio (OR) = 3.863, 95% confidence interval (95%CI) = 1.237-12.060,P = 0.020]. After stratification according to their diabetic history, the blood Lac was an independent risk factor for the prognosis of sepsis patients without diabetes (OR = 4.816, 95%CI = 1.407-15.824, P = 0.010), but the blood Lac had no effect on the prognosis of sepsis patients with diabetes (OR = 0.000, 95%CI =0.000-1.103,P = 0.270).Conclusions The predictive value of blood Lac on sepsis patients with or without diabetes was different. The blood Lac was related with the prognosis of sepsis patients without diabetes, while further study should be conducted for the prognostic value of blood Lac in sepsis patients with diabetes, and it's possible to increase the cut-off-point of Lac level in these patients.

This paper retrospectively analyzed nursing care of 20 critically ill patients with human infections of avian influenza A(H7N9) virus treated by oxygen therapy.According to the severity of hypoxia in patients admitted to the hospital,individualized oxygen therapy strategy was selected,such as humidified high flow nasal cannula or mechanical ventilation.Oxygen therapy strategy was adjusted in a timely manner according to patients' condition,such as prone position ventilation and extracorporeal membrane oxygenation.As a resuh,15 cases were transferred to the general ward when the virus associated test was negative,and 5 cases died.

Objective To study the effect of multimedia on the home care to the patients with double lumen catheter.Methods Totally 100 patients with double lumen catheter for dialysis were divided into control group and observation group according to the registration order,50 cases in each group.In the control group,the home care was done to nurse the catheter and in the observation group,multimedia was used to guide home care to the catheter.The incidence of catheter-related complications was compared between two groups.Result The toal rate of catheter-related complications in the observed group was significantly lower than that of the control group (P＜0.05).Conclusion The multimedia for home care to the catheter for dialysis are easy for the patients to master so that the can change better their home care and avoid (or decrease) catheter-related complications.

Mechano growth factor (MGF) is an autocrine/paracrine factor and sensitive to mechanical stimulation. MGF can be highly expressed in various soft tissues under physical stimuli, biochemistry stimuli or in damaged situation. MGF may "compensate" the stress for tissue in the processing of tissue repair. MGF can effectively accelerate the repair of the soft tissue by promoting the proliferation, migration and differentiation of cells. This paper summarizes the MGF expressions in different soft tissues and their functions in soft tissue repair. The paper also discusses current problems and challenges in using MGF to repair the soft tissue.
Cell Differentiation ; Cell Proliferation ; Humans ; Insulin-Like Growth Factor I ; physiology ; Soft Tissue Injuries ; Wound Healing