For the infertility patients undergoing IVF, LDG could alleviate clinical symptoms, improve rates of high quality oocytes and embryos, so as to raise clinical pregnancy rate. The mechanism may be through regulating proteome expression in the follicular fluid to improve the developmental microenvironment for oocytes which would lead to a successful embryo implantation.

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings. Diabetes, as a group of metabolic disorders characterized by hyperglycemia, can lead to many serious, long-term complications 1-3. The global epidemic of diabetes currently affects more than 440 million people. The Asia-pacific region has the highest number of people with diabetes, and the prevalence of diabetes in this region has risen sharply in recent decades 4-6. With a population of 1.38 billion and about 110 million people with diabetes, China now has the largest number of diabetes in the world 7 and the number continues to grow, placing a huge burden on the health care system. In the 2013 study, which included 170,287 participants, the prevalence of diabetes was 10.9 percent, with 60 percent not knowing they had been diagnosed with diabetes. In addition, an additional 35.7% of the population found abnormal glucose homeostasis, highlighting the large number of people at risk for Diabetes 8. The reasons for the missed diagnosis are on the one hand the lack of self-awareness of disease management, on the other hand, it is caused by the inaccuracy of diabetes results (only by checking the fasting blood glucose) 9,10. The ideal way is to check the fasting blood glucose and the two-hour blood glucose value after the oral glucose tolerance test (OGTT) at the same time. However, universal access to blood sugar testing seems unlikely in Northwest China, where the medical standards are poor. The prevalence of diabetes in Xinjiang is at a high level. According to the 2018 national health examination data of Xinjiang, the detection rate in Urumqi is the highest, reaching 13.9%. Research has proved that a healthy lifestyle and a reasonable diet structure can effectively delay or prevent the occurrence of type||diabetes mellitus (T2DM)...

Existing studies primarily explored chronic obstructive pulmonary disease (COPD) in smokers, whereas the clinical characteristics and the disease course of passive or nonsmokers have been rarely described. In the present study, patients hospitalized and diagnosed as acute exacerbation of COPD (AECOPD) were recruited and followed up until being discharged. Clinical and laboratory indicators were ascertained and delved into. A total of 100 patients were covered, namely, 52 active smokers, 34 passive smokers, and 14 nonsmokers. As revealed from the results here, passive or nonsmokers developed less severe dyspnea (patients with modified Medical Research Council scale (mMRC) <2, 0.0% vs. 8.8% vs. 14.3%, p < 0.05, active, passive, and nonsmokers, respectively), higher oxygenation index (206.4 ± 45.5 vs. 241.2 ± 51.1 vs. 242.4 ± 41.8 mmHg, p < 0.01), as well as lower arterial partial pressure of carbon dioxide (70.8 ± 12.7 vs. 58.85 ± 9.9 vs. 56.6 ± 6.5 mmHg, p < 0.001). Despite lower treatment intensity over these patients, amelioration of dyspnea, mitigation of cough, and elevation of oxygenation index were comparable to those of active smokers. However, in terms of patients exhibiting mMRC ≥2 and type 2 respiratory failure, amelioration of dyspnea was more common in nonsmokers as compared with passive smokers (46.4% vs. 83.3%, p < 0.05, passive and nonsmokers, respectively). In terms of patients exhibiting Global Initiative for COPD severity <3, mMRC ≥2, and type 2 respiratory failure, active smokers achieved the least mitigation of cough symptom (8.7% vs. 35.0% vs. 44.4%, p < 0.05). Similar results could be achieved after the effects of confounders were excluded, with the most prominent amelioration of dyspnea (odds ratio (OR) 3.8, 95% confidence interval (CI) 1.1–13.6, p < 0.05, as compared with active smokers) and cough (OR 3.3, 95% CI 1.0–10.7, p < 0.05) in nonsmokers, and relatively better amelioration of hypoxemia in passive smokers (oxygenation index change, 39.0 ± 34.6 vs. 51.5 ± 32.4 vs. 45.3 ± 25.4 mmHg, p < 0.05). In brief, passive or nonsmokers with AECOPD were subjected to less severe disease, and nonsmokers, especially patients with more severe disease, might achieve the optimal enhancement of clinical presentation after treatment.

Objectives Compared with unaffected individuals, patients with type 2 diabetes (T2DM) have higher risk of hypertension, and diabetes combined with hypertension can lead to server cardiovascular disease. Therefore, the purpose of this study was to establish a simple nomogram model to identify the determinants of hypertension in patients with T2DM and to quickly calculate the probability of hypertension in individuals with T2DM. Materials and methods A total of 643,439 subjects participating in the national physical examination has been recruited in this cross-sectional study. After excluding unqualified subjects, 30,507 adults with T2DM were included in the final analysis. 21,355 and 9,152 subjects were randomly assigned to the model developing group and validation group, respectively, with a ratio of 7:3. The potential risk factors used in this study to assess hypertension in patients with T2DM included questionnaire investigation and physical measurement variables. We used the least absolute shrinkage and selection operator models to optimize feature selection, and the multivariable logistic regression analysis was for predicting model. Discrimination and calibration were assessed using the receiver operating curve (ROC) and calibration curve. Results The results showed that the major determinants of hypertension in patients with T2DM were age, gender, drinking, exercise, smoking, obesity and atherosclerotic vascular disease. The area under ROC curve of developing group and validation group are both 0.814, indicating that the prediction model owns high disease recognition ability. The p values of the two calibration curves are 0.625 and 0.445, suggesting that the nomogram gives good calibration. Conclusion The individualized nomogram model can facilitate improved screening and early identification of patients with hypertension in T2DM. This procedure will be useful in developing regions with high epidemiological risk and poor socioeconomic status just like Urumqi, in Northern China.

Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.