Effects of nutritional nursing intervention based on glycemic load for patient with gestational diabetes mellitus

Objectives: To determine the effects of nutritional nursing intervention based on glycemic load (GL) for patients with gestational diabetes mellitus. Material and methods: One hundred thirty-four patients diagnosed with gestational diabetes mellitus at our hospital were selected from March 2015 to March 2017 and randomly divided into the observation (n = 67) and control groups (n = 67). All of the patients in the observation and control groups received conventional nutritional nursing. In addition, the patients in the observation group received nutritional nursing intervention based on GL. The changes in blood glucose levels and pregnancy outcomes were compared between the two groups after intervention. Results: There were significant differences in fasting blood glucose (FBG) and the 2h postprandial glucose (2hPG) levels between the two groups (P < 0.05). There was a lower incidence of premature delivery, fetal macrosomia, eclampsia, preg- nancy hypertension syndrome, and fetal distress in the observation group. Conclusions: Nutritional nursing intervention based on GL is more effective than traditional nutritional nursing for patients with gestational diabetes, and can effectively control the blood glucose level, reduce the incidence of pregnant complica- tions, and improve the pregnancy outcome. Thus, nutritional nursing intervention based on GL deserves to be popularized.

Flashlight-Net : A Modular Convolutional Neural Network for Motor Imagery EEG Classification

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Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information

Many data analysis tasks heavily rely on a deep understanding of tables (multi-dimensional data). Across the tasks, there exist comonly used metadata attributes of table fields / columns. In this paper, we identify four such analysis metadata: Measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. While those metadata face challenges of insufficient supervision signals, utilizing existing knowledge and understanding distribution. To inference these metadata for a raw table, we propose our multi-tasking Metadata model which fuses field distribution and knowledge graph information into pre-trained tabular models. For model training and evaluation, we collect a large corpus (~582k tables from private spreadsheet and public tabular datasets) of analysis metadata by using diverse smart supervisions from downstream tasks. Our best model has accuracy = 98%, hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four analysis metadata inference tasks, respectively. It outperforms a series of baselines that are based on rules, traditional machine learning methods, and pre-trained tabular models. Analysis metadata models are deployed in a popular data analysis product, helping downstream intelligent features such as insights mining, chart / pivot table recommendation, and natural language QA...Comment: 13pages, 7 figures, 9 table

Hyperuricemia and severity of coronary artery disease: An observational study in adults 35 years of age and younger with acute coronary syndrome

Background: Coronary artery disease (CAD) in adults ≤ 35 years of age is rare, but the incidence is on the rise and the risk factors for this age group are largely uncertain. Previous studies have shown that hyperuricemia (HUA) is an independent risk factor for CAD in the general population, whereas the role in adults ≤ 35 years of age with acute coronary syndrome (ACS) is unclear. Methods: Patients, 18–35 years of age, diagnosed with ACS for the first time at the documented institu- tion between January 2005 and December 2015, were enrolled in the current study. The severity of CAD was assessed by the Gensini score. Patients were divided into two groups according to the definition of HUA. The relationship between HUA and CAD severity was assessed based on multi-variate analysis.  Results: Seven hundred seventy-one participants fulfilling the criteria were included in this study (mean age, 31.6 years; 94.4% male). HUA, which was defined as a serum uric acid level ≥ 7.0 mg/dL (420μmol/L) in males and ≥ 6.0 mg/dL (357 μmol/L) in females, accounted for 37% of the participants. Multivariate analysis identified that HUA is an independent risk factor of CAD severity, as assessed by the Gensini score, in very young adults with ACS (OR 8.28; 95% CI 1.96–14.59; p = 0.01), and the effect of HUA on CAD severity was second only to diabetes mellitus. Conclusions: Hyperuricemia was shown to be an independent risk factor for CAD severity in young adults with ACS (18–35 years of age)