Influence of meteorological factors on the seasonal onset of esophagogastric variceal bleeding

Purpose: To investigate the influence of meteorological factors on the esophagogastric variceal bleeding. The rhythmicity and variation mechanism of the onset of esophagogastric variceal bleeding were determined by large sample study. Methods: 572 patients with esophagogastric variceal bleeding confirmed by endoscopy were enrolled in the study, and the gender, age, onset date and Child-Pugh grading of liver function were recorded, the meteorological data were provided by the Shiyan Meteorological Bureau, which included temperature, air pressure, air speed, precipitation, sunshine duration and so on. Results: The onset numbers in the four seasons were 130, 122, 144 and 176, respectively, and differences of the onset number in different seasons were significant (X2 = 11.888, p = 0.008), and the onset number in winter reached to maximum, while it decreased to minimum in summer. The results of Child-Pugh grading were as follows: Grade A 113 (19.8%), Grade B 234 (40.9%), and Grade C 225 (39.3%). There was no significance among the different grades by crosstabs analysis (X2 = 4.463, p = 0.107). The Spearman correlation analysis concluded the result of (r > 0 and p ?C accumulated temperature. The p value was more than0.01 inthe other factors. Conclusion: The onset of esophagogastric variceal bleeding was rhythmical, which rose to the maximum in winter and decreased to minimum in summer. The onset of the disease correlated positively with daily air pressure (mean, maximal, minimal), daily mean temperature, ten days’ air pressure (mean, daily difference, maximal, minimal and range) and ten days’ temperature range, and correlated negatively with daily maximal temperature, daily minimal temperature, ten days’ temperature (mean, maximal and minimal) and ten days’ ≥0°C accumulated temperature, and had no relationship with other factors. The mechanism of the onset may associate with the increase of portal venous flow through vasoconstriction induced by chill factors. It also may be the variation of air pressure which influenced the intraesophageal pressure and led to intraesophageal hemangiectasia that may increase the risk of bleeding.

Learning to Complete Knowledge Graphs with Deep Sequential Models

Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of (head, relation, tail). Traditional KG completion methods compel two- thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchmark data sets. Furthermore, the deep sequential characteristic of our method enables it to predict the relations given head (or tail) only, and even predict the whole triples. Our experiments on these two new KG completion tasks demonstrate that our method achieves superior performance compared with several alternative methods.