BACKGROUND/AIMS: Several studies have demonstrated that sleep problems are associated with functional gastrointestinal disorders (FGIDs): irritable bowel syndrome (IBS), functional dyspepsia, etc, but the relationship between excessive daytime sleepiness (EDS) and FGIDs has not been systematically studied in the general population. This study aims to explore the relationship between EDS and specific types of FGIDs and the effect of the number of FGIDs on EDS. METHODS: A sample of 3600 individuals (aged 18–80 years) was selected from 5 regions in China using a randomized, stratified, multi-stage sampling method. EDS was measured by the Epworth Sleepiness Scale, while gastroesophageal reflux disease (GERD) and other FGIDs were assessed by Reflux Disease Questionnaire and the Rome II diagnostic criteria, respectively. RESULTS: The survey was completed by 2906 individuals (response rate: 80.72%), and 644 individuals (22.16%) had EDS. EDS was significantly associated with ulcer-like dyspepsia (OR, 2.50; 95% CI, 1.08–5.79), diarrhea-predominant IBS (OR, 2.00; 95% CI, 1.09–3.66), alternating IBS (OR, 2.32; 95% CI, 1.30–4.13), functional constipation (OR, 1.68; 95% CI, 1.20–2.35), and GERD (OR, 1.72; 95% CI, 1.08–2.72). Risk of EDS increased along with the increasing numbers of FGIDs: with 1 FGID (OR, 1.72; 95% CI, 1.37–2.15); with 2 FGIDs (OR, 2.43; 95% CI, 1.63–3.62); and with 3 or more FGIDs (OR, 3.26; 95% CI, 1.37–7.78). CONCLUSIONS: FGIDs, such as ulcer-like dyspepsia, diarrhea-predominant IBS, alternating IBS, functional constipation, and GERD, were significantly associated with EDS. Those who suffered from more kinds of FGIDs were more susceptible to EDS.
China* ; Constipation ; Disorders of Excessive Somnolence ; Dyspepsia ; Epidemiology ; Gastroesophageal Reflux ; Gastrointestinal Diseases* ; Irritable Bowel Syndrome ; Methods

Objective To evaluate cardiovascular benefits in patients with type 2 diabetes mellitus treated with the marketed 11 sodium-glucose co-transporter-2 (SGLT-2) inhibitors and glucagon-like polypeptide-1 (GLP-1) receptor agonism by Bayesian network meta-analysis system. Methods MEDLINE, Embase and Cochrane Library were searched from the establishment of the database to 18 July 2020. The endpoint of the study was adverse cardiovascular events. The effect measures were hazard ratios (HR) and 95% credible intervals (CI). Results Compared with placebo, empagliflozin, canagliflozin, dapagliflozin, albiglutide, dulaglutide, exenatide, liraglutide, semaglutide reduced the risk of major adverse cardiovascular events in patients with type 2 diabetes with HR and 95% CI ranging between 0.75(0.60-0.95)~0.90(0.82-0.99); The risk of heart failure was reduced by empagliflozin, canagliflozin, dapagliflozin and ertugliflozin, with HR and 95%CI ranging between 0.64(0.49-0.82)~0.74(0.65-0.85); Empagliflozin, canagliflozin, dapagliflozin, exenatide, liraglutide and oral semaglutide reduced the incidence of all-cause mortality with HR and 95%CI ranging between 0.52(0.33-0.84)~0.89(0.80-0.99); Empagliflozin, canagliflozin, liraglutide and oral semaglutide can reduce the risk of cardiovascular death events, with HR and 95% CI ranging between 0.54(0.30-0.95)~0.83(0.71-0.96) . Conclusion The order of the cardiovascular benefits of SGLT-2 inhibitors or GLP-1 receptor agonists in patients with type 2 diabetes mellitus complicated with atherosclerotic cardiovascular disease are canagliflozin (the best), empagliflozin, dulaglutide, liraglutide; for patients with type 2 diabetes and heart failure. The order of the cardiovascular benefits for patients with type 2 diabetes and heart failure are empagliflozin, canagliflozin, ertugliflozin, and dapagliflozin.

Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
Humans ; Blood Pressure/physiology* ; Blood Pressure Determination/methods* ; Algorithms ; Hypertension/diagnosis* ; Sexually Transmitted Diseases