Serum lipid profile and its association with hypertension in Bangladesh

BACKGROUND: Hypertension and dyslipidemia are major risk factors for cardiovascular disease, accounting for the highest morbidity and mortality among the Bangladeshi population. The objective of this study was to determine the association between serum lipid profiles in hypertensive patients with normotensive control subjects in Bangladesh. METHODS: A cross-sectional study was carried out among 234 participants including 159 hypertensive patients and 75 normotensive controls from January to December 2012 in the National Centre for Control of Rheumatic Fever and Heart Disease in Dhaka, Bangladesh. Data were collected on sociodemographic factors, anthropometric measurements, blood pressure, and lipid profile including total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL), and high density lipoprotein (HDL). RESULTS: The mean (± standard deviation) systolic blood pressure and diastolic blood pressure of the participants were 137.94±9.58 and 94.42±8.81, respectively, which were higher in the hypertensive patients (P<0.001). The serum levels of TC, TG, and LDL were higher while HDL levels were lower in hypertensive subjects compared to normotensives, which was statistically significant (P<0.001). Age, waist circumference, and body mass index showed significant association with hypertensive patients (P<0.001) but not with normotensives. The logistic regression analysis showed that hypertensive patients had 1.1 times higher TC and TG, 1.2 times higher LDL, and 1.1 times lower HDL than normotensives, which was statistically significant (P<0.05). CONCLUSION: Hypertensive patients in Bangladesh have a close association with dyslipidemia and need measurement of blood pressure and lipid profile at regular intervals to prevent cardiovascular disease, stroke, and other comorbidities

Weight loss surgery for obstructive sleep apnoea with obesity in adults: a systematic review and meta-analysis protocol

Introduction Obstructive sleep apnoea (OSA) is causedby complete or partial obstruction of the upper airwayresulting in repeated episodes of interrupted or shallowbreaths. OSA is associated with significant morbidity andmortality. The prevalence is estimated to range from 3%to 7% in the general population but may be much higher.Several studies show that weight loss or bariatric surgerymay have a role in treating OSA. The aim of this systematicreview is to assess the safety and efficacy of randomisedcontrolled trials (RCTs) of weight loss surgery for adultswith OSA and comorbid obesity.Methods and analysis A search of the Cochrane CentralRegister of Controlled Trials, PubMed, EMBASE and twomajor Chinese biomedical databases will be performedto identify related trials published as of October 2018.This study will include RCTs, comparing different typesof weight loss surgery for OSA with obesity or weightloss surgery for OSA with obesity with other upper airwaysurgeries. The primary outcomes that will be measuredare apnoea&ndash;hypopnoea index, excess weight loss andin-hospital mortality. The secondary outcomes willinclude duration of hospital stay, neck circumference,reoperation, waist circumference, body mass index,Epworth Sleepiness Scale score, overt complications(eg, gastric fistula, bleeding, delayed gastric emptying,wound infection), quality of life, quality of sleep and/orfunctionality. The systematic review will be conductedaccording to the recommendations as outlined by theCochrane collaboration.Ethics and dissemination The systematic review andmeta-analysis will include published data available onlineand thus ethics approval will not be required. The findingswill be disseminated and published in a peer-reviewedjournal. Review updates will be conducted if there isnew evidence that may cause any change in reviewconclusions. Any changes to the study protocol will beupdated in the PROSPERO trial registry accordingly

Laparoscopic metabolic surgery for the treatment of type 2 diabetes in Asia: a scoping review and evidence-based analysis

BACKGROUND: Laparoscopic metabolic surgery has been previously shown to be an effective treatment for obese patients with type 2 diabetes (T2DM). The objective of this scoping review is to determine the impact of metabolic surgery for the treatment of type 2 diabetes in Asia and perform an evidence-based analysis. METHODS: We performed a literature search in PubMed for research on laparoscopic metabolic surgery for the treatment of T2DM in Asia region. We classified the included studies based on the Oxford Center for Evidence Based Medicine guidelines. And performed and evidence analysis. RESULTS: In total, 205 articles were identified. 62.9% of the studies were from East Asia. The evidence of 26 studies are level I, 59 are level II. Laparoscopic sleeve gastrectomy (LSG) was the most commonly reported surgical procedure (63.1%) in Asia. The number of laparoscopic metabolic surgery for T2DM in Asian countries has increased rapidly over the last 8&nbsp;years. We identified 16 studies which showed that laparoscopic metabolic surgery is an effective and safe treatment for T2DM in patients with a BMI of &gt; 25&nbsp;kg/m2 to &lt; 35&nbsp;kg/m2 in Asia. CONCLUSIONS: Our results suggest that laparoscopic metabolic surgery might be an effective and safe treatment for T2DM patients with BMI &lt; 35&nbsp;kg/m2, and that LSG is the most commonly performed surgical procedure for this in Asia

The impact of type 2 diabetes on health related quality of life in Bangladesh: results from a matched study comparing treated cases with non-diabetic controls

Background Little is known about the association between diabetes and health related quality of life (HRQL) in lower-middle income countries. This study aimed to investigate HRQL among individuals with and without diabetes in Bangladesh. Methods The analysis is based on data of a case-control study, including 591 patients with type 2 diabetes (cases) who attended an outpatient unit of a hospital in Dhaka and 591 age -and sex-matched individuals without diabetes (controls). Information about socio-demographic characteristics, health conditions, and HRQL were assessed in a structured interview. HRQL was measured with the EuroQol (EQ) visual analogue scale (VAS) and the EQ five-dimensional (5D) descriptive system. The association between diabetes status and quality of life was examined using multiple linear and logistic regression models. Results Mean EQ-VAS score of patients with diabetes was 11.5 points lower (95 %-CI: −13.5, −9.6) compared to controls without diabetes. Patients with diabetes were more likely to report problems in all EQ-5D dimensions than controls, with the largest effect observed in the dimensions ‘self-care’ (OR = 5.9; 95 %-CI: 2.9, 11.8) and ‘mobility’ (OR = 4.5; 95 %-CI: 3.0, −6.6). In patients with diabetes, male gender, high education, and high-income were associated with higher VAS score and diabetes duration and foot ulcer associated with lower VAS scores. Other diabetes-related complications were not significantly associated with HRQL. Conclusions Our findings suggest that the impact of diabetes on HRQL in the Bangladeshi population is much higher than what is known from western populations and that unlike in western populations comorbidities/complications are not the driving factor for this effect

Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%.Spanish Government RTI2018-098913-B100 CV20-45250 A-TIC-080UGR1

Physical activity level and stroke risk in US population: A matched case-control study of 102,578 individuals

Background: Stroke has been linked to a lack of physical activity; however, the extent of the association between inactive lifestyles and stroke risk has yet to be characterized across large populations. Purpose: This study aimed to explore the association between activity-related behaviors and stroke incidence. Methods: Data from 1999 to 2018 waves of the concurrent cross-sectional National Health and Nutrition Examination Survey (NHANES) were extracted. We analyzed participants characteristics and outcomes for all participants with data on whether they had a stroke or not and assessed how different forms of physical activity affect the incidence of disease. Results: Of the 102,578 individuals included, 3851 had a history of stroke. A range of activity-related behaviors was protective against stroke, including engaging in moderate-intensity work over the last 30 days (OR = 0.8, 95% CI = 0.7-0.9; P = 0.001) and vigorous-intensity work activities over the last 30 days (OR = 0.6, 95% CI = 0.5-0.8; P \u3c 0.001), and muscle-strengthening exercises (OR = 0.6, 95% CI = 0.5-0.8; P \u3c 0.001). Conversely, more than 4 h of daily TV, video, or computer use was positively associated with the likelihood of stroke (OR = 11.7, 95% CI = 2.1-219.2; P = 0.022). Conclusion: Different types, frequencies, and intensities of physical activity were associated with reduced stroke incidence, implying that there is an option for everyone. Daily or every other day activities are more critical in reducing stroke than reducing sedentary behavior duration

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images