Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Study of Diabetic Differences between International Migrants and Natives among the Asian Population

Migration presents a substantial social and public health issue. However, it is unclear whether diabetes is worse among Asian migrants than natives of South Korea over time. This longitudinal study investigated the nationwide population, including 2,680,495 adults aged 20 years and older (987,214 Asian migrants and 1,693,281 natives), who received health check-ups, using the Korean National Health Insurance Service data (2009–2015). Joinpoint regression was used to estimate the annual percentage change of diabetes, and multivariable logistic regression was used to examine differences in incident type 2 diabetes between Asian migrants and natives adjusting for age, sex, economic status, body mass index, smoking status, any alcohol use, and physical activity. The age-adjusted prevalence of diabetes increased among native men (from 8.8% in 2009 to 9.7% in 2015, APC=1.64, p<0.05) compared to Asian migrant men, and the age-adjusted prevalence of diabetes increased among native women (from 6.0% in 2009 to 6.7% in 2015, APC=1.88, p<0.05) compared to Asian migrant women. In the multivariate analyses, Asian migrants were less likely to get type 2 diabetes than natives (odds ratio, 0.82; 95% CI, 0.78 to 0.86) between the first and last health check-ups. However, the odds ratio for developing type 2 diabetes was 1.15 (95% CI, 1.10 to 1.20) among low-income levels compared to high-income levels, regardless of whether they were Asian migrants or natives. The results could help to establish a new strategy for prevention, treatment, and management of diabetes among the Asian population.
N-acyl taurines are endogenous lipid messengers that improve glucose homeostasis

Significance Deletion of fatty acid amide hydrolase (FAAH) in mice results in the concomitant elevation of 2 major types of fatty acid amides, N-acylethanolamines (NAEs) and N-acyl taurines (NATs), as well as obesity, hyperphagia, and insulin resistance. However, the respective contribution of each class of fatty acid amide to these phenotypes is unresolved. Here we employ an engineered variant of FAAH to specifically elevate NATs in vivo and administer the predominant circulating human NAT species to mice, demonstrating that NATs improve insulin sensitivity and augment secretion of the antidiabetic hormone GLP-1. These findings expose a divergent role for FAAH substrates in the regulation of glucose homeostasis, informing future efforts to target fatty acid amide signaling to treat metabolic disease. Fatty acid amide hydrolase (FAAH) degrades 2 major classes of bioactive fatty acid amides, the N-acylethanolamines (NAEs) and N-acyl taurines (NATs), in central and peripheral tissues. A functional polymorphism in the human FAAH gene is linked to obesity and mice lacking FAAH show altered metabolic states, but whether these phenotypes are caused by elevations in NAEs or NATs is unknown. To overcome the problem of concurrent elevation of NAEs and NATs caused by genetic or pharmacological disruption of FAAH in vivo, we developed an engineered mouse model harboring a single-amino acid substitution in FAAH (S268D) that selectively disrupts NAT, but not NAE, hydrolytic activity. The FAAH-S268D mice accordingly show substantial elevations in NATs without alterations in NAE content, a unique metabolic profile that correlates with heightened insulin sensitivity and GLP-1 secretion. We also show that N-oleoyl taurine (C18:1 NAT), the most abundant NAT in human plasma, decreases food intake, improves glucose tolerance, and stimulates GPR119-dependent GLP-1 and glucagon secretion in mice. Together, these data suggest that NATs act as a class of lipid messengers that improve postprandial glucose regulation and may have potential as investigational metabolites to modify metabolic disease.
NOVEL STAIN SEPARATION METHOD FOR AUTOMATIC STEREOLOGY OF IMMUNOSTAINED TISSUE SECTIONS

Abstract Many studies of brain aging and neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases require rapid counts of high signal: noise (S:N) stained brain cells such as neurons and neuroglial (microglia cells) on tissue sections. To increase throughput efficiency of this work, we have combined deep learned (DL) neural networks and computerized stereology (DL-stereology) for automatic cell counts with low error (<10%) compared to time-intensive manual counts. To date, however, this approach has been limited to sections with a single high S:N immunostain for neurons (NeuN) or microglial cells (Iba-1). The present study expands this approach to protocols that combine immunostains with counterstains, e.g., cresyl violet (CV). In our method, a stain separation technique called Sparse Non-negative Matrix Factorization (SNMF) converts a dual-stained color image to a single gray image showing only the principal immunostain. Validation testing was done using semi- and automatic stereology-based counts of sections immunostained for neurons or microglia with CV counterstaining from the neocortex of a transgenic mouse model of tauopathy (Tg4510 mouse) and controls. Cell count results with principal stain gray images show an average error rate of 16.78% and 28.47% for the semi-automatic approach and 8.51% and 9.36% for the fully-automatic DL-stereology approach for neurons and microglia, respectively, as compared to manual cell counts (ground truth). This work indicates that stain separation by SNMF can support high throughput, fully automatic DL-stereology based counts of neurons and microglia on counterstained tissue sections.
Preference-based measure of health-related quality of life and its determinants in sickle cell disease in Nigeria

Background Health-related quality of life (HRQL) and economic burden are important issues for people with sickle cell disease (SCD) owing to better survival due to medical advances. Preference-based or utility information is necessary to make informed economic decisions on treatment and alternative therapies. This study aimed to assess preference-based measures of HRQL in sickle cell patients. Methods and findings Data were collected from two SCD outpatient clinics in Ibadan, Nigeria. A standard algorithm was used to derive utility scores, and measure SF-6D from the SF-36. A multivariate regression model was used to assess predictors and their impact. A combination of socio-demographic, bio-physiological and psychosocial variables predicted utility score in people with SCD. Socio-demographic and bio-physiological factors explained 7.5% and 17.9% of the variance respectively, while psychosocial factors explained 4.9%. Women had lower utility scores with a small effect size (d = 0.17). Utility score increased with level of education but decreased with age, anxiety, frequency of pain episodes and number of co-morbidities. Conclusions Utility score in SCD was low indicating a substantial impact of the disease on HRQL of patients and the value they place on their health state due to the limitations they experienced. Interventions should include both clinical and psychosocial approach to help in improving their quality of life of the patients.
Predictors of prolonged mechanical ventilation in patients admitted to intensive care units: A systematic review

Objective: Although intensive care medicine has evidenced a significant growth in recent decades, the number of patients requiring prolonged mechanical ventilation (PMV) still represents a considerable burden on health-care expenditure. The prediction of the need for PMV seems to provide a plausible cost-effective intervention. The objective of this study is to systematically review the predictors of the need for PMV of adult patients admitted to intensive care units (ICUs) due to medical and surgical needs. Methods: We conducted a systematic search on three online databases (PubMed, Embase, and MEDLINE) till February 20, 2019. The search process employed several combinations of specific keywords and Boolean operators. Results: A total of 15 articles were included in the study. Based on pooling the outcomes of odds ratios (ORs) and their respective 95% confidence intervals (CIs) as reported from logistic regression analyses, the pooled PMV incidence in 8220 patients (69.59% males) was 17.67 cases per 100 ICU admissions (95% CI 13.69–21.65). We could not conduct a meta-analysis of ORs and 95% CIs due to the significant heterogeneity observed between the included studies (P < 0.001, I2 = 97%). Pre-operative/preadmission kidney dysfunction and chronic obstructive pulmonary disease were the most significant independent predictors of the need for PMV. Following cardiac surgeries, repeated or emergency surgery, prolonged cardiopulmonary bypass time, and the need for blood transfusion were predictors of the need for PMV. Conclusion: Within the study limitations, several predictors were identified, which could be further investigated using a unified PMV definition. Successful prediction of the need for PMV would assist clinicians in identifying and adjusting a “weaning strategy” as well as improving patient care to reduce morbidity. Furthermore, establishing specialized weaning units could be warranted based on PMV incidence and prediction in the local settings.
Diagnostic plasma miRNA-profiles for ovarian cancer in patients with pelvic mass

Background Ovarian cancer is the fifth most common cancer in women worldwide. Moreover, there are no reliable minimal invasive tests to secure the diagnosis of malignant pelvic masses. Cell-free, circulating microRNAs have the potential as diagnostic biomarkers in cancer. Here, we performed and validated a miRNA panel with the potential to distinguish OC from benign pelvic masses. Methods The profile of plasma microRNA was determined with a panel of 46 candidates in a discovery group and a validation group, each consisting of 190 pre-surgery plasma samples from age-matched patients with malignant (n = 95) and benign pelvic mass (n = 95), by real time RT-qPCR. Results Four up-regulated (miR-200c-3p, miR-221-3p, miR-21-5p, and miR-484) and two down-regulated (miR-195-5p and miR-451a) microRNAs were discovered. From those, miR-200c-3p and miR-221-3p were further confirmed in a validation cohort. A combination of these 2 microRNAs together with CA-125 yielded an overall diagnostic accuracy of AUC = 0.96. Conclusions We showed consistent plasma microRNA profiles that provide independent diagnostic information of late stage OC.
Genome-wide CRISPR screening reveals genetic modifiers of mutant EGFR dependence in human NSCLC

EGFR-mutant NSCLCs frequently respond to EGFR tyrosine kinase inhibitors (TKIs). However, the responses are not durable, and the magnitude of tumor regression is variable, suggesting the existence of genetic modifiers of EGFR dependency. Here, we applied a genome-wide CRISPR-Cas9 screening to identify genetic determinants of EGFR TKI sensitivity and uncovered putative candidates. We show that knockout of RIC8A, essential for G-alpha protein activation, enhanced EGFR TKI-induced cell death. Mechanistically, we demonstrate that RIC8A is a positive regulator of YAP signaling, activation of which rescued the EGFR TKI sensitizing phenotype resulting from RIC8A knockout. We also show that knockout of ARIH2, or other components in the Cullin-5 E3 complex, conferred resistance to EGFR inhibition, in part by promoting nascent protein synthesis through METAP2. Together, these data uncover a spectrum of previously unidentified regulators of EGFR TKI sensitivity in EGFR-mutant human NSCLC, providing insights into the heterogeneity of EGFR TKI treatment responses.
Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease.

Importance Recent guidelines endorse using history of menopause before age 40 years to refine atherosclerotic cardiovascular disease risk assessments among middle-aged women. Robust data on cardiovascular disease risk in this population are lacking. Objective To examine the development of cardiovascular diseases and cardiovascular risk factors in women with natural and surgical menopause before age 40 years. Design, Setting, and Participants Cohort study (UK Biobank), with adult residents of the United Kingdom recruited between 2006 and 2010. Of women who were 40 to 69 years old and postmenopausal at study enrollment, 144 260 were eligible for inclusion. Follow-up occurred through August 2016. Exposures Natural premature menopause (menopause before age 40 without oophorectomy) and surgical premature menopause (bilateral oophorectomy before age 40). Postmenopausal women without premature menopause served as the reference group. Main Outcomes and Measures The primary outcome was a composite of incident coronary artery disease, heart failure, aortic stenosis, mitral regurgitation, atrial fibrillation, ischemic stroke, peripheral artery disease, and venous thromboembolism. Secondary outcomes included individual components of the primary outcome, incident hypertension, hyperlipidemia, and type 2 diabetes. Results Of 144 260 postmenopausal women included (mean [SD] age at enrollment, 59.9 [5.4] years), 4904 (3.4%) had natural premature menopause and 644 (0.4%) had surgical premature menopause. Participants were followed up for a median of 7 years (interquartile range, 6.3-7.7). The primary outcome occurred in 5415 women (3.9%) with no premature menopause (incidence, 5.70/1000 woman-years), 292 women (6.0%) with natural premature menopause (incidence, 8.78/1000 woman-years) (difference vs no premature menopause, +3.08/1000 woman-years [95% CI, 2.06-4.10]; P < .001), and 49 women (7.6%) with surgical premature menopause (incidence, 11.27/1000 woman-years) (difference vs no premature menopause, +5.57/1000 woman-years [95% CI, 2.41-8.73]; P < .001). For the primary outcome, natural and surgical premature menopause were associated with hazard ratios of 1.36 (95% CI, 1.19-1.56; P < .001) and 1.87 (95% CI, 1.36-2.58; P < .001), respectively, after adjustment for conventional cardiovascular disease risk factors and use of menopausal hormone therapy. Conclusions and Relevance Natural and surgical premature menopause (before age 40 years) were associated with a small but statistically significant increased risk for a composite of cardiovascular diseases among postmenopausal women. Further research is needed to understand the mechanisms underlying these associations.
Cardiac Rehabilitation After Stroke

Cardiac Rehabilitation after Stroke Ada Tang Doctor of Philosophy 2010 Institute of Medical Science University of Toronto In contrast to cardiac rehabilitation (CR) programs, traditional stroke rehabilitation aims to maximize functional independence and does not have a strong focus on exercise training and risk factor modification. Given the parallels between stroke and heart disease in cardiovascular etiology and risk factors, CR may be suited to supplement stroke rehabilitation by providing opportunities to enhance fitness and manage stroke risk factors. The aim of this work was to 1) examine the use of a non-adapted CR program of care with individuals with stroke and/or transient ischemic attack (TIA) through a retrospective database review, 2) using a prospective trial, determine the feasibility and effects of an adapted CR program for people with mild to moderate impairment from stroke, and 3) explore characteristics related to degree of program response in aerobic and functional capacity through secondary data analysis. The results from Study 1 demonstrated that traditional CR is an underutilized service for individuals with stroke or TIA, yet improvements in aerobic fitness were comparable to their non-stroke counterparts. In Study 2, adapted CR was feasible for individuals with a range of stroke-related disability and effective in increasing aerobic capacity. The anticipated carry over to improved walking capacity was not observed. There were no changes in health-related quality of life or stroke risk factors.
FRAILTY ASSESSMENT BASED ON THE QUALITY OF DAILY WALKING

Abstract Frailty is an increasingly recognized geriatric syndrome resulting in age-related decline in reserve across multiple physiologic systems. An impaired physical function is a prime indicator of frailty. In this study, we aim to implement a body-worn sensor to characterize the quantity and quality of everyday walking, and establish associations between gait impairment and frailty. Daily physical activity was acquired for 48 hours from 125 older adults (≥65 years; 44 non-frail, 60 pre-frail, and 21 frail based on the Fried gold standard) using a tri-axial accelerometer motion-sensor. Continuous purposeful walks (≥60s) without pauses were identified from time-domain acceleration data. Power spectral density (PSD) analysis was performed to define higher gait variability, which was identified by a shorter and wider PSD peak. Association between frailty and gait parameters was assessed using multivariable nominal logistic models with frailty as the dependent variable, and demographic parameters along with the gait parameters as the independent variables. Stride times, PSD gait variability, and total and maximum continuous purposeful walking duration were significantly different between non-frail and pre-frail/frail groups (p<0.05). Using a step-wise model with the above qualitative and quantitative gait parameters as predictors, the pre-frail/frail group (vs. non-frail) was identified with 71.4% sensitivity and 75.4% specificity. Everyday walking characteristics were found to be accurate determinants of frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the stages of frailty. In-home gait analysis is advantageous over clinical gait analysis as it enables cost- and space-effective continuous monitoring.
Mapping Ethanol Tolerance in Budding Yeast Reveals High Genetic Variation in a Wild Isolate

Ethanol tolerance, a polygenic trait of the yeast Saccharomyces cerevisiae, is the primary factor determining industrial bioethanol productivity. Until now, genomic elements affecting ethanol tolerance have been mapped only at low resolution, hindering their identification. Here, we explore the genetic architecture of ethanol tolerance, in the F6 generation of an Advanced Intercrossed Line (AIL) mapping population between two phylogenetically distinct, but phenotypically similar, S. cerevisiae strains (a common laboratory strain and a wild strain isolated from nature). Under ethanol stress, 51 quantitative trait loci (QTLs) affecting growth and 96 QTLs affecting survival, most of them novel, were identified, with high resolution, in some cases to single genes, using a High-Resolution Mapping Package of methodologies that provided high power and high resolution. We confirmed our results experimentally by showing the effects of the novel mapped genes: MOG1, MGS1, and YJR154W. The mapped QTLs explained 34% of phenotypic variation for growth and 72% for survival. High statistical power provided by our analysis allowed detection of many loci with small, but mappable effects, uncovering a novel “quasi-infinitesimal” genetic architecture. These results are striking demonstration of tremendous amounts of hidden genetic variation exposed in crosses between phylogenetically separated strains with similar phenotypes; as opposed to the more common design where strains with distinct phenotypes are crossed. Our findings suggest that ethanol tolerance is under natural evolutionary fitness-selection for an optimum phenotype that would tend to eliminate alleles of large effect. The study provides a platform for development of superior ethanol-tolerant strains using genome editing or selection.
Machine learning validation of EEG+tACS artefact removal

Objective. Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Approach. Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a linear discriminant analysis was trained on the cleaned EEG to differentiate different parts of the experiment. Main results. Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65%–94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process. Significance. This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.
Small grain production as an adaptive strategy to climate change in Mangwe District, Matabeleland South in Zimbabwe

This article assesses the feasibility of small grains as an adaptive strategy to climate change in the Mangwe District in Zimbabwe. The change in climate has drastically affected rainfall patterns across the globe and in Zimbabwe in particular. Continuous prevalence of droughts in Zimbabwe, coupled with other economic calamities facing the Southern African country, has contributed to a larger extent to the reduction in grain production among communal farmers, most of whom are in semi-arid areas. This has caused a sudden increase in food shortages, particularly in the Mangwe District, as a result of erratic rainfall, which has negatively affected subsistence farming. This article was deeply rooted in qualitative research methodologies. Purposive sampling was used to sample the population. The researchers used key informant interviews, focus group discussions and secondary data to collect data. Data were analysed using INVIVO software, a data analysis tool that brings out themes. The results of the study are presented in the form of themes. The study established that small grains contributed significantly to addressing food shortages in the Mangwe District. The study results revealed that small grains were a reliable adaptive strategy to climate change as they increased food availability, accessibility, utilisation and stability. Despite the significant contribution of small grains to addressing food shortages, there is a need for the government to come up with a vibrant small grains policy, and government support that is visible as well as market creation for small grains. The study further recommends that small grains in semi-arid areas can be a panacea to food insecurity in Zimbabwe.