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Efficient human activity recognition on edge devices using DeepConv LSTM architectures
Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach
Sub-population identification of multimorbidity in sub-Saharan African populations
Abstract This work provides three contributions that straddle the medical literature on multimorbidity and the data science community with an interest on exploratory analysis of health-related research data. First, we propose a definition for multimorbidity as the co-occurrence of (at least) two disease diagnoses from a pre-determined list. This interpretation adds to a growing body of working definitions emerging from the literature. Second, we apply this novel outcome of-interest to two sub-Saharan populations located in Nairobi, Kenya and Agincourt, South Africa. The source data for this analysis was collected as part of the Africa Wits-INDEPTH Partnership for Genomic Studies project. Third, we stratify this outcome-of-interest across all possible sub-populations and identify sub-populations with anomalously high (or low) rates of multimorbidity. Critically, the automatic stratification approach emphasizes efficient, disciplined exploratory-based analysis as a complementary alternative to more commonly-used confirmation analysis methods. Our results show that high-risk sub-populations identified in one part of the continent transfer to the other location (and vice-versa) with the equivalent sub-population at the other location also experiencing higher rates of multimorbidity. Second, we discover a real-world scenario where a more-at risk sub-population existed beyond the simpler sub-populations traditionally stratified by age and sex. This is in contrast to existing literature which commonly stratifies disease diagnoses by sex when reporting results. Patterns in diseases, and healthcare more generally, are likely more nuanced than manual approaches may be able to describe. This work helps introduce public health researchers to data science methods that scale to the size and complexity of modern day datasets.
Cerumenogram as an assay for the metabolic diagnosis of precancer, cancer, and cancer remission
Author Correction: Exploring dose–response variability and relative severity assessment in STZ-induced diabetes male NSG mice
Enhanced broadband low-frequency performance of negative Poisson’s ratio metamaterials with added mass
Habesha cultural cloth classification using deep learning
Efficacy of corticosteroids addition to multimodal cocktail periarticular injection in total knee arthroplasty with hemophilic arthropathy
Ultra-high optomechanical EUV-hypersound coupling rate in phoxonic crystal structures based on 2D MWCNTs array
Detection of out-of-seam and out-of-scope mining using double filtering method
Analysis the status and spatio-temporal characteristics of the synergistic development of China’s multi-level medical insurance system
Identification and validation of the inflammatory response-related LncRNAs as diagnostic biomarkers for acute ischemic stroke
Abstract Ischemic stroke is one of the leading causes of deaths and disability, which is linked to inflammation. In this study, we aimed to identify inflammation-related lncRNAs as diagnostic biomarkers of acute ischemic stroke (AIS). A competing endogenous RNAs (ceRNA) network was established through whole transcriptome analysis. Gene expression datasets from the GEO database were analyzed to identify differentially expressed genes (DEGs), miRNAs and lncRNAs. Inflammation-related DEGs were determined through the intersection of the DEGs of the inflammation-related gene set from Genecards. Multiple databases like lncBase and Targetscan were analyzed to establish a ceRNA network. Several hub genes and sub-networks were obtained from a protein to protein (PPI) network. In addition, the candidate lncRNAs derived from the subnetwork were validated using mice MCAO model and clinical samples. Finally, a network comprising 20 lncRNAs, 26 miRNAs, and 43 inflammatory genes was analyzed, leading to the identification of MALAT1, SNHG8, and GAS5 as potential diagnostic biomarkers. Knockdown of MALAT1 and GAS5 resulted in decreased neurological severity score and inflammation response in mice MCAO model, indicating that these genes were significant diagnostic biomarkers for distinguishing AIS from healthy controls. These findings show that circulating MALAT1 and GAS5 have the potential to serve as clinical diagnostic biomarkers of AIS associated with inflammation.
Effect of wall design on heat loss and drying kinetics in a solar greenhouse for yellow pepper
Theoretical and experimental investigation of hydration behavior of choline salicylate ionic liquid in the presence of L- glycine
Abstract Choline salicylate [Ch][Sal] an active pharmaceutical ingredient ionic liquid (API-IL) aqueous solutions in the presence of L-glycine that is a simple simulated biological media has been studied. The thermodynamic and transport properties including density, speed of sound, viscosity and electrical conductance have been studied under atmospheric pressure and a temperature range of (288.15 to 318.15) K. The key thermophysical properties, including apparent molar volume (V φ ), apparent molar isentropic compressibility (κ φ ), viscosity B-coefficient, ion association constant (K a ), and limiting molar conductivity (Λ 0 ) were derived from these data. The transfer properties (Δ tr V φ 0 , Δ tr κ φ 0 , and ΔB tr ) demonstrate the dominance hydrophilic-hydrophilic interactions between the IL and L-glycine in the studied systems that increased with L-glycine concentration. The COSMO results revealed that differences in molecular size, stability, and hydration behavior of [Ch][Sal] and L-glycine, with [Ch][Sal] exhibiting stronger hydration due to its larger size and hydrogen bonding capacity. These findings provide insights into solute-solvent interactions and potential synergistic effects of [Ch][Sal] and L-glycine as a simple simulated biological media contributing to the optimization of pharmaceutical formulations and related activities.
Neutrophils induce astrocytic AQP4 expression via IL-1α and TNF, contributing to cerebral oedema in ischaemic stroke rats
The association between serum growth differentiation factor 15 and insulin resistance in women diagnosed with polycystic ovary syndrome
Explaining international differences in excess mortality due to Covid-19
Abstract Many explanations have been advanced for why the frequency of deaths associated with Covid-19 varied so much across countries. Previous work has provided evidence that numerous social, economic, and environmental factors correlate with Covid-19 outcomes. One problem researchers face in identifying which of these explanations are best able to explain cross-country variation is that the number of these explanations is too large to be usefully included in a single regression model. This paper uses Bayesian Model Averaging (BMA) to address this problem, focusing on excess mortality to ensure meaningful comparisons across countries. The results suggest that a key determinant of countries’ success in containing Covid-19 has been the strength of the Rule of Law. We also find evidence that rainfall and seaborders are key potential explanations for differences in excess mortality.