A trend analysis of Black American women with cardiovascular disease and chronic medical conditions, sociodemographic factors from NHANES From 2011 to 2020.

Background: Significant racial and gender differences exist in the prevalence of CVD in the United States. The goal of this study is to evaluate the prevalence of CVD among Black American women, the relationship between CVD and some medical conditions, and significant sociodemographic factors.

Methods: The researchers in this study used data from four cycles of the NHANES, carried out by the NCHS.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies.

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape.

Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study.

Tomato leaf diseases can incur significant financial damage by having adverse impacts on crops and, consequently, they are a major concern for tomato growers all over the world. The diseases may come in a variety of forms, caused by environmental stress and various pathogens. An automated approach to detect leaf disease from images would assist farmers to take effective control measures quickly and affordably.

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification.

The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term damage to the brain. Magnetic resonance imaging (MRI) is one of the most common methods of detecting brain tumors. To determine whether a patient has a brain tumor, MRI filters are physically examined by experts after they are received.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

Background: Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer.

Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management.

The COVID-19 pandemic has caused a global crisis with 47,209,305 confirmed cases and 1,209,505 confirmed deaths worldwide as of November 2, 2020. Forecasting confirmed cases and understanding the virus dynamics is necessary to provide valuable insights into the growth of the outbreak and facilitate policy-making regarding virus containment and utilization of medical resources. In this study, we applied a mathematical epidemic model (MEM), statistical model, and recurrent neural network (RNN) variants to forecast the cumulative confirmed cases.

Health Internet Technology for Chronic Conditions: Review of Diabetes Management Apps.

Background: Mobile health (mHealth) smartphone apps have shown promise in the self-management of chronic disease. In today's oversaturated health app market, selection criteria that consumers are employing to choose mHealth apps for disease self-management are of paramount importance. App quality is critical in monitoring disease controls but is often linked to consumer popularity rather than clinical recommendations of effectiveness in disease management.

Epileptic Seizure Detection for Imbalanced Datasets Using an Integrated Machine Learning Approach.

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures.

OpenPharma Blockchain on FHIR: An Interoperable Solution for Read-Only Health Records Exchange through Blockchain and Biometrics.

The healthcare system in the United States is unique. From payor to provider, patients have the freedom of choice. This creates a complicated and profitable paradigm of care.

A Novel Method for the Pentosan Analysis Present in Jute Biomass and Its Conversion into Sugar Monomers Using Acidic Ionic Liquid.

Recently, ionic liquids (ILs) are used for biomass valorization into valuable chemicals because of their remarkable properties such as thermal stability, lower vapor pressure, non-flammability, higher heat capacity, and tunable solubility and acidity. Here, we demonstrate a method for the synthesis of C5 sugars (xylose and arabinose) from the pentosan present in jute biomass in a one-pot process by utilizing a catalytic amount of Brønsted acidic 1-methyl-3-(3-sulfopropyl)-imidazolium hydrogen sulfate IL. The acidic IL is synthesized in the lab and characterized using NMR spectroscopic techniques for understanding its purity.

Chemical Synthesis of Porous Barium Titanate Thin Film and Thermal Stabilization of Ferroelectric Phase by Porosity-Induced Strain.

Barium titanate (BaTiO3, hereafter BT) is an established ferroelectric material first discovered in the 1940s and still widely used because of its well-balanced ferroelectricity, piezoelectricity, and dielectric constant. In addition, BT does not contain any toxic elements. Therefore, it is considered to be an eco-friendly material, which has attracted considerable interest as a replacement for lead zirconate titanate (PZT).

Significant Effect of Pore Sizes on Energy Storage in Nanoporous Carbon Supercapacitors.

Mesoporous carbon can be synthesized with good control of surface area, pore-size distribution, and porous architecture. Although the relationship between porosity and supercapacitor performance is well known, there are no thorough reports that compare the performance of numerous types of carbon samples side by side. In this manuscript, we describe the performance of 13 porous carbon samples in supercapacitor devices.

Direct Production of Furfural in One-pot Fashion from Raw Biomass Using Brønsted Acidic Ionic Liquids.

The conversion of raw biomass into C5-sugars and furfural was demonstrated with the one-pot method using Brønsted acidic ionic liquids (BAILs) without any mineral acids or metal halides. Various BAILs were synthesized and characterized using NMR, FT-IR, TGA, and CHNS microanalysis and were used as the catalyst for raw biomass conversion. The remarkably high yield (i.

The formation of nano-layered grains and their enhanced superconducting transition temperature in Mg-doped FeSe0.9 bulks.

To search a proper dopant to further improve superconductivity in 11 type Fe-based superconductors makes sense to both their superconductivity mechanism and possible technological applications. In present work, Mg doped FeSe polycrystalline bulks were obtained by a two-step solid-state reaction method. Even though there are many MgSe and iron impurities existing in the Mg heavy doped FeSe bulks, they exhibit obviously increased Tc compared to undoped FeSe sample.

Narratives in the network: interactive methods for mining cell signaling networks.

In this article, we describe our work on graph mining as applied to the cellular signaling pathways in the Signal Transduction Knowledge Environment (STKE). We present new algorithms and a graphical tool that can help biologists discover relationships between pathways by looking at structural overlaps within the database. We address the problem of determining pathway relationships by using two data mining approaches: clustering and storytelling.

Connecting the dots between PubMed abstracts.

Background: There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications.