Segmenting 3D geometry of left coronary artery from coronary CT angiography using deep learning for hemodynamic evaluation.

While coronary CT angiography (CCTA) is crucial for detecting several coronary artery diseases, it fails to provide essential hemodynamic parameters for early detection and treatment. These parameters can be easily obtained by performing computational fluid dynamic (CFD) analysis on the 3D artery geometry generated by CCTA image segmentation. As the coronary artery is small in size, manually segmenting the left coronary artery from CCTA scans is a laborious, time-intensive, error-prone, and complicated task which also requires a high level of expertise.

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal.

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored.

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor.

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach.

Electrospinning of Biomedical Nanofibers/Nanomembranes: Effects of Process Parameters.

Nanotechnology has attracted great attention from researchers in modern science because nanomaterials have innovative and superior physical, chemical, and biological properties, and they can be altered and modified accordingly. As particles get smaller, their surface area increases compared to their volume. Electrospinning is one of the advanced techniques to produce ultrathin nanofibers and membranes, and it is one of the best ways to create continuous nanomaterials with variable biological, chemical, and physical properties.

Role of Reactive Oxygen Species in Aging and Age-Related Diseases: A Review.

Research on the role of reactive oxygen species (ROS) in the aging process has advanced significantly over the last two decades. In light of recent findings, ROS takes part in the aging process of cells along with contributing to various physiological signaling pathways. Antioxidants being cells' natural defense mechanism against ROS-mediated alteration, play an imperative role to maintain intracellular ROS homeostasis.

Modeling a sustainable vaccine supply chain for a healthcare system.

This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network's economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network.

Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion.

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward.

Epidemic trend analysis of SARS-CoV-2 in South Asian Association for Regional Cooperation countries using modified susceptible-infected-recovered predictive model.

A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than 200 countries around the world, and became pandemic by the time. In this article, a modified version of the well-known mathematical epidemic model susceptible-infected-recovered (SIR) is used to analyze the epidemic's course of COVID-19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka.

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City.

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30 years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST.

Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach.

Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout in the 1960s, dengue fever has been endemic in Bangladesh, with the highest concentration of infections in the capital, Dhaka. This study aims to develop a machine learning model that can use relevant information about the factors that cause Dengue outbreaks within a geographic region.

Bicycle industry as a post-pandemic green recovery driver in an emerging economy: a SWOT analysis.

The COVID-19 pandemic has exposed socioeconomic vulnerabilities around the world. After fighting the coronavirus for more than 1 and a half years now, the countries are recovering from the epidemic with the help of cutting-edge medical research. The policymakers are implementing stimulus packages for post-pandemic economic recovery.

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation.

Background: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific tasks like calling for emergencies, monitoring patient health, etc.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes.

Advances in Nanomaterial-Based Platforms to Combat COVID-19: Diagnostics, Preventions, Therapeutics, and Vaccine Developments.

The COVID-19 pandemic caused by the SARS-CoV-2, a ribonucleic acid (RNA) virus that emerged less than two years ago but has caused nearly 6.1 million deaths to date. Recently developed variants of the SARS-CoV-2 virus have been shown to be more potent and expanded at a faster rate.

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach.

Comparison of the emission factors of air pollutants from gasoline, CNG, LPG and diesel fueled vehicles at idle speed.

The emission factor (EF) is a parameter used to assess vehicle emissions. Many studies have reported EFs for vehicles in driving conditions. However, the idling emissions should not be neglected in characterizing actual vehicle emissions in congested large cities, where idling is very common on the road.

Face Masks to Combat Coronavirus (COVID-19)-Processing, Roles, Requirements, Efficacy, Risk and Sustainability.

Increasingly prevalent respiratory infectious diseases (e.g., COVID-19) have posed severe threats to public health.

Fabrication, Characterization and In Vitro Assessment of Derived Hydroxyapatite Particulate-Filled Polymer Composite for Implant Applications.

This paper presents the formulation, characterization, and in vitro studies of polymer composite material impregnated with naturally derived hydroxyapatite (HA) particulates for biomedical implant applications. () seashells (SS) were collected, washed and cleaned, sun-dried for 24 h, and ground into powder particulates. The SS particulates of different weight percentages (0, 10, 20, 30, 40, 50 wt%)-loaded high-density polyethylene (HDPE) composites were fabricated by compression molding for comparative in vitro assessment.

Global landscape of COVID-19 vaccination progress: insight from an exploratory data analysis.

The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling of the COVID-19 vaccination campaign across the globe. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted.

COVID-19 and black fungus: Analysis of the public perceptions through machine learning.

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.

Video based non-contact monitoring of respiratory rate and chest indrawing in children with pneumonia.

Pneumonia is the single largest cause of death in children worldwide due to infectious diseases. According to WHO guidelines, fast breathing and chest indrawing are the key indicators of pneumonia in children requiring antibiotic treatments. The aim of this study was to develop a video based novel method for simultaneous monitoring of respiratory rate and chest indrawing without upsetting babies.

Surface characterization of thin-film composite membranes using contact angle technique: Review of quantification strategies and applications.

Thin-film composite (TFC) membranes are the most widely used membranes for low-cost and energy-efficient water desalination processes. Proper control over the three influential surface parameters, namely wettability, roughness, and surface charge, is vital in optimizing the TFC membrane surface and permeation properties. More specifically, the surface properties of TFC membranes are often tailored by incorporating novel special wettability materials to increase hydrophilicity and tune surface physicochemical heterogeneity.

The effects of plaque morphological characteristics on the post-stenotic flow in left main coronary artery bifurcation.

Local post-stenotic hemodynamics has critical influence in the atherosclerotic plaque progression occurring in susceptible arterial sites, in particular the left main coronary artery (LMCA) bifurcation. Understanding the effects of plaque morphological characteristics: stenosis severity (SS), eccentricity index (EI) and lesion length (LL) on the post-stenotic flow behavior can significantly improve treatment planning. In order to investigate these effects, we have employed computational fluid dynamics (CFD) simulations in twenty computer-generated and five patient-specific LMCA models and the hemodynamic parameters: velocity, pressure (P), wall pressure gradient (WPG), wall shear stress (WSS), time averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and helicity intensity (h) were analyzed.