A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region.

Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting these scans is often challenging, necessitating automated solutions for timely and accurate diagnosis. This research proposed a novel hybrid model that integrates a Vision Transformer (ViT) and a Long Short Term Memory (LSTM) to accurately detect and classify stroke characteristics using CT images.

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models.

Physicochemical Properties, Antioxidant Activity, and High-Performance Thin-Layer Chromatography Profiling of Propolis Samples from Western Australia.

This study reports on the physicochemical and antioxidant properties of propolis samples from various regions across Western Australia and identifies some phenolic constituents using high-performance thin-layer chromatography (HPTLC). Total phenolic content (TPC) was determined using a modified Folin-Ciocalteu assay, and antioxidant activity was investigated with the Ferric Reducing Antioxidant Power (FRAP) assay and also visualised and semi-quantified by HPTLC-DPPH analysis. TPC values ranged from 9.

Regulation of regeneration in .

Unlabelled: We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in . Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration.

Evaluation of feasibility phase of adaptive version of locally made bubble continuous positive airway pressure oxygen therapy for the treatment of COVID-19 positive and negative adults with severe pneumonia and hypoxaemia.

Background: Bubble continuous positive airway pressure (bCPAP) oxygen therapy has been shown to be safe and effective in treating children with severe pneumonia and hypoxaemia in Bangladesh. Due to lack of adequate non-invasive ventilatory support during coronavirus disease 2019 (COVID-19) crisis, we aimed to evaluate whether bCPAP was safe and feasible when adapted for use in adults with similar indications.

Methods: Adults (18-64 years) with severe pneumonia and moderate hypoxaemia (80 to <90% oxygen saturation (SpO) in room air) were provided bCPAP via nasal cannula at a flow rate of 10 litres per minute (l/min) oxygen at 10 centimetres (cm) HO pressure, in two tertiary hospitals in Dhaka, Bangladesh.

Bioactivities and Phenolic Profiles of Honeys Derived from Plants of the Goldfields, Esperance and Wheatbelt Regions of Western Australia.

The aim of this study was to examine a collection of 79 honeys derived from plants endemic to several Western Australian unique bioregions for bioactivity and physicochemical characteristics. For physicochemical analyses, total phenolic content, high performance thin layer chromatography (HPTLC) fingerprints, pH, Brix, colour and hydrogen peroxide generation were examined. Brix (82.

Trends and inequity in improved sanitation facility utilisation in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys.

Improved sanitation is indispensable to human health. However, lack of access to improved sanitation remains one of the most daunting public health challenges of the twenty-first century in Bangladesh. The aim of the study was to describe the trends in access to improved sanitation facilities following the inequity gap among households in different socioeconomic groups in Bangladesh.

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.

Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver.

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown.

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging.

Antioxidant Activity and Phenolic Compound Identification and Quantification in Western Australian Honeys.

This study reports on the total phenolic content and antioxidant activity as well as the phenolic compounds that are present in spp. (Red Bell), (Coastal Peppermint), (Marri) and (Jarrah) honeys from Western Australia. The honey's total phenolic content (TPC) was determined using a modified Folin-Ciocalteu assay, while their total antioxidant activity was determined using FRAP and DPPH assays.

Deep transfer learning approaches for Monkeypox disease diagnosis.

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients.

A Comprehensive HPTLC-Based Analysis of the Impacts of Temperature on the Chemical Properties and Antioxidant Activity of Honey.

Honeys are commonly subjected to a series of post-harvest processing steps, such as filtration and/or radiation treatment and heating to various temperatures, which might affect their physicochemical properties and bioactivity levels. Therefore, there is a need for robust quality control assessments after honey processing and storage to ensure that the exposure to higher temperatures, for example, does not compromise the honey's chemical composition and/or antioxidant activity. This paper describes a comprehensive short-term (48 h) and long-term (5 months) study of the effects of temperature (40 °C, 60 °C and 80 °C) on three commercial honeys (Manuka, Marri and Coastal Peppermint) and an artificial honey, using high-performance thin-layer chromatography (HPTLC) analysis.

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.

Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications.

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.

With the increase in severity of COVID-19 pandemic situation, the world is facing a critical fight to cope up with the impacts on human health, education and economy. The ongoing battle with the novel corona virus, is showing much priority to diagnose and provide rapid treatment to the patients. The rapid growth of COVID-19 has broken the healthcare system of the affected countries, creating a shortage in ICUs, test kits, ventilation support system.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void.

Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30-40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications.

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services.

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years.

Australian Honeypot Ant () Honey-A Comprehensive Analysis of the Physiochemical Characteristics, Bioactivity, and HPTLC Profile of a Traditional Indigenous Australian Food.

Despite its cultural and nutritional importance for local Aboriginal people, the unusual insect honey produced by Western Australian honeypot ant () has to date been rarely investigated. This study reports on the honey's physicochemical properties, its total phenolic, major sugars and 5-hydroxymethylfurfural contents, and its antioxidant activities. The honey's color value is 467.

Hyponatremia in COVID-19 patients: Experience from Bangladesh.

Background: The purpose of the study was to measure the prevalence of hyponatremia and its association with clinical and laboratory characteristics of hospitalized coronavirus disease 2019 (COVID-19) patients at Dhaka Medical College and Hospital (DMCH).

Methods: This retrospective study was conducted in COVID-19 dedicated wards at DMCH from June to August 2020. Demographic, clinical, and laboratory data were collected from patient treatment sheets.

An investigation of the suitability of melissopalynology to authenticate Jarrah honey.

This study reports on the analysis of eleven Jarrah () honeys, of which nearly half (n = 5) were re-classified as Blackbutt () honey on the grounds of the predominant flower pollen identified by melissopalynology. Based on a comprehensive analysis of the honeys' physico- and phytochemical characteristics and antioxidant activity data, taking into account pH, electrical conductivity, refractive index and Brix values as well as moisture content, individual fructose and glucose content and derived fructose to glucose ratio alongside total phenolic content and antioxidant activity determined by the DPPH assay, no statistically significant difference was found amongst the eleven honeys classified by pollen analysis into two honey groups, 'Jarrah' or 'Blackbutt'. This study therefore draws into question the value of melissopalynology as an analysis tool to authenticate Jarrah honey.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases.

A 2-month post-COVID-19 follow-up study on patients with dyspnea.

Background And Aims: Dyspnea is one of the most common symptoms associated with the COVID-19 caused by novel coronavirus SARS-CoV-2. This study aimed to assess the prevalence of dyspnea, observe co-variables, and find predictors of dyspnea after 2 months of recovery from COVID-19.

Methods: A total of 377 patients were included in the study based on their responses and clinical findings during initial admission to the hospital with COVID-19.

Persistence of sleep disturbance among post-COVID patients: Findings from a 2-month follow-up study in a Bangladeshi cohort.

To evaluate the persistence and factors associated with sleep disturbances among COVID-19 patients with a history of sleep disturbances 2 months after discharge from the hospital. A total of 400 patients admitted at Dhaka Medical College Hospital during July and August were diagnosed as suffering from sleep disturbances during their hospital stay using a standardized scale. They were followed up 2 months later through telephone, and a total of 322 participants were interviewed (excluding 63 nonresponders and five deceased) regarding the persistence of disturbances in sleep through a structured questionnaire.

Capacity development and safety measures for health care workers exposed to COVID-19 in Bangladesh.

Background: The safety of health care workers (HCWs) in Bangladesh and the factors associated with getting COVID-19 have been infrequently studied. The aim of this study was to address this gap by assessing the capacity development and safety measures of HCWs in Bangladesh who have been exposed to COVID-19 and by identifying the factors associated with respondents' self-reported participation in capacity development trainings and their safety practices.

Methods: This cross-sectional study was based on an online survey of 811 HCWs working at 39 dedicated COVID-19 hospitals in Bangladesh.