Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI.

Background: Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment.

The polymorphic landscape analysis of GATA1 exons uncovered the genetic variants associated with higher thrombocytopenia in dengue patients.

The current study elucidated an association between gene variants and thrombocytopenia through the investigation of the exonic polymorphic landscape of hematopoietic transcription factor-GATA1 gene in dengue patients. A total of 115 unrelated dengue patients with dengue fever (DF) (N = 91) and dengue hemorrhagic fever (DHF) (N = 24) were included in the study. All dengue patients were confirmed through detection of NS1 antigen, IgM, and IgG antibodies against the dengue virus.

Investigation of the efficacy and safety of eltrombopag to correct thrombocytopenia in moderate to severe dengue patients - a phase II randomized controlled clinical trial.

Background: The dengue-infected patients with or without hemorrhagic manifestations, typically exhibit moderate to severe thrombocytopenia. A thrombopoietin receptor agonist - eltrombopag has been efficacious in correcting thrombocytopenia in patients with various pathological conditions including immune thrombocytopenia, chronic liver disease, and severe aplastic anemia. This study investigated the efficacy and safety of eltrombopag to correct dengue-mediated thrombocytopenia.

A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh.

Aim: Attainment of sustainable development goal (SDG) targets requires reducing the rate of new hepatitis B virus (HBV)-induced infection and mortality rate to 90% and 65%, respectively, by 2030. Therefore, it is important to investigate the feasibility of reducing the required rates of HBV-induced infection and death incidents at the current rate of vaccination coverage in Bangladesh. Moreover, factors influencing vaccination coverage like negative bias toward girls during immunization can affect the current vaccination program and ultimately hinder the efforts to reduce HBV-induced infection and death rates.

Predicting and Designing Epitope Ensemble Vaccines against HTLV-1.

The infection mechanism and pathogenicity of Human T-lymphotropic virus 1 (HTLV-1) are ambiguously known for hundreds of years. Our knowledge about this virus is recently emerging. The purpose of the study is to design a vaccine targeting the envelope glycoprotein, GP62, an outer membrane protein of HTLV-1 that has an increased number of epitope binding sites.

Screening of Human Proteins for Fluoride and Aluminum Binding.

Previous studies showed that prolonged exposure to fluoride (F-) and aluminum (Al3+) ions is associated with numerous diseases including neurological disorders. They don't have any known biological function. But they can bind with proteins that interact with ions similar to them.

Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA.

Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC).