Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.

Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.

The Haemagglutinin Gene of Bovine-Origin H5N1 Influenza Viruses Currently Retains Receptor-binding and pH-fusion Characteristics of Avian Host Phenotype.

Clade 2.3.4.

Autologous Hematopoietic Cell Transplant as an Effective Treatment Modality for Systemic Sclerosis and Multiple Myeloma.

Systemic sclerosis (SSc) is a multi-system disease characterized by a dysregulated immune system. Autologous hematopoietic cell transplantation (AHCT) is the only treatment that has been shown to confer significant benefit in controlling disease and improving survival for patients with SSc. A diagnosis of multiple myeloma (MM) after the diagnosis of SSc is rare and optimal treatment in such cases remains unclear.

Comparative Efficacy and Safety of Tissue Plasminogen Activators (tPA) in Acute Ischemic Stroke: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials.

Background: Intravenous alteplase (ALT) is the standard treatment for acute ischemic stroke (AIS). However, recent trials comparing other tissue plasminogen activators (tPAs) like tenecteplase (TNK) and reteplase with ALT have yielded conflicting results. This necessitated a network meta-analysis to compare the efficacy and safety of various tPAs in AIS patients.

A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques.

Background: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.

Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).