Vitamin D metabolites and analytical challenges.

Vitamin D is an essential micronutrient for bone health and the general cellular functions of the body. Its insufficiency/deficiency leads to the pathophysiology of disorders like diabetes, cancer, autoimmune, neurodegenerative, and cardiovascular diseases. Clinical interest in Vitamin D metabolites and their role in various medical disorders have contributed to an increase in laboratory demands for vitamin D measurements.

Serum Inflammatory Markers in Patients with Guillain Barre Syndrome.

Background: Guillain Barre syndrome (GBS) is an immune-mediated peripheral neuropathy characterized by the demyelination and axonal damage of the peripheral neurons. The pathogenesis of GBS involves the breakdown of the blood-brain barrier after which pro inflammatory cytokines attack the neurons in the peripheral nervous system.

Aims: This study aims to evaluate five markers, namely matrix metalloproteinase (MMP)-2 and MMP-9, vascular endothelial growth factor (VEGF)-A, basic fibroblast growth factor (bFGF), and SFLT-1, which could have a role in the inflammatory response in patients with GBS and healthy controls.

Assessment for learning: a needs analysis study using formative assessment to evaluate the need for curriculum reform in basic sciences.

A needs analysis study for curriculum reform in basic sciences was conducted at Melaka Manipal Medical College, India, by means of a formative assessment method, namely Basic Science Retention Examination (BSRE). Students participated in a BSRE, which comprised recall and clinical multiple-choice questions in six discipline areas. They also rated the clinical relevance of each question and provided responses to three open-text questions about the exam.

Deep learning improves antimicrobial peptide recognition.

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates.

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.

Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity.