Utilising Hyperspectral Autofluorescence Imaging in the Objective Assessment of Disease State and Pain in Patients with Rheumatoid Arthritis.

Rheumatoid Arthritis (RA) is a chronic inflammatory disease resulting in joint swelling and pain. Treatment options can be reliant on disease activity scores (DAS) incorporating patient global assessments, which are quantified via visual analogue scales (VAS). VAS can be subjective and not necessarily align with clinical symptoms, such as inflammation, resulting in a disconnect between the patient's and practitioners' experience.

Transforming agri-food value chains in Bangladesh: A practical application of blockchain for traceability and fair pricing.

The agricultural sector is a vital component of Bangladesh's economy, but its agri-food supply chain faces signifi-cant inefficiencies primarily due to the involvement of numerous intermediaries. This complexity not only reduces the profits for farmers but also affects the overall transparency and efficiency of the supply chain. This study aims to em-ploy blockchain technology to transform the traditional agri-food supply chain in Bangladesh, focusing on increasing transparency, enhancing efficiency, and improving profitability for farmers, thus potentially bolstering the entire agri-food ecosystem in the country.

Multispectral Imaging of Collagen, NAD(P)H and Flavin Autofluorescence in Mesenchymal Stem Cells Undergoing Trilineage Differentiation.

Understanding the molecular mechanisms of differentiation is important for regenerative medicine and developmental biology. This study aims to characterise the role of the glycolysis/oxidative phosphorylation balance as a driver of mesenchymal stem cell (MSC) differentiation. Cells were maintained in normal conditions or stimulated towards the MSC trilineage cell types over 21 days.

Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.

Motivation: Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs.