Unravelling the involvement of protein disorder in cyanobacterial stress responses.

This study has explored the involvement of Intrinsically Disordered Proteins (IDPs) in cyanobacterial stress response. IDPs possess distinct physicochemical properties, which allow them to execute diverse functions. Anabaena PCC 7120, the model photosynthetic, nitrogen-fixing cyanobacterium encodes 688 proteins (11 % of the total proteome) with at least one intrinsically disordered region (IDR).

Modulation of oxidative stress machinery determines the contrasting ability of cyanobacteria to adapt to Se(VI) or Se(IV).

Excess of selenium (Se) in aquatic ecosystems has necessitated thorough investigations into the effects/consequences of this metalloid on the autochthonous organisms exposed to it. The molecular details of Se-mediated adaptive response remain unknown in cyanobacteria. This study aims to uncover the molecular mechanisms driving the divergent physiological responses of cyanobacteria on exposure to selenate [Se(VI)] or selenite [Se(IV)], the two major water-soluble oxyanions of Se.

Novel silver nanoparticle-antibiotic combinations as promising antibacterial and anti-biofilm candidates against multiple-antibiotic resistant ESKAPE microorganisms.

Hypothesis: The emergence of Multiple Antibiotic Resistance (MAR) in ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogens is a global challenge to public health. The inherent antimicrobial nature of silver nanoparticles (AgNPs) makes them promising antimicrobial candidates against antibiotic-resistant pathogens.

MapperPlus: Agnostic clustering of high-dimension data for precision medicine.

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability.