Genome sequences of bacteriophages that infect Typhi from Bangladesh.

This report presents near-complete genome sequences of 14 bacteriophages that infect Typhi, identified through environmental surveillance in Bangladesh between August 2021 and June 2022. The bacteriophages, belonging to the genera , , and , exhibit high degrees of sequence similarity and conserved genetic features with previously reported Typhi bacteriophages.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity.

Multifunctional silver nanoparticle embedded eri silk cocoon scaffolds against burn wounds-associated infection.

Antimicrobial wound dressings offer enhanced efficacy compared to conventional dressing platforms by limiting bacterial infections, expediting the healing process, and creating a barrier against additional wound contamination. The use of silk derived from silkworm cocoons in wound healing applications is attributed to its exceptional characteristics. Compared to mulberry silk, sericin from non-mulberry cocoons has higher water exchange mobility and moisture retention.

Repurposing of dibucaine and niflumic acid as antimicrobial agents in combination with antibiotics against Staphylococcus aureus.

The versatile human commensal bacteria and pathogen Staphylococcus aureus cause several community and hospital-acquired illnesses associated with significant morbidity and death. Antibiotic therapy for S. aureus infections has grown increasingly difficult as the organism has developed a wide spectrum of antibiotic resistance mechanisms.

A count-based model for delineating cell-cell interactions in spatial transcriptomics data.

Motivation: Cell-cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand-receptor correlations can be computed between cells or spots that are physically close in the tissue.