Genomic Insights into Colistin and Tigecycline Resistance in ESBL-Producing and Harboring Genes in Ecuador.

() and () are resistant to third-generation cephalosporins (3GCs), carbapenems, colistin, and tigecycline, making them a major public health priority, mainly within the developing world. However, their genomic epidemiology and possible determinants of resistance remain to be elucidated. Thus, this study aimed to perform a genomic characterization of and , both of which are resistant to last-line antibiotics, isolated from humans, poultry, and a dairy farm environment within Ecuador.

Arbuscular mycorrhizal colonization defines root ecological strategies in an extreme arid environment.

The symbiosis between mycorrhizae fungi and plant roots is essential for plant establishment in nearly all terrestrial ecosystems. However, the role of mycorrhizal colonization (colM) in shaping root ecological strategies remains poorly understood. Emerging research identifies colM as a key trait influencing the multidimensional covariation of root traits within the Root Economic Space (RES), where a 'collaboration gradient' is proposed.

One-step bioprinting of endothelialized, self-supporting arterial and venous networks.

Advances in biofabrication have enabled the generation of freeform perfusable networks mimicking vasculature. However, key challenges remain in the effective endothelialization of these complex, vascular-like networks, including cell uniformity, seeding efficiency, and the ability to pattern multiple cell types. To overcome these challenges, we present an integrated fabrication and endothelialization strategy to directly generate branched, endothelial cell-lined networks using a diffusion-based, embedded 3D bioprinting process.

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.