Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies

Autor: Erica J. Zheng, Ian W. Andrews, Alexandra T. Grote, Abigail L. Manson, Miguel A. Alcantar, Ashlee M. Earl, James J. Collins

Publikováno v: Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)

Antibiotic tolerance, or the ability of bacteria to survive antibiotic treatment in the absence of genetic resistance, often involves a low metabolic state. Here, Zheng et al. show that tolerance does not readily evolve against antibiotics whose effi

Externí odkaz: https://doaj.org/article/7c9a36880bc84974902377009584c79c

Blood volume measurement using cardiovascular magnetic resonance and ferumoxytol: preclinical validation

Autor: Rajiv Ramasawmy, Toby Rogers, Miguel A. Alcantar, Delaney R. McGuirt, Jaffar M. Khan, Peter Kellman, Hui Xue, Anthony Z. Faranesh, Adrienne E. Campbell-Washburn, Robert J. Lederman, Daniel A. Herzka

Publikováno v: Journal of Cardiovascular Magnetic Resonance, Vol 20, Iss 1, Pp 1-10 (2018)

Abstract Background The hallmark of heart failure is increased blood volume. Quantitative blood volume measures are not conveniently available and are not tested in heart failure management. We assess ferumoxytol, a marketed parenteral iron supplemen

Externí odkaz: https://doaj.org/article/d6fa3b86054d4da182c98c384f8541e4

Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials.

Autor: James T Yurkovich, Miguel A Alcantar, Zachary B Haiman, Bernhard O Palsson

Publikováno v: PLoS Computational Biology, Vol 14, Iss 8, p e1006356 (2018)

Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly des

Externí odkaz: https://doaj.org/article/236d2f241ebb48b8a06c628e29c56c0f

A CRISPR-based assay for the detection of opportunistic infections post-transplantation and for the monitoring of transplant rejection

Autor: Michael M, Kaminski, Miguel A, Alcantar, Isadora T, Lape, Robert, Greensmith, Allison C, Huske, Jacqueline A, Valeri, Francisco M, Marty, Verena, Klämbt, Jamil, Azzi, Enver, Akalin, Leonardo V, Riella, James J, Collins

Publikováno v: Nature biomedical engineering. 4(6)

In organ transplantation, infection and rejection are major causes of graft loss. They are linked by the net state of immunosuppression. To diagnose and treat these conditions earlier, and to improve long-term patient outcomes, refined strategies for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::b9788887dd186593d859377bd5fb4d55
https://pubmed.ncbi.nlm.nih.gov/32284553

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Autor: Douglas McCloskey, Allison J. Lopatkin, Lars Schrübbers, Sarah N Wright, Graham C. Walker, Sangeeta Satish, Bernhard O. Palsson, Amir Nili, Miguel A. Alcantar, Meagan Hamblin, Jason H. Yang, James J. Collins

Publikováno v: PMC
Cell
Yang, J H, Wright, S N, Hamblin, M, McCloskey, D, Alcantar, M A, Schrübbers, L, Lopatkin, A J, Satish, S, Nili, A, Palsson, B O, Walker, G C & Collins, J J 2019, ' A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action ', Cell, vol. 177, no. 6, pp. 1649-1661.e9 . https://doi.org/10.1016/j.cell.2019.04.016

© 2019 Elsevier Inc. Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated “white-box”

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a17b6e733d1b694dcad766ea0231ee40
https://hdl.handle.net/1721.1/135182