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Record W4256036356 · doi:10.1002/ange.201500598

Amphiphilic Tobramycins with Immunomodulatory Properties

2015· article· en· W4256036356 on OpenAlexaff
Goutam Guchhait, Anthony Altieri, Balakishan Gorityala, Xuan Yang, Brandon Findlay, George G. Zhanel, Neeloffer Mookherjee, Frank Schweizer

Bibliographic record

VenueAngewandte Chemie · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsHealth Sciences CentreUniversity of Manitoba
Fundersnot available
KeywordsLipopolysaccharideImmune systemInnate immune systemAntibioticsSeptic shockBacteriaAmphiphileChemistryMicrobiologyMode of actionTumor necrosis factor alphaImmunityBiologyCell biologySepsisImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Amphiphilic aminoglycosides (AAGs) are an emerging source of antibacterials to combat infections caused by antibiotic‐resistant bacteria. Mode‐of‐action studies indicate that AAGs predominately target bacterial membranes, thereby leading to depolarization and increased permeability. To assess whether AAGs also induce host‐directed immunomodulatory responses, we determined the AAG‐dependent induction of cytokines in macrophages in the absence or presence of lipopolysaccharide (LPS). Our results show for the first time that AAGs can boost the innate immune response, specifically the recruitment of immune cells such as neutrophils required for the resolution of infections. Moreover, AAGs can selectively control inflammatory responses induced in the presence of endotoxins to prevent septic shock. In conclusion, our study demonstrates that AAGs possess multifunctional properties that combine direct antibacterial activity with host‐directed clearance effects reminiscent of those of host‐defense peptides.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.212
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2015
Admission routes1
Has abstractyes

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