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Record W2525225058 · doi:10.1128/mbio.01541-16

Antibiotic Resistance and Regulation of the Gram-Negative Bacterial Outer Membrane Barrier by Host Innate Immune Molecules

2016· article· en· W2525225058 on OpenAlexfundno aff
Samuel I. Miller

Bibliographic record

VenuemBio · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesUniversity of British Columbia
KeywordsBacterial outer membranePorinInnate immune systemAntibioticsMicrobiologyContext (archaeology)BacteriaBiogenesisAntibiotic resistanceBiologyGram-negative bacteriaImmune systemMembraneChemistryCell biologyBiochemistryImmunologyEscherichia coli

Abstract

fetched live from OpenAlex

The Gram-negative outer membrane is an important barrier that provides protection against toxic compounds, which include antibiotics and host innate immune molecules such as cationic antimicrobial peptides. Recently, significant research progress has been made in understanding the biogenesis, regulation, and functioning of the outer membrane, including a recent paper from the laboratory of Dr. Brett Finlay at the University of British Columbia (J. van der Heijden et al., mBio 7:e01238-16, 2016, http://dx.doi.org/10.1128/mBio.01541-16). These investigators demonstrate that toxic oxygen radicals, such as those found in host tissues, regulate outer membrane permeability by altering the outer membrane porin protein channels to regulate the influx of oxygen radicals as well as β-lactam antibiotics. This commentary provides context about this interesting paper and discusses the prospects of utilizing increased knowledge of outer membrane biology to develop new antibiotics for antibiotic-resistant Gram-negative bacteria.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.004
GPT teacher head0.185
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

Citations251
Published2016
Admission routes1
Has abstractyes

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