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Record W4392779328 · doi:10.53555/sfs.v10i6.2295

The Role Of Lipopolysaccharide Modification By Amino Arabinose In Extreme Polymyxin Resistance In Serratia Marcescens

2023· article· en· W4392779328 on OpenAlexvenueno aff
Fayez Abdullah Saeed Almutiri, Nourh Ghanim Khalaf Al-Shammari, Sameerah Salem Hamzah Alkhabiry, Mohammed F. Almutairi, Saeed Masfer Algahtani, Mansour Faihan Almotairi, Mohammed Battah Alenazi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
Fundersnot available
KeywordsSerratia marcescensPolymyxinMicrobiologyLipopolysaccharidePolymyxin BSerratiaChemistryMedicineBiologyBacteriaBiochemistryImmunologyAntibioticsEscherichia coliPseudomonasGenetics

Abstract

fetched live from OpenAlex

Lipopolysaccharide (LPS) modification is a key mechanism that bacteria use to resist the action of antimicrobial peptides such as polymyxins. Serratia marcescens, in particular, has been known to develop extreme resistance to polymyxins due to the modification of its LPS with aminoarabinose. This essay aims to clarify the role of aminoarabinose in extreme polymyxin resistance in Serratia marcescens by evaluating the literature on this topic. The results suggest that aminoarabinose modification plays a crucial role in conferring resistance to polymyxins in this bacterium by altering the interaction between LPS and the polymyxin molecule. This finding provides valuable insights into the mechanisms of polymyxin resistance and may guide the development of new strategies to combat multidrug-resistant.

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 score0.001

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)0.0000.000

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 head0.098
GPT teacher head0.274
Teacher spread0.176 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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