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Record W2799908293 · doi:10.1021/jacs.8b03575

Biodegradable Nanocomposite Antimicrobials for the Eradication of Multidrug-Resistant Bacterial Biofilms without Accumulated Resistance

2018· article· en· W2799908293 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the American Chemical Society · 2018
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthManning FundMassachusetts Life Sciences Center
KeywordsBiofilmMultiple drug resistanceBacteriaChemistryMicrobiologyAntimicrobialNanocompositeAntibiotic resistanceBiocompatible materialAntibioticsPenetration (warfare)NanotechnologyBiologyBiochemistryBiomedical engineeringMedicineMaterials science

Abstract

fetched live from OpenAlex

Infections caused by multidrug-resistant (MDR) bacteria are a rapidly growing threat to human health, in many cases exacerbated by their presence in biofilms. We report here a biocompatible oil-in-water cross-linked polymeric nanocomposite that degrades in the presence of physiologically relevant biomolecules. These degradable nanocomposites demonstrated broad-spectrum penetration and elimination of MDR bacteria, eliminating biofilms with no toxicity to cocultured mammalian fibroblast cells. Notably, serial passaging revealed that bacteria were unable to develop resistance toward these nanocomposites, highlighting the therapeutic promise of this platform.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.273
Teacher spread0.257 · 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