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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 OpenAlexfundno aff
Ryan F. Landis, Cheng‐Hsuan Li, Akash Gupta, Yi-Wei Lee, Mahdieh Yazdani, Nipaporn Ngernyuang, Ismail Altinbasak, Sanaa Mansoor, Muhammadaha A. S. Khichi, Amitav Sanyal, Vincent M. Rotello

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.

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 score0.000
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 head0.015
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

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

Citations130
Published2018
Admission routes1
Has abstractyes

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