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Record W4400029612 · doi:10.1002/cptc.202400164

Antimicrobial Efficacy of Photocaged β‐Lapachone in <i>Bacillus subtilis</i> Biofilms

2024· article· en· W4400029612 on OpenAlexafffund
Elyse Hudson, Christabel Faylinn, Ivonne R. Lopez-Miranda, Josh Milstein, Andrew A. Beharry

Bibliographic record

VenueChemPhotoChem · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicBioactive Compounds and Antitumor Agents
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsBacillus subtilisAntimicrobialBiofilmMicrobiologyChemistryBiologyBacteria

Abstract

fetched live from OpenAlex

Abstract With the rise of antibiotic resistance within clinical settings, combating the growth of microbial biofilms presents a unique challenge. Biofilm‐inhabiting bacteria are embedded within a self‐produced, protective matrix, which can reduce the efficacy of treatment. The naturally derived product β‐lapachone is an appealing therapeutic agent that has been reported to inhibit biofilm growth. However, its off‐target toxicity and poor metabolic stability pose a significant hurdle for its application in vivo. Using a photo‐pharmacological approach via a coumarin‐based photocage, the reactivity of β‐lapachone can be tuned so it only becomes active once the photocage is removed. Here we report both the photo‐uncaging efficiency and the effective inhibition concentration of photocaged β‐lapachone within model Bacillus subtilis biofilms. Additionally, the mechanism of action is analyzed with results supporting catalase inhibition. This novel light‐activatable anti‐microbial has potential applications in medical settings to inhibit biofilm growth and provide synergistic treatment with traditional antibiotics.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.409
Teacher spread0.315 · 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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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