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Record W4313640810 · doi:10.3389/fmicb.2022.987164

Effect of chemical modifications of tannins on their antimicrobial and antibiofilm effect against Gram-negative and Gram-positive bacteria

2023· article· en· W4313640810 on OpenAlexaboutno aff
Xabier Villanueva, Lili Zhen, José Núñez Ares, Thijs Vackier, Heiko Lange, Claudia Crestini, Hans Steenackers

Bibliographic record

VenueFrontiers in Microbiology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicTannin, Tannase and Anticancer Activities
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeMinistero dell’Istruzione, dell’Università e della RicercaUniversità degli Studi di Milano-Bicocca
KeywordsBiofilmAntimicrobialBacteriaProanthocyanidinDerivatizationChemistryPseudomonas aeruginosaAntibacterial activityEscherichia coliAmmoniumGram-negative bacteriaGram-positive bacteriaMicrobiologyBiochemistryBiologyOrganic chemistryPolyphenolAntioxidant

Abstract

fetched live from OpenAlex

Background: Tannins have demonstrated antibacterial and antibiofilm activity, but there are still unknown aspects on how the chemical properties of tannins affect their biological properties. We are interested in understanding how to modulate the antibiofilm activity of tannins and in delineating the relationship between chemical determinants and antibiofilm activity. Materials and methods: was determined in the Calgary biofilm device. Results: Most of the unmodified tannins exhibited specific antibiofilm activity against the assayed bacteria. The chemical modifications were found to alter the antibiofilm activity level and spectrum of the tannins. A positive charge introduced by derivatization with higher amounts of ammonium groups shifted the anti-biofilm spectrum toward Gram-negative bacteria, and derivatization with lower amounts of ammonium groups and acidifying derivatization shifted the spectrum toward Gram-positive bacteria. Furthermore, the quantity of phenolic OH-groups per molecule was found to have a weak impact on the anti-biofilm activity of the tannins. Conclusion: We were able to modulate the antibiofilm activity of several tannins by specific chemical modifications, providing a first approach for fine tuning of their activity and antibacterial spectrum.

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)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Citations60
Published2023
Admission routes1
Has abstractyes

Explore more

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