Modulating photochemical reactions in Langmuir monolayers of Escherichia coli lipid extract with the binding mechanisms of eosin decyl ester and toluidine blue-O photosensitizers
Bibliographic record
Abstract
Photodynamic damage to the cell envelope can inactivate microorganisms and may be applied to combat super-resistance phenomenon, empowered by the indiscriminate use of antibiotics. Efficiency in microbial inactivation is dependent on the incorporation of photosensitizers (PS) into the bacterial membranes to trigger oxidation reactions under illumination. Herein, Langmuir monolayers of Escherichia coli lipid extract were built to determine the binding mechanisms and oxidation outcomes induced by eosin decyl ester (EosDEC) and toluidine blue-O (TBO) PSs. Surface-pressure isotherms of the E. coli monolayers were expanded upon EosDEC and TBO, suggesting incorporation of both PSs. Fourier-transform infrared spectroscopy (FTIR) of Langmuir-Schaefer (LS) films reveled that the EosDEC and TBO binding mechanisms are dominated by electrostatic interactions with the anionic polar groups, with limited penetration into the chains. Light-irradiation reduced the relative area of E. coli monolayer on TBO, indicating an increased loss of material to the subphase owing to the chain cleavage, generated by contact-dependent reactions with excited states of TBO. In contrast, the increased relative area of E. coli monolayers containing EosDEC suggests lipid hydroperoxidation, which is PS contact-independent. Even considering a small chain penetration, the saturated EosDEC may have partitioned towards saturated reach domains, avoiding direct contact with membrane unsaturations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".