Effect of Structure on the Interactions between Five Natural Antimicrobial Compounds and Phospholipids of Bacterial Cell Membrane on Model Monolayers
Bibliographic record
Abstract
Monolayers composed of bacterial phospholipids were used as model membranes to study interactions of the naturally occurring phenolic compounds 2,5-dihydroxybenzaldehyde and 2-hydroxy-5-methoxybenzaldehyde, and the plant essential oil compounds carvacrol, cinnamaldehyde, and geraniol, previously found to be active against both Gram-positive and Gram-negative pathogenic microorganisms. The lipid monolayers consist of 1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (DPPE), 1,2-dihexa- decanoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (DPPG), and 1,1',2,2'-tetratetradecanoyl cardiolipin (cardiolipin). Surface pressure-area (π-A) and surface potential-area (Δψ-A) isotherms were measured to monitor changes in the thermodynamic and physical properties of the lipid monolayers. Results of the study indicated that the five compounds modified the three lipid monolayer structures by integrating into the monolayer, forming aggregates of antimicrobial -lipid complexes, reducing the packing effectiveness of the lipids, increasing the membrane fluidity, and altering the total dipole moment in the monolayer membrane model. The interactions of the five antimicrobial compounds with bacterial phospholipids depended on both the structure of the antimicrobials and the composition of the monolayers. The observed experimental results provide insight into the mechanism of the molecular interactions between naturally-occurring antimicrobial compounds and phospholipids of the bacterial cell membrane that govern activities.
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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 | 0.001 |
| 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".