Effect of monoglycerides and fatty acids on a ceramide bilayer
Bibliographic record
Abstract
Monoglycerides and unsaturated fatty acids, naturally present in trace amounts in the stratum corneum (top layer of skin) lipid matrix, are commonly used in pharmaceutical, cosmetic and health care formulations. However, a detailed molecular understanding of how the oil additives get incorporated into the skin lipids from topical application and, once incorporated, how they affect the properties and integrity of the lipid matrix remains unexplored. Using ceramide 2 bilayers as skin lipid surrogates, we use a series of molecular dynamics simulations with six different natural oil ingredients at multiple concentrations to investigate the effect of the oils on the properties and stability of the bilayers. The six oils: monoolein, monostearin, monoelaidin, oleic acid, stearic acid and linoleic acid - all having the same length of the alkyl chain, C18, but a varying degree of saturation, allow us to systematically address the effect of unsaturation in the additives. Our results show that at low oil concentration (∼5%) the mixed bilayers containing any of the oils and ceramide 2 (CER2) become more rigid than pure CER2 bilayers due to more efficient lipid packing. Better packing also results in the formation of larger numbers of hydrogen bonds between the lipids, which occurs at the expense of the hydrogen bonds between lipids and water. The mixed bilayers with saturated or trans-unsaturated oils remain stable over the whole range of oil concentration. In contrast, the presence of the oils with at least one cis-double bond leads to bilayer instability and complete loss of bilayer structure at the oil content of about 50-65%. Two cis-double bonds in the lipid tail induce bilayer disruption at even lower concentration (∼30%). The mixed bilayers remain in the gel phase (without melting to a fluid phase) until the phase transition to a non-bilayer phase occurs. We also demonstrate that the stability of the bilayer strongly correlates with the order parameter of the lipid tails.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".