A comparison between interactions of triglyceride oil and mineral oil with proteins and their ability to reduce cleanser surfactant‐induced irritation
Bibliographic record
Abstract
OBJECTIVE: Skin irritation in personal cleansing has been correlated with surfactant binding with stratum corneum proteins. Polar and non-polar oils are increasingly being used in cleansing formulations which contain high (10-15%) level of anionic and non-ionic surfactants. However, the effects of oils in modulating skin damage from a cleansing product have not been studied in any detail. The objectives of this study are to determine whether low-viscosity polar and non-polar oils differ in their ability to reduce surfactant-induced skin irritation and, if so, how it might be related to their interactions with proteins. METHODS: Surfactant-induced skin irritation was measured by a 14-day in vivo cumulative patch irritation test. The methodology was similar to the well-known soap chamber test. Surfactant interactions with the water-soluble protein, bovine serum albumin (BSA), in the presence of oils were measured by conductometric titration. The effects of low-viscosity polar and non-polar oils on stratum corneum protein dynamics in the sulfhydryl group region were studied by electron paramagnetic resonance (EPR) using the covalently bound spin-label 3-maleimido-proxyl (5-MSL). EPR measurements were performed with stratum corneums obtained from discarded skins of 3- to 4-week-old female pigs. Simulation of the complex spectra provided insights on the environment and mobility of the protein-bound spin label. RESULTS: Addition of 1% polar sunflower seed oil (viscosity 42 centipoise) reduced in vivo irritation of 1% sodium lauryl ether sulphate with two ethoxylate/cocamidopropyl betaine (SLES/CAPB) by 20%, whereas 1% non-polar mineral oil (viscosity 15 centipoise) had no effect. Polar oil glyceryl trioleate (a major component in sunflower seed oil) at 10% level reduced surfactant binding to BSA protein in water by 40%, whereas the non-polar oil dodecane (a major component of mineral oil) at a similar level did not have any effect. The mobility of the spin label in a dry corneum was very low and was increased significantly with the addition of water and glycerol trioleate but less so with mineral oil. CONCLUSION: Sunflower seed oil reduces surfactant-induced in vivo skin irritation more than mineral oil. This is possibly due to stronger interaction of polar oil with proteins, thus protecting it from surfactant binding.
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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".