Interaction of Long-Chain Alcohol with Dry Yeast, Cholesterol, and Sea Firefly Luciferase
Bibliographic record
Abstract
Background: A hand sanitizer containing alcohol, usually ethanol or isopropanol, is typically used for disinfection, but given that cholesterol is one of the main components of virus envelopes, long-chain alcohol may be more effective. To better understand the potential disinfection activity of long-chain alcohols, we studied their interactions with dry yeast, cholesterol, and sea firefly luciferase. Methods and Results: We measured, at 30oC and 39oC, the minimum inhibition concentration (MIC) of dry yeast fermentation and the stability of cholesterol and sea firefly luciferase with alcohols, diols, cetyltrimethylammonium chloride, and stearyltrimethylammonium chloride. The MIC decreased with the chain length at C≤12 for dry yeast and cholesterol with alcohol at 30oC. At C13 and higher, the cut-off region was observed. At 39oC, the cut-off region shifted to C15 and higher. The reduction of MIC was measured with the diol or sea firefly luciferase at C≤14. Conclusion: The presence of the cut-off region is suggested to be related to whether the alcohol is in the liquid state. For the liquid alcohol, the longer the chain length, the lower the MIC. This suggests a potential disinfection activity of long-chain alcohol.
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 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".