Yeast Viability in HLD–NAC-Designed Fully Dilutable Lecithin-Linker Microemulsions
Bibliographic record
Abstract
Using microemulsions (µEs) as preservation media for cells was pursued in the 1990s; however, the difficulty in formulating biocompatible µEs and keeping unacclimatized cells alive for more than three days hindered developments in this area. This work explores the use of fully dilutable self-microemulsifying delivery systems (SMEDS) formulated with lecithin (Le) and polyglycerol-10-caprylate (PG10C) at a ratio of 2/5. This surfactant blend was mixed with ethyl oleate (EOL) at a ratio of 60 surfactant/40 EOL to produce a D60 dilution line. This D60 SMEDS was diluted with 0.9% w/v NaCl solution to produce lecithin-linker µEs (LLMs). The properties of the resulting LLMs were predicted using the hydrophilic–lipophilic-difference (HLD) and net-average curvature (NAC) model, indicating that LLMs with aqueous content from 5% to 60% are bicontinuous, confirmed via viscosity and conductivity. The largest yeast activity and viability obtained with LLMs were achieved with 30% aqueous content, resulting from the balance between having enough water for the effective transport of metabolites, enough SMEDS to contribute nutrients and lipids, and a low enough water to limit the partition of PG10C that, when present in the aqueous phase, inhibited yeast activity. For SMEDS, its low water activity ensured that the yeast remained dormant, keeping them alive for at least 10 weeks.
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) | 0.000 | 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".