Anesthetics significantly increase the amount of intramembrane water in lipid membranes
Bibliographic record
Abstract
The potency of anesthesia was directly linked to the partitioning of the drug molecules in cell membranes by Meyer and Overton. Many molecules interact with lipid bilayers and lead to structural and functional changes. It remains an open question which change in membrane properties is responsible for a potential anesthetic effect or if anesthetics act by binding to direct targets. We studied the effect of ethanol, diethyl ether and isoflurane on the water distribution in lipid bilayers by combining all-atom molecular dynamics simulations and neutron diffraction experiments. The simulations show strong membrane-drug interactions with partitioning coefficients of 38%, 92% and 100% for ethanol, diethyl ether and isoflurane, respectively, and provide evidence for an increased water partitioning in the membrane core. The amount of intramembrane water molecules was experimentally determined by selectively deuterium labeling lipids, anesthetic drug and water molecules in neutron diffraction experiments. Four additional water molecules per lipid were observed in the presence of ethanol. Diethyl ether and isoflurane were found to significantly increase the amount of intramembrane water by 25% (8 water molecules). This increase in intramembrane water may contribute to the non-specific interactions between anesthetics and lipid membranes.
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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 | 0.000 |
| 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".