Fluorescence quenching of gramicidin D in model membranes by halothane
Bibliographic record
Abstract
Inhaled anesthetics were introduced in surgery over a century ago. To this day, the molecular mechanism of anesthetic action remains largely unknown. However, ion-channels of neuronal membranes are believed to be the most- likely molecular targets of inhaled anesthetics. In the study presented here, we investigated the interaction of a simplified ion-channel system, gramicidin, with halothane, a small haloalkane inhaled anesthetic in various environments. Fluorescence-quenching experiments of gramicidin D in dioleoylphosphatidylcholine (DOPC) large unilamellar vesicles (LUVS) have shown that halothane can directly interact with the ion channel (K SV = 66 M –1 ). Halothane quenched the fluorescence from tryptophan residues located at the lipid bilayer – aqueous interfaces as well as those tryptophans located deeper in the bilayer. Quenching data from gramicidin D in sodium dodecyl sulfide (SDS) micelles revealed that the tryptophan residues located at the micelle–solvent interface were preferentially quenched by halothane (K SV = 22 M –1 ). In 1-octanol, fluorescence quenching was observed, but with a lower K SV value (K SV = 6 M –1 ) than in DOPC LUVS and SDS micelles. Taken together, these results indicate that halothane interactions with gramicidin, mediated by a lipid bilayer, are the strongest, and that the mechanism of anesthetic action may also be lipid-mediated.
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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) | 0.000 | 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".