Effects of Various Small-Molecule Anesthetics on Vesicle Fusion: A Study Using Two-Photon Fluorescence Cross-Correlation Spectroscopy
Bibliographic record
Abstract
Currently, the molecular mechanism for membrane fusion remains unconfirmed. The most compelling suggested mechanism is the stalk hypothesis, which states that membrane fusion proceeds via stalk formation/hemifusion, among other steps. Because the stalk would have a very high radius of curvature, small lipophilic molecules could enhance fusion by lowering the energy barrier to stalk formation. We previously showed that the general anesthetic halothane is capable of inducing membrane fusion in 1,2-dileoyl-sn-3-glycero-3-phospocholine (DOPC) vesicles. In the present study, we examined other small molecules, general anesthetics (chloroform, isoflurane, enflurane, and sevoflurane), to determine whether they exhibit fusion properties with model lipid membranes similar to those of halothane. We employed both two-photon excitation fluorescence cross-correlation spectroscopy (TPE-FCCS) and steady-state fluorescence dequenching (FD) assays. Using volatile general anesthetics as novel fusion agents, we also aimed to gain a better understanding of the membrane fusion mechanism at a molecular level. We found that lipid mixing or lipid rearrangement, which is required for the formation of the fusion-state intermediates and the fusion pore, rather than the association of lipid vesicles, is rate-limiting. In addition, halothane and chloroform were found to induce lipid mixing (rearrangement) to a greater extent than isoflurane, enflurane, and sevoflurane. Finally, it is proposed that the efficiency of these general anesthetics as fusion agents is related to their partition coefficients, water solubilities, polarities, and molecular volumes, all of which affect the ability of each anesthetic to perturb the contacting bilayer membranes of fusing vesicles.
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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 | 0.001 | 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 | 0.001 |
| 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".