Anesthetics Act in Quantum Channels in Brain Microtubules to Prevent Consciousness
Bibliographic record
Abstract
The mechanism by which anesthetic gases selectively prevent consciousness and memory (sparing non-conscious brain functions) remains unknown. At the turn of the 20(th) century Meyer and Overton showed that potency of structurally dissimilar anesthetic gas molecules correlated precisely over many orders of magnitude with one factor, solubility in a non-polar, 'hydrophobic' medium akin to olive oil. In the 1980s Franks and Lieb showed anesthetics acted in such a medium within proteins, suggesting post-synaptic membrane receptors. But anesthetic studies on such proteins yielded only confusing results. In recent years Eckenhoff and colleagues have found anesthetic action in microtubules, cytoskeletal polymers of the protein tubulin inside brain neurons. 'Quantum mobility' in microtubules has been proposed to mediate consciousness. Through molecular modeling we have previously shown: (1) olive oil-like non-polar, hydrophobic quantum mobility pathways ('quantum channels') of tryptophan rings in tubulin, (2) binding of anesthetic gas molecules in these channels, and (3) capabilities for π-electron resonant energy transfer, or exciton hopping, among tryptophan aromatic rings in quantum channels, similar to photosynthesis protein quantum coherence. Here, we show anesthetic molecules can impair π-resonance energy transfer and exciton hopping in tubulin quantum channels, and thus account for selective action of anesthetics on consciousness and memory.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".