Exploration of the mechanisms of unconsciousness induced by propofol with positron emission tomography (PET) functional brain imaging
Bibliographic record
Abstract
In anesthesia practice, consciousness is often equated with the waking state and with the ability to respond to stimuli in the integrated manner. The reversible loss of consciousness is induced by the general anesthetic, which have a wide range of molecular structure and physicochemical characteristics. The mechanisms of unconsciousness induced by anesthetic agents are not well understood. The studies I have conducted for my Ph.D. have focused on how anesthetic drugs produce unconsciousness in human subjects. In two separate PET studies, receptor imaging and regional CBF analysis were used to examine the unconsciousness induced by propofol, a popular general anesthetic. The first study evaluated kinetic analysis methods for estimation of the receptor availability of the muscarinic receptor using dynamic positron emission tomography (PET) studies with [N-11C-methyl]-benztropine. The study also investigated the effect of propofol on central muscarinic receptor availability during general anesthesia. The results of this study suggested the propofol-related reductions in muscarinic receptor availability. The second study identified the brain function changes specifically linked to the difference in levels of consciousness. We used physostigmine (an anticholinestherase) to restore consciousness in the subjects anesthetized with a constant concentration of propofol. The results revealed that the thalamus and precuneus/cuneus jointly play a critical role in controlling the changes in the level of consciousness during general anesthesia.
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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 | 0.001 |
| 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".