Propofol and thiopental depress gap junction formation in a preneuronal cell line
Bibliographic record
Abstract
A-220 Background and goal of the study: Gap junction communication is widespread throughout the mammalian nervous system, among neurones as well as glia. We addressed the hypothesis that propofol and thiopental block gap junction mediated coupling of preneuronal cells. Materials and methods: We characterized the extent of dye coupling over time in the P19 preneuronal cell line taking advantage of an established method, the seeding technique. The effects of various general anaesthetics were determined using this technique by cell count and flow cytometric analysis. Calcein-AM, a low molecular weight molecule which passes through most gap junctions, was used to evaluate intercellular connections. CM-Dil, which has a large molecular size, was used to identify donor cells. Results and discussion: Clinically relevant concentrations of propofol and thiopental and high concentrations of halothane were effective in reducing gap junction permeability in P19 cell cultures. Halothane (1.8 mM) then propofol (15 μM) and thiopental (10 μM) depressed dye coupling to a greater degree. Carbenoxolone, a gap junction blocker, was used as a positive control agent for these experiments (see Figure 1; statistical significance was determined usinf ANOVA and Mann-Whitney U-test between: *propofol, thiopental and halothane from control; **propofol and thiopental from halothane; ***propofol and thiopental).FigureConclusions: Clinically relevant concentrations of propofol and thiopental depress gap junction permeability in P19 preneuronal cell lines. Halothane in very high concentrations is more potent in depressing gap junction formation compared with clinically relevant concentrations of i.v. anaesthetics.
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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) | 0.002 | 0.001 |
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".