Bi-directional Relationship of in Vitro Mast Cell-Nerve Communication Observed by Confocal Laser Scanning Microscopy.
Bibliographic record
Abstract
Communication between nerves and mast cells is a prototypic demonstration of neuro-immune interaction. Recently, we used an in vitro co-culture approach comprising cultured murine superior cervical ganglia (SCG) and rat basophilic leukemia (RBL) cells to study this interaction. Previously, we concentrated mainly on the activation signal from neurites to mast cells (RBL). However, it is proposed that mast cell-nerve communication is not a one-sided relationship but a bi-directional one. In the present work, we studied the communication from mast cells to neurites. We observed that binding of anti-IgE receptor antibodies to mast cells increases calcium ion concentration [Ca2+]i in SCG neurites. This indicates that mast cell-nerve communication is bi-directional. Confocal fluorescence microscopic images indicated that [Ca2+]i in neurites increased after an increase of [Ca2+]i in mast cells. The lag-time of neurite activation was several times longer than that of mast cell activation. The correlation coefficient between the lag-times for mast cell and nerve activation was calculated to be 0.81. In addition, the fluorescence images showed that calcium signals in SCG neurites were able to extend to a long distance (100-200 microm) from the site where mast cells (RBL) attached to neurites.
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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 | 0.001 |
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".