Loss of vagal anti-inflammatory effect: in vivo visualization and adoptive transfer
Bibliographic record
Abstract
The vagus nerve is a conduit for bidirectional signaling between the brain and the viscera. Vagal signaling has been shown to downregulate gastrointestinal inflammation, and the mechanism is thought to involve acetylcholine binding to the alpha-7 subunit of the nicotinic acetylcholine receptor on macrophages. The aims of this study were to quantify the impact of vagotomy in vivo by visualizing nuclear factor (NF)-kappaB activity and to determine if the proinflammatory impact of vagotomy could be transferred by lymphocytes. Real-time biophotonic imaging revealed that subdiaphragmatic vagotomy resulted in increased levels of NF-kappaB in vivo. NF-kappaB activation was further exaggerated in vivo following exposure to 4% DSS for 5 days. Vagotomized animals also exhibited higher disease activity scores and secreted more proinflammatory cytokines. Adoptive transfer of CD4(+) T cells from vagotomized animals (but not CD4(+) T cells from sham-operated controls) to naive dextran sulfate sodium (DSS)-treated recipients resulted in increased inflammatory scores. Further examination of the CD4(+) T cells revealed that adoptive transfer of the CD25(-) population alone from vagotomized donors (but not sham-operated donors) was sufficient to aggravate colitis in DSS-treated recipients. Increased DSS-induced inflammation was associated with reduced CD4(+)CD25(+)Foxp3(+) regulatory T cell numbers in recipients. This study clearly demonstrates the ability of the vagus nerve to modulate activity of the proinflammatory transcription factor NF-kappaB in vivo. The proinflammatory effect of vagotomy is transferable using splenic T cells and highlights a previously unappreciated cellular mechanism for linking central parasympathetic processes with mucosal inflammation and immune homeostasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".