Neurotrophin‐3, but not nerve growth factor, promotes survival of human intestinal mast cells
Bibliographic record
Abstract
Neurotrophins are potent regulators of neuronal cell survival and function. Nerve growth factor (NGF) was shown to reduce apoptosis in cord blood-derived mast cells. Here, we examined the effect of the neurotrophins NGF and neurotrophin (NT)-3 on survival and mediator release of human intestinal mast cells. Mast cells isolated from normal intestinal tissue were cultured in the presence of NGF, NT-3, or stem cell factor (SCF) alone or in the presence of SCF together with each neurotrophin. NGF or NT-3 alone did not promote mast cell survival. In contrast, mast cell recovery was increased twofold when mast cells were cultured with NT-3 in addition to SCF for 14 days compared with control. Mast cell recovery was further increased following a combined addition of NT-3, SCF and IL-4. NT-3 mediated mast cell growth was dependent on the primary receptor for NT-3 TrkC. NGF in combination with SCF or with SCF and IL-4 showed no effect on mast cell survival. Histamine release and histamine content per mast cell remained unchanged, whereas leukotriene C4 release decreased if mast cells were cultured with NGF or NT-3 in addition to SCF. In summary, NT-3 affects mature human mast cells by promoting mast cell survival, whereas NGF does not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".