Redundancy or cell‐type‐specific regulation? Tumour necrosis factor in alveolar macrophages and mast cells
Bibliographic record
Abstract
Tumour necrosis factor (TNF) is an important inflammatory cytokine produced by several cell types. To test the hypothesis that there is cell-type-specific regulation and not redundancy of TNF production, we investigated its production by alveolar macrophages (AM) and peritoneal mast cells (PMC). Cell lysates of freshly isolated AM and PMC contained 9 +/- 3 pg and 57 +/- 17 pg of TNF/10(6) cells, respectively. Furthermore, unstimulated PMC expressed 4 x 10(3)-fold more attomols of TNF mRNA/microg total RNA compared with AM. These data may explain in part the greater TNF-dependent cytotoxicity of PMC. Furthermore, fixed PMC showed significantly higher TNF-dependent cytotoxic activity than AM (sevenfold), suggesting that PMC express more membrane TNF than AM. Although AM and PMC contain different amounts of TNF, antigen stimulation caused a similar release of TNF from sensitized rats. Interferon (IFN)-gamma, respectively, stimulated and inhibited AM and PMC TNF-dependent cytotoxicity whereas lipopolysaccharide (LPS) significantly stimulated TNF-dependent cytotoxicity in both cell types. However, TNF released (AM 400-fold and PMC threefold) and TNF mRNA expression, as measured by competitive reverse transcription-polymerase chain reaction (AM 7 x 10(3)-fold and PMC twofold), were considerably greater in LPS-stimulated AM than PMC. Our data indicate that TNF is differentially expressed in these two cell types and that its production is dependent on the nature of the stimulus. These data provide vital basis in experimental approaches aimed at modulating the effect of TNF in airway disease conditions involving both AM and mast cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".