Toll-like receptor 4-mediated activation of murine mast cells
Bibliographic record
Abstract
Toll-like receptors (TLRs) are a family of pattern recognition receptors that are critical for cellular responses to a variety of bacterial, viral, and fungal products. Mast cells are important to host survival in a number of models of bacterial infection and might act as sentinel cells in host defense. We therefore examined the expression of TLRs and associated molecules by murine bone marrow-derived mast cells (BMMCs). BMMCs and the murine mast cell line MC/9 expressed mRNA for TLR2, TLR4, and TLR6 but not TLR5 and for both adapter molecule MD-2 and signaling molecule MyD88 but lacked surface CD14. After activation with the TLR2- and TLR4-dependent stimuli Staphylococcus aureus-derived peptidoglycan and Escherichia coli-derived lipopolysaccharide (LPS), respectively, mast cells produced significant levels of interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-alpha). To determine whether mast cells require TLR4 for cellular responses to LPS, mast cells were derived from the bone marrow cells of C3H/HeJ and C57Bl/10ScNCr mice containing a point mutation and a null mutation, respectively, in TLR4. Using these models, we demonstrated that the BMMC IL-6 and TNF-alpha responses to LPS were completely dependent on functional TLR4 with no significant LPS response observed in its absence. These findings have important implications for the mechanism of mast cell responses to pathogens and their products and suggest that different TLR4-expressing cells might have different thresholds for activation with LPS.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".