Mast Cell Cytokine and Chemokine Responses to Bacterial and Viral Infection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mast cells have been most widely studied in the context of allergic disease but also play a critical role in host defence against bacterial infection, most elegantly demonstrated in studies using mast cell deficient w/wv mice. There is less data available concerning the role of mast cells in defence against viral pathogens, however, mast cells have been demonstrated to be a potential reservoir of infection for several pathogens, such as HIV-1 and dengue, and capable of producing mediators following challenge with a number of viral products. Traditional mast cell mediators such as histamine, protease enzymes and leukotrienes are important for effective host responses. The cytokines and chemokines produced by mast cells in response to pathogens are known to profoundly alter the nature of the innate immune response and its effectiveness in eliminating infection. Cytokine and chemokine production by mast cells is closely regulated and may occur independently of classical mast cell degranulation. Depending upon the nature of the stimulus or type of infection, a unique profile of cytokines is induced. In this review, we will examine the role and regulation of mast cell cytokines and chemokines in the context of a number of bacterial and viral infections, emphasizing the multiple receptor mechanisms used to activate mast cells. This area of research is still in its early stages and much work remains to be done. However, understanding the unique properties of resident tissue mast cells and how their cytokine responses are regulated by pathogens or pathogen products, will provide important opportunities for the therapeutic manipulation of local immune responses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 it