Characterization of Chicken Thrombocyte Responses to Toll-Like Receptor Ligands
Bibliographic record
Abstract
Thrombocytes are the avian equivalent to mammalian platelets. In addition to their hemostatic effects, mammalian platelets rely in part on pattern recognition receptors, such as the Toll-like receptors (TLR), to detect the presence of pathogens and signal the release of certain cytokines. Ligands for TLRs include lipopolysaccharide (LPS), which is bound by TLR4, as well as unmethylated CpG DNA motifs, which are bound by TLR9 in mammals and TLR21 in chickens. Similar to mammalian platelets, avian thrombocytes have been shown to express TLR4 and secrete some pro-inflammatory cytokines in response to LPS treatment. However, the full extent of the contributions made by thrombocytes to host immunity has yet to be elucidated. Importantly, the mechanisms by which TLR stimulation may modulate thrombocyte effector functions have not been well characterized. As such, the objective of the present study was to gain further insight into the immunological role of thrombocytes by analyzing their responses to treatment with ligands for TLR4 and TLR21. To this end, we quantified the relative expression of several immune system genes at 1, 3, 8 and 18 hours post-treatment using real-time RT-PCR. Furthermore, production of nitric oxide and phagocytic activity of thrombocytes was measured after their activation with TLR ligands. We found that thrombocytes constitutively express transcripts for both pro- and anti-inflammatory cytokines, in addition to those associated with anti-viral responses and antigen presentation. Moreover, we found that both LPS and CpG oligodeoxynucleotides (ODN) induced robust pro-inflammatory responses in thrombocytes, as characterized by more than 100 fold increase in interleukin (IL)-1β, IL-6 and IL-8 transcripts, while only LPS enhanced nitric oxide production and phagocytic capabilities. Future studies may be aimed at examining the responses of thrombocytes to other TLR ligands.
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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) | 0.000 | 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".