<i>TLR2</i> affects <i>CD86</i> expression and inflammatory response in burn injury mice through regulation of <i>p38</i>
Bibliographic record
Abstract
) mice were obtained, and a mouse burn injury model was constructed. Tissue samples were examined with hematoxylin and eosin staining and the transferase mediated nick end labeling (TUNEL) method. Macrophages were treated with TLR2 agonist and p38 inhibitor. The expression levels of TLR2, p38, CD86, IL-1β, and TNF-α were quantified by RT-qPCR, Western blot, and ELISA. When compared with the sham group, the burn group had a significantly higher rate of apoptosis as well as higher expressions of TLR2, p38, CD86, IL-1β, and TNF-α. Inhibiting TLR2 was shown to significantly reduce the expressions of p-p38, CD86, IL-1β, and TNF-α. In the results of in-vitro experiments, TLR2 agonist increased the expression of p-p38, CD86, IL-1β, and TNF-α, whereas a p38 inhibitor was shown to reduce the expression of CD86, IL-1β, and TNF-α. Our results suggest that the TLR2-p38-CD86 signaling pathway plays a vital role in inflammation associated with burn injury.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".