Local coordination verses systemic disregulation: complexities in leukocyte recruitment revealed by local and systemic activation of TLR4 in vivo
Bibliographic record
Abstract
The recruitment of leukocytes to a tissue is a critical step in the inflammatory response. Toll-like receptor 4 (TLR4) is an important receptor involved in the initiation of inflammatory responses. Administration of the ligand for TLR4, lipopolysaccharide, is often used to model inflammation--local responses to stimuli within a specific tissue and systemic responses such as those observed during endotoxic or septic shock. Here, we review work, which demonstrates that in response to local activation of TLR4, highly coordinated and multistep processes are initiated, ultimately resulting in the leukocyte's arrival at the inflamed tissue. In contrast, systemic activation of TLR4 results in nonspecific accumulation of leukocytes within the lung capillaries and liver sinusoids through mechanisms profoundly different than those involved in local tissue recruitment. Contrary to current dogma, leukocyte accumulation in the lung is dependent on endothelial rather than leukocyte activation. Finally, we discuss recent evidence suggesting that activation of leukocytes through TLR4, although still in the circulation, effectively paralyzes inflammatory cells, rendering them incapable of appropriate trafficking to inflamed tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".