Exploring the complex role of chemokines and chemoattractants in vivo on leukocyte dynamics
Bibliographic record
Abstract
Chemotaxis is fundamental for leukocyte migration in immunity and inflammation and contributes to the pathogenesis of many human diseases. Although chemokines and various other chemoattractants were initially appreciated as important mediators of acute inflammation, in the past years they have emerged as critical mediators of cell migration during immune surveillance, organ development, and cancer progression. Such advances in our knowledge in chemokine biology have paved the way for the development of specific pharmacological targets with great therapeutic potential. Chemoattractants may belong to different classes, including a complex chemokine system of approximately 50 endogenous molecules that bind to G protein-coupled receptors, which are expressed by a wide variety of cell types. Also, an unknown number of other chemoattractants may be generated by pathogens and damaged/dead cells. Therefore, blocking chemotaxis without causing side effects is an extremely challenging task. In this review, we focus on recent advances in understanding how the chemokine system orchestrates immune cell migration and positioning at the whole organ level in homeostasis, inflammation, and infection.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".