Bibliographic record
Abstract
INTRODUCTION Neutrophils (polymorphonuclear leukocytes, PMN) have a clearly defined role in inflammation. In response to injury or infection, PMN migration across vascular endothelial cells is a first line of defense against infectious agents, and defects in such PMN–endothelial interactions contributes to fulminate microbial infections, mucosal ulcerations, and delayed tissue healing. The protective aspects of PMN in disease are objectively exemplified by the clinical observation that patients with primary defects in PMN function, including neutropenia and genetic PMN immunopathologies (e.g., leukocyte adhesion deficiencies, chronic granulomatous disease, Chediak–Higashi syndrome, myeloperoxidase deficiency, etc.), exhibit ongoing mucosal infections. This chapter focuses on our current understanding of how PMN interact with vascular endothelial cells under physiologic and pathophysiologic conditions (Figure 11.1). MOLECULAR MECHANISMS OF PMN ADHESION AND TRANSMIGRATION PMN migration across the endothelial surface is a result of an orchestrated series of events, ultimately resulting in PMN accumulation at sites of tissue injury. The recruitment signals, the cell–cell interaction steps, and the regulatory pathways for these events have been an area of extensive exploration in the past two decades. A number of recent reviews have addressed these steps in detail [1 –4]. Here, we will summarize some of the major steps and guide the reader to the primary literature for more insight into this dynamic process.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".