Human Brain Microvessel Endothelial Cell and Leukocyte Interactions
Bibliographic record
Abstract
The recruitment of leukocytes from the blood into secondary lymphoid and peripheral organs is a key process in both leukocyte homeostasis and the initiation and maintenance of immune responses ( 1 ). Within the past decade, several important advances have been made in identifying factors involved both in normal leukocyte homing and recruitment to sites of inflammation. These studies have identified adhesion molecules as instrumental in tethering leukocytes to endothelial surfaces and potentiating subsequent adhesion and migration. In addition, soluble chemoattractant cytokines (known as chemokines) have been identified that can direct the site and often the composition of the inflammatory infiltrate, as well as proteinases, which dismantle the barrier that leukocytes are crossing ( 2 ). In several of these studies, in vivo models have been used to block the activity of specific molecules in order to determine the global outcome and the involvement of these molecules in secondary lymphoid organ trafficking or disease pathogenesis. Such models, however, become of limited use when trying to focus on relative contributions to specific events such as direct cell-cell interactions occurring at the onset of inflammatory cell extravasation. Because of the complicated nature and multitude of cells and factors present in in vivo systems, in vitro models have become a powerful and more simplistic way of analyzing events by allowing for better control of the environment. For this purpose, in vitro models employing endothelial monolayers have been instrumental in trying to characterize factors which influence vascular permeability and the sequence of events during leukocyte-endothelial adhesion and transmigration. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| 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) | 0.007 | 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".