Adhesion Molecules: Master Controllers of the Circulatory System
Bibliographic record
Abstract
This manuscript will review our current understanding of cellular adhesion molecules (CAMs) relevant to the circulatory system, their physiological role in control of vascular homeostasis, innate and adaptive immune responses, and their importance in pathophysiological (disease) processes such as acute lung injury, atherosclerosis, and pulmonary hypertension. This is a complex and rapidly changing area of research that is incompletely understood. By design, we will begin with a brief overview of the structure and classification of the major groups of adhesion molecules and their physiological functions including cellular adhesion and signaling. The role of specific CAMs in the process of platelet aggregation and hemostasis and leukocyte adhesion and transendothelial migration will be reviewed as examples of the complex and cooperative interplay between CAMs during physiological and pathophysiological processes. The role of the endothelial glycocalyx and the glycobiology of this complex system related to inflammatory states such as sepsis will be reviewed. We will then focus on the role of adhesion molecules in the pathogenesis of specific disease processes involving the lungs and cardiovascular system. The potential of targeting adhesion molecules in the treatment of immune and inflammatory diseases will be highlighted in the relevant sections throughout the manuscript.
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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 | 0.001 |
| 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 | 0.002 |
| 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".