A Role for Platelets and Endothelial Selectins in Tumor Necrosis Factor-α–Induced Leukocyte Recruitment in the Brain Microvasculature
Bibliographic record
Abstract
The mechanisms mediating leukocyte recruitment into the cerebral nervous system during inflammation are still poorly understood. The objective of this study was to investigate the leukocyte recruitment in the brain microcirculation by intravital microscopy. Superfusion of the brain with artificial cerebrospinal fluid did not induce leukocyte rolling or adhesion. However, intraperitoneal tumor necrosis factor-alpha (TNF-alpha) caused marked leukocyte rolling and adhesion in the brain microcirculation. Histology revealed that the recruitment was primarily of neutrophils. Both E- and P-selectin were required for TNF-alpha-induced leukocyte recruitment, as rolling was reduced after treatment with either anti-E- or anti-P-selectin antibody and eliminated in E- or P-selectin-deficient mice. A significant increase in brain P- and E-selectin expression was seen after TNF-alpha treatment, but both were an order of magnitude less than in any other tissue. We observed significant platelet paving of TNF-alpha-stimulated endothelium and found that anti-platelet antibody reduced leukocyte rolling and adhesion, as did acetylsalicylic acid (aspirin). However, depletion of platelets did not reduce cerebral P-selectin expression. Moreover, chimeric mice lacking P-selectin on endothelium but not platelets had significantly decreased P-selectin expression and reduced leukocyte recruitment in the brain. This suggests a role for endothelial P-selectin in cerebral leukocyte recruitment. In conclusion, TNF-alpha-induced neutrophil recruitment into the brain requires both endothelial E-selectin and P-selectin as well as platelets, but platelet P-selectin was not a major contributor to this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".