9. Investigating the relationship between Fibrinogen deposition and myelomonocytic cell infiltration in EAE Spinal Cord
Bibliographic record
Abstract
Multiple sclerosis and its animal model, experimental autoimmune encephalitis (EAE), are primarily characterized by inflammatory degeneration. It has also been shown that deposition of fibrinogen from the blood in spinal cord tissue increased in MS and EAE. Fibrinogen is a substrate molecule found in the blood for a receptor called CD11 expressed on the surface of both brain and blood immune cells. Using a technique to light up specific tissue (called immunofluorescence), we demonstrate that there is a positive correlation between levels of fibrinogen deposition in spinal cord and the progression of clinical symptoms. Contrary to previous research, our past work indicated that brain immune cells play a limited role in diseaseprogression, while immune cells in the blood are critical. Thus an alternative interpretation of these results may be that blocking fibrinogen deposition within the spinal cord during EAE reduces the infiltration of blood immune cells into the spinal cord. Investigating this experimentally, however, is complicated by the inability to distinguish blood and brain resident immune cells. To circumvent this, we use parabiosisirradiation-separation models to investigate the replacement of circulating immune cells, which leads to the dynamic assessment of blood immune cell infiltration in spinal cord. We have used fibrinogen deposits during EAE, thereby successfully identifying a pathological component, and a immunofluorescence tovisualize fluorescent blood cells within spinal cord and their relationship to therapeutic target for multiple sclerosis.
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) | 0.002 | 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".