Monocyte behaviour and tissue transglutaminase expression during experimental autoimmune encephalomyelitis in transgenic CX3CR1gfp/gfp mice
Bibliographic record
Abstract
Leukocyte infiltration into the central nervous system (CNS) is a key pathological feature in multiple sclerosis (MS) and the MS animal model experimental autoimmune encephalomyelitis (EAE). Recently, preventing leukocyte influx into the CNS of MS patients is the main target of MS therapies and insight into cell behaviour in the circulation is needed for further elucidation of such therapies. In this study, we aimed at in vivo visualization of monocytes in a time-dependent manner during EAE. Using intravital two-photon microscopy (IVM), we imaged CX3CR1 gfp/gfp mice during EAE, visualizing CX3CR1-GFP + monocytes and their dynamics in the spinal cord vasculature. Our observations showed that intraluminal crawling of CX3CR1-GFP + monocytes increased even before the clinical onset of EAE due to immunization of the animals. Furthermore, intraluminal crawling remained elevated during ongoing clinical disease. Besides, the displacement of these cells was larger during the peak of EAE compared to the control animals. In addition, we showed that the enzyme tissue transglutaminase (TG2), which is present in CNS-infiltrated cells in MS patients, is likewise found in CX3CR1-GFP + monocytes in the spinal cord lesions and at the luminal side of the vasculature during EAE. It might thereby contribute to adhesion and crawling of monocytes, facilitating extravasation into the CNS. Thus, we put forward that interference with monocyte adhesion, by e.g. inhibition of TG2, should be applied at a very early stage of EAE and possibly MS, to effectively combat subsequent pathology.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".