CD93 regulates central nervous system inflammation in two mouse models of autoimmune encephalomyelitis
Bibliographic record
Abstract
Summary Microglia and non‐professional immune cells (endothelial cells, neurons) participate in the recognition and removal of pathogens and tissue debris in the injured central nervous system through major pro‐inflammatory processes. However, the mechanisms involved in regulating these responses remain ill‐characterized. We herein show that CD93, also known as complement C1qRp/AA4 stem cell marker, has an important role in the regulation of inflammatory processes. The role of CD93 was evaluated in two models of neuroinflammation. We used the MOG‐experimental autoimmune encephalomyelitis (EAE) model and the antibody‐dependent EAE (ADEAE), which were induced in wild‐type and CD93 knockout mice. We found that CD93 was highly expressed by neurons, endothelial cells and microglia (ramified >> amoeboid). Astrocytes and oligodendrocytes did not to express CD93. We further observed that CD93‐deficient (CD93 −/− ) mice presented a more robust brain and spinal cord inflammation in EAE and ADEAE. Encephalitis in CD93 −/− was characterized by increased numbers of infiltrating M1 macrophages (CD11c + CD206 − ) and amoeboid microglia exhibiting a more activated phenotype (Tomato Lectin high Cox2 high ). Damage to and leakage through the blood–brain barrier was increased in CD93 −/− animals and was associated with a more robust neuronal injury when compared with wild‐type EAE mice. We propose that CD93 is an important neuro‐immune regulator to control central nervous system inflammation.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".