Neuroprotection by Ozanimod Following Intracerebral Hemorrhage in Mice
Bibliographic record
Abstract
The destruction of the blood-brain barrier (BBB) after intracerebral hemorrhage (ICH) is associated with poor prognosis. Modulation of sphingosine 1-phosphate receptor (S1PR) may improve outcomes from ICH. Ozanimod (RPC-1063) is a newly developed S1PR regulator which can selectively modulate type 1/5 sphingosine receptors. Here, we studied the impact of Ozanimod on neuroprotection in an experimental mouse model of ICH, induced by injecting collagenase type VII into the basal ganglia. Ozanimod was administered by gavage 2 h after surgery and once a day thereafter until sacrifice. The results demonstrate that Ozanimod treatment improved neurobehavioral deficits in mice and decreased weight loss after ICH. Ozanimod significantly reduced the density of activated microglia and infiltrated neutrophils in the perihematoma region. Furthermore, Ozanimod reduced hematoma volume and water content of the ICH brain. The results of TUNEL staining indicate that Ozanimod mitigated brain cell death. The quantitative data of Evans blue (EB) staining showed that Ozanimod reduced EB dye leakage. Overall, Ozanimod reduces the destruction of the BBB and exert neuroprotective roles following ICH in mice.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".