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Record W4282976117 · doi:10.3389/fnmol.2022.927150

Neuroprotection by Ozanimod Following Intracerebral Hemorrhage in Mice

2022· article· en· W4282976117 on OpenAlexaff
Fei Wang, Xiangyu Zhang, Yang Liu, Zhe Li, Ruixue Wei, Yan Zhang, Ruiyi Zhang, Suliman Khan, V. Wee Yong, Mengzhou Xue

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Calgary
FundersNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMedicineNeuroprotectionIntracerebral hemorrhageInternal medicinePharmacology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.245
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

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