Magnetic resonance molecular imaging of post‐stroke neuroinflammation with a P‐selectin targeted iron oxide nanoparticle
Bibliographic record
Abstract
We have developed a magnetic resonance molecular imaging method using a novel iron-oxide contrast agent targeted towards P-selectin - MNP-PBP (magnetic nanoparticle-P-selectin binding peptide) - to image endothelial activation following cerebral ischemia/reperfusion. MNP-PBP consists of approximately 1000 PBP ligands (primary sequence: GSIQPRPQIHNDGDFEEIPEEYLQ GGSSLVSVLDLEPLDAAWL) conjugated to a 50 nm diameter aminated dextran iron oxide particle. In vitro P- and E-selectin binding was assessed by competition ELISA. Transient focal cerebral ischemia was induced in male C57/BL 6 mice followed by contrast injection (MNP-PBP; MNP-NH2; Feridex; MNP-PBP-FITC) at 24 h after reperfusion and T(2) magnetic resonance imaging at 9.4 T was performed. Infarction and microvasculature accumulation of contrast agent was assessed in coronal brain sections. MNP-PBP attenuated antibody binding to P-selectin by 34.8 +/- 1.7%. P-selectin was preferentially increased in the infarct hemisphere and MNP-PBP-FITC accumulation in the infarct hemisphere microvasculature was observed. Compared with the nontargeted iron oxide agents MNP-NH2 and Feridex, MNP-PBP showed a significantly greater T(2) effect within the infarction. MR imaging of P-selectin expression with a targeted iron oxide nanoparticle contrast agent may reveal early endothelial activation in stroke and other neuroinflammatory states.
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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 | 0.000 | 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) | 0.000 | 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".