Iron‐oxide labeling of hematogenous macrophages in a model of experimental autoimmune encephalomyelitis and the contribution to signal loss in fast imaging employing steady state acquisition (FIESTA) images
Bibliographic record
Abstract
PURPOSE: To determine the contribution of blood-derived macrophages to the signal loss observed in MR images of inflammatory lesions in experimental autoimmune encephalomyelitis (EAE). MATERIALS AND METHODS: A relapsing-remitting form of EAE was induced in transgenic mice that express enhanced green fluorescent protein (EGFP) specifically in hematopoietic cells of the myelomonocytic lineage. Animals were injected with Feridex, a superparamagnetic iron oxide (SPIO) nanoparticle, 24 hours prior to in vivo MRI. MRI was performed using a 1.5T whole-body scanner; a high-performance, custom-built gradient coil insert; and a 3D steady-state free precession (SSFP) imaging pulse sequence. Comparisons were made between MR images and corresponding anti-GFP and Perl's Prussian blue (PPB)-stained brain sections. RESULTS: MR images revealed the presence of discrete regions of signal loss throughout the brains of EAE animals that were administered Feridex. Histological staining showed that regions of signal loss on MR images corresponded anatomically with regions of PPB- and GFP-positive cells. CONCLUSION: This experiment provides the first direct evidence that macrophages of hematogenous origin are labeled with SPIO after intravenous administration of Feridex, and contribute to the regions of signal loss detected in MR images of EAE brain.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".