Macrophage Imaging in Central Nervous System and in Carotid Atherosclerotic Plaque Using Ultrasmall Superparamagnetic Iron Oxide in Magnetic Resonance Imaging
Bibliographic record
Abstract
The long blood circulating time and the progressive macrophage uptake in inflammatory tissues of ultrasmall superparamagnetic iron oxide (USPIO) particles are 2 properties of major importance for magnetic resonance imaging (MRI) pathologic tissue characterization. This article reviews the proof of principle of applications such as imaging of carotid atherosclerotic plaque, stroke, brain tumor characterization, or multiple sclerosis. In the human carotid artery, USPIO accumulation in activated macrophages induced a focal drop in signal intensity compared with preinfusion MRI. The USPIO signal alterations observed in ischemic areas of stroke patients is probably related to the visualization of inflammatory macrophage recruitment into human brain infarction since animal experiments in such models demonstrated the internalization of USPIO into the macrophages localized in these areas. In brain tumors, USPIO particles which do not pass the ruptured blood-brain barrier at early times postinjection can be used to assess tumoral microvascular heterogeneity. Twenty-four hours after injection, when the cellular phase of USPIO takes place, the USPIO tumoral contrast enhancement was higher in high-grade than in low-grade tumors. Several experimental studies and a pilot multiple sclerosis clinical trial in 10 patients have shown that USPIO contrast agents can reveal the presence of inflammatory multiple sclerosis lesions. The enhancement with USPIO does not completely overlap with the gadolinium chelate enhancement. While the proof of concept that USPIO can visualize macrophage infiltrations has been confirmed in animals and patients in several applications (carotid atherosclerotic lesions, stroke, brain tumors and multiple sclerosis), larger prospective clinical studies are needed to demonstrate the clinical benefit of using USPIO as an MRI in vivo surrogate marker for brain inflammatory diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".