Cellular Imaging of Inflammation after Experimental Spinal Cord Injury
Bibliographic record
Abstract
The ability to visualize the cellular inflammatory responses after experimental spinal cord injury (SCI) was investigated using a clinical 1.5-T magnetic resonance imaging scanner, a custom-built, high-strength gradient coil insert, a 3-D fast imaging employing steady-state acquisition (FIESTA) imaging sequence and a superparamagnetic iron oxide (SPIO) contrast agent. An "active labeling" approach was used, with SPIO administered intravenously at different time points following SCI. Our results show that this strategy can be used to visualize clusters of iron-labeled cells associated with the inflammatory response in SCI. Of particular importance for this application was the finding that in FIESTA images hemorrhage does not cause signal loss. In T2-weighted spin echo or T2*-weighted gradient-echo images, which are more commonly used to detect signal loss associated with SPIO, the signal loss associated with hemorrhage interferes with the detection of iron-induced signal loss. FIESTA, therefore, allowed us to discriminate between iron associated with blood products in hemorrhage that occurs in acute SCI and the iron associated with SPIO-labeled cells accumulating in the injured cord.
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 | 0.001 | 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 | 0.001 |
| 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".