<i>In vitro</i>molecular magnetic resonance imaging detection and measurement of apoptosis using superparamagnetic iron oxide + antibody as ligands for nucleosomes
Bibliographic record
Abstract
Recent research in cell biology as well as oncology research has focused on apoptosis or programmed cell death as a means of quantifying the induced effects of treatment. A hallmark of late-stage apoptosis is nuclear fragmentation in which DNA is degraded to release nucleosomes with their associated histones. In this work, a method was developed for detecting and measuring nucleosome concentration in vitro with magnetic resonance imaging (MRI). The indirect procedure used a commercially available secondary antibody-superparamagnetic iron oxide (SPIO) particle complex as a contrast agent that bound to primary antibodies against nucleosomal histones H4, H2A and H2B. Using a multiple-echo spin-echo sequence on a 1.5 T clinical MRI scanner, significant T₂ relaxation enhancement as a function of in vitro nucleosomal concentration was measured. In addition, clustering or aggregation of the contrast agent was demonstrated with its associated enhancement in T₂ effects. The T₂ clustering enhancement showed a complex dependence on relative concentrations of nucleosomes, primary antibody and secondary antibody + SPIO. The technique supports the feasibility of using MRI measurements of nucleosome concentration in blood as a diagnostic, prognostic and predictive tool in the management of cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".