Application of the static dephasing regime theory to superparamagnetic iron‐oxide loaded cells
Bibliographic record
Abstract
The relaxation rates of iron-oxide nanoparticles compartmentalized within cells were studied and found to satisfy predictions of the static dephasing (SD) regime theory. THP-1 cells in cell culture were loaded using two different iron-oxide nanoparticles (superparamagnetic iron-oxide (SPIO) and ultrasmall SPIO (USPIO)) with four different iron concentrations (0.05, 0.1, 0.2, and 0.3 mg/ml) and for five different incubation times (6, 12, 24, 36, and 48 hr). Cellular iron-oxide uptake was assessed using a newly developed imaging version of MR susceptometry, and was found to be linear with both dose and incubation time. R(2)* sensitivity to iron-oxide loaded cells was found to be 70 times greater than for R(2), and 3100 times greater than for R(1). This differs greatly from uniformly distributed nanoparticles and is consistent with a cellular bulk magnetic susceptibility (BMS) relaxation mechanism. The cellular magnetic moment was large enough that R(2)' relaxivity agreed closely with SD regime theory predictions for all cell samples tested [R(2)'=2 pi/(9 x the square root of 3) x gamma LMD] where the local magnetic dose (LMD) is the sample magnetization due to the presence of iron-oxide particles). Uniform suspensions of SPIO and USPIO produced R(2)' relaxivities that were a factor of 3 and 8 less, respectively, than SD regime theory predictions. These results are consistent with theoretical estimates of the required mass of iron per compartment needed to guarantee SD-regime-dominant relaxivity. For cellular samples, R(2) was shown to be dependent on both the concentration and distribution of iron-oxide particles, while R(2)' was sensitive to iron-oxide concentration alone. This work is an important first step in quantifying cellular iron content and ultimately mapping the density of a targeted cell population.
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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 | 0.001 |
| 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".