A facile colorimetric method for the quantification of labile iron pool and total iron in cells and tissue specimens
Bibliographic record
Abstract
Quantification of iron is an important step to assess the iron burden in patients suffering from iron overload diseases, as well as tremendous value in understanding the underlying role of iron in the pathophysiology of these diseases. Current iron determination of total or labile iron, requires extensive sample handling and specialized instruments, whilst being time consuming and laborious. Moreover, there is minimal to no overlap between total iron and labile iron quantification methodologies-i.e. requiring entirely separate protocols, techniques and instruments. Herein, we report a unified-ferene (u-ferene) assay that enables a 2-in-1 quantification of both labile and total iron from the same preparation of a biological specimen. We demonstrate that labile iron concentrations determined from the u-ferene assay is in agreement with confocal laser scanning microscopy techniques employed within the literature. Further, this assay offers the same sensitivity as the current gold standard, inductively coupled plasma mass spectrometry (ICP-MS), for total iron measurements. The new u-ferene assay will have tremendous value for the wider scientific community as it offers an economic and readily accessible method for convenient 2-in-1 measurement of total and labile iron from biological samples, whilst maintaining the precision and sensitivity, as compared to ICP-MS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".