The development of an optimized and reliable iron overload HepG2 cell model
Bibliographic record
Abstract
Abstract Cell-based iron overload models provide tremendous utility for the investigations into the pathogenesis of different diseases as well as assessing efficacy of various therapeutic strategies. In the literature, establishing such models vary tremendously with regards to cell lines, iron source, iron treatment conditions and duration. Due to this diversity, researchers reported significant differences in the measured outcomes, either in cellular function or response to a stimulus. Herein, we report the process required to establish an iron overload HepG2 cell model to achieve a consistent and reproducible results such that the literature can strive towards a consensus. Iron loading in cells was achieved with 50 µM of iron every 24 hours for 2 days, followed by an additional 24 hours of maintenance in fresh media. We demonstrated that iron overloaded cells had significantly increased ROS generation, labile and total iron whilst having various cellular functions resemble cells without iron overload. The present report addresses key pitfalls with regards to the lack of consensus currently present in the literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".