Regulating iron storage and metabolism with RNA: an overview of posttranscriptional controls of intracellular iron homeostasis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Iron (Fe) is a double‐edged sword for most living organisms. Although it is essential for the catalytic activity of a large number of enzymes, ferrous iron (Fe 2+ ) becomes cytotoxic in the presence of normal respiratory by‐products such as H 2 O 2 . Because of this toxicity, intracellular iron concentrations ought to be regulated by elaborated homeostasis systems that, despite decades of extensive studies, have not yet revealed all of their surprising arrays of mechanistic details. Within the last few years, our understanding of iron metabolism has revealed that posttranscriptional regulation represents a major contribution to iron homeostasis in a host of organisms. While the small RNA RyhB regulates iron homeostasis in bacteria, its functional homolog protein Cth2 performs a similar task in yeasts. Recent advances in the elucidation of the mechanism of action and functions of RyhB have been made in Escherichia coli . In addition, other RyhB‐like small RNAs have been identified in several bacterial species, such as Pseudomonas aeruginosa , Salmonella enterica , Vibrio cholerae, Neisseria meningitidis , and Shigella spp. These recent findings have shed light on the complexity of iron homeostasis. WIREs RNA 2012, 3:26–36. doi: 10.1002/wrna.102 This article is categorized under: Translation > Translation Regulation RNA Turnover and Surveillance > Regulation of RNA Stability Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA in Disease and Development > RNA in Disease
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it