Autocrítica i Marianne Sagebrecht: ¡Otra maldita novela sobre la guerra civil! [Isaac Rosa]
Bibliographic record
Abstract
Cytosolic ferritins sequester and store iron, consequently protecting cells against iron-mediated free radical damage. However, the mechanisms of iron exit from the ferritin cage and reutilization are largely unknown. In a previous study, we found that mitochondrial ferritin (MtFt) expression led to a decrease in cytosolic ferritin. Here we showed that treatment with inhibitors of lysosomal proteases largely blocked cytosolic ferritin loss in both MtFt-expressing and wild-type cells. Moreover, cytosolic ferritin in cells treated with inhibitors of lysosomal proteases was found to store more iron than did cytosolic ferritins in untreated cells. The prevention of cytosolic ferritin degradation in MtFt-expressing cells significantly blocked iron mobilization from the protein cage induced by MtFt expression. These studies also showed that blockage of cytosolic ferritin loss by leupeptin resulted in decreased cytosolic ferritin synthesis and prolonged cytosolic ferritin stability, potentially resulting in diminished iron availability. Lastly, we found that proteasomes were responsible for cytosolic ferritin degradation in cells pretreated with ferric ammonium citrate. Thus, the current studies suggest that cytosolic ferritin degradation precedes the release of iron in MtFt-expressing cells; that MtFt-induced cytosolic ferritin decrease is partially preventable by lysosomal protease inhibitors; and that both lysosomal and proteasomal pathways may be involved in cytosolic ferritin degradation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".