Mitochondrial iron–sulfur clusters: Structure, function, and an emerging role in vascular biology
Bibliographic record
Abstract
Iron-sulfur (Fe-S) clusters are essential cofactors most commonly known for their role mediating electron transfer within the mitochondrial respiratory chain. The Fe-S cluster pathways that function within the respiratory complexes are highly conserved between bacteria and the mitochondria of eukaryotic cells. Within the electron transport chain, Fe-S clusters play a critical role in transporting electrons through Complexes I, II and III to cytochrome c, before subsequent transfer to molecular oxygen. Fe-S clusters are also among the binding sites of classical mitochondrial inhibitors, such as rotenone, and play an important role in the production of mitochondrial reactive oxygen species (ROS). Mitochondrial Fe-S clusters also play a critical role in the pathogenesis of disease. High levels of ROS produced at these sites can cause cell injury or death, however, when produced at low levels can serve as signaling molecules. For example, Ndufs2, a Complex I subunit containing an Fe-S center, N2, has recently been identified as a redox-sensitive oxygen sensor, mediating homeostatic oxygen-sensing in the pulmonary vasculature and carotid body. Fe-S clusters are emerging as transcriptionally-regulated mediators in disease and play a crucial role in normal physiology, offering potential new therapeutic targets for diseases including malaria, diabetes, and cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".