39 <sup>th</sup> International Winter-Workshop Clinical, Chemical and Biochemical Aspects of Pteridines and Related Topics Innsbruck, February 25 <sup>th</sup> - 28 <sup>th</sup> , 2020
Bibliographic record
Abstract
Dyshomeostasis of iron metabolism has been linked to the development of cardiovascular disease.However, the underlying mechanisms remain poorly understood.By combining data from a GWAS screening in >188,000 individuals of European ancestry, CRISPR/Cas9 genome editing, and functional validation studies in mice, we identified iron metabolism as an important regulator of cholesterol homeostasis.Iron overload of dyslipidemic mice strongly reduced plasma low density lipoprotein cholesterol (LDL-C) levels, thereby inhibiting atherosclerosis development.Mechanistically, we identified a novel iron -related regulator of LDL receptor expression in hepatocytes, whereas iron promoted the transfer of cholesterol from Kupffer cells (KCs) to hepatocytes in an Abca1-dependent fashion.Importantly, we characterized KCs as a gatekeeper of lipid homeostasis by controlling hepatic removal of LDL-C.Our results define iron metabolism as conserved regulator of cholesterol homeostasis and identify KCs as promising therapeutic targets to treat dyslipidemia in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.045 |
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".