Recent progress in understanding protein and lipid factors affecting hepatic VLDL assembly and secretion
Bibliographic record
Abstract
Excess lipid induced metabolic disorders are one of the major existing challenges for the society. Among many different causes of lipid disorders, overproduction and compromised catabolism of triacylglycerol-rich very low density lipoproteins (VLDL) have become increasingly prevalent leading to hyperlipidemia worldwide. This review provides the latest understanding in different aspects of VLDL assembly process, including structure-function relationships within apoB, mutations in APOB causing hypobetalipoproteinemia, significance of modulating microsomal triglyceride-transfer protein activity in VLDL assembly, alterations of VLDL assembly by different fatty acid species, and hepatic proteins involved in vesicular trafficking, and cytosolic lipid droplet metabolism that contribute to VLDL assembly. The role of lipoprotein receptors and exchangeable apolipoproteins that promote or diminish VLDL assembly and secretion is discussed. New understanding on dysregulated insulin signaling as a consequence of excessive triacylglycerol-rich VLDL in the plasma is also presented. It is hoped that a comprehensive view of protein and lipid factors that contribute to molecular and cellular events associated with VLDL assembly and secretion will assist in the identification of pharmaceutical targets to reduce disease complications related to hyperlipidemia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".