Impact of keratin intermediate filaments on insulin‐mediated glucose metabolism regulation in the liver and disease association
Bibliographic record
Abstract
In all cells, a tight regulation exists between glucose uptake and utilization to prevent diseases related to its perturbed metabolism. In insulin-targeted cells, such as hepatocytes, proper glucose utilization requires an elaborate interplay between the insulin receptor, the glucose transporter, and mitochondria that involves the participation of actin microfilaments and microtubules. In addition, there is increasing evidence of an involvement of the third cytoskeletal network provided by intermediate filaments (IFs). Keratins belong to the multigene family of IF proteins, coordinately expressed as distinct pairs within the context of epithelial cell differentiation. Hepatocyte IFs are made up of the [keratin (K)8/K18] pair only, whereas pancreatic β-cell IFs additionally include small amounts of K7. There are accumulating examples of K8/K18 involvement in the glucose-insulin cross-talk, including the modulation of plasma glucose levels, insulin release from pancreatic β-cells, and insulin-mediated glucose uptake and glycogen production in hepatocytes after a K8/K18 loss. This review integrates the mechanistic features that support such an impact of K8/K18 IFs on insulin-dependent glucose metabolism regulation in liver and its implication in glucose- or insulin-associated diseases.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".