Leukemia inhibitory factor stimulates muscle glucose uptake by a PI3‐kinase dependent pathway that is maintained in white muscle in obesity (1162.4)
Bibliographic record
Abstract
OBJECTIVE: To investigates the metabolic effects of leukemia inhibitory factor (LIF) in mouse skeletal muscle. METHODS: Effects of LIF on muscle glucose transport, palmitate oxidation and cellular signaling were investigated in soleus and extensor digitorum longus (EDL) muscles from chow and highfat diet fed wildtype (WT) mice or chow fed muscle‐specific AMPKα2 kinase‐dead (KD) and suppressors of cytokine signaling (SOCS) 3 muscle‐specific knockout mice. RESULTS: LIF increased muscle glucose transport in a dose‐ and time‐dependent manner. LIF increased Akt Ser473‐P, whereas AMPK Thr172‐P was unaffected. Incubation of muscles from KD or WT mice with Wortmannin, LY294002 and Parthenolide demonstrated that PI3‐kinase, but not AMPK, was essential for LIF‐stimulated glucose transport. Incubation with Rapamycin and AZD8055 demonstrated that mammalian target of rapamycin complex (mTORC)2 and not mTORC1 is necessary for LIF‐stimulated glucose transport. LIF‐stimulated glucose transport was maintained in EDL muscle from obese insulin resistant mice. Lack of SOCS3 did not affect LIF‐stimulated glucose uptake. CONCLUSIONS: LIF acutely increases muscle glucose transport by a mechanism involving the PI3‐kinase/mTORC2/Akt pathway which is maintained in EDL muscle from obese insulin resistant mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".