The mammalian target of rapamycin-signaling pathway in regulating metabolism and growth1,2
Bibliographic record
Abstract
The mammalian target of rapamycin (mTOR) plays key roles in cellular metabolism and hypertrophic-hyperplasic growth, and it acts as a central regulator of protein synthesis and ribosome biogenesis at the transcriptional and translational levels by sensing and integrating signals from mitogens and nutrients. Hormonal and stress factors can affect the mTOR-signaling pathway via their receptors and signal transduction pathways. Nutritional regulation of the mTOR-signaling pathway is mediated by their corresponding plasma membrane transporters, other unknown mechanisms, or both. Adenine monophosphate-activated protein kinase, an important cellular energy sensor, can interact with the mTOR-signaling pathway to maintain cellular energy homeostasis. Interactions of mTOR with regulatory-associated protein of TOR or rapamycin-insensitive companion of mTOR result in 2 mTOR complexes, with the former (mTOR complex-1) being the primary controller of cell growth and the latter (mTOR complex-2) mediating effects that are insensitive to rapamycin, such as cytoskeletal organization. Upstream elements of the mTOR-signaling pathway include Ras-homolog enriched in brain, and tuberous sclerosis complex 1 and 2, with tuberous sclerosis complex 2 as the linker between phosphatidylinositol 3-kinase/protein kinase B or Ras-Raf-mitogen-activated protein kinase-extracellular signal-regulated protein kinase pathways and the mTOR pathway. Ribosomal protein S6 protein kinase 1 and eukaryotic initiation factor 4E binding protein 1 are currently the 2 best-known downstream effectors of mTOR signaling. Hormonal factors, stressors, and nutrients can differentially mediate cellular metabolism and growth via the mTOR pathway with effectors specific to the organ or tissue types involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".