Bibliographic record
Abstract
Size distribution in a group of differentiated cells often falls into a constant range. However, in vitro and in vivo studies have shown that cells can temporarily change their size in response to their surrounding environment and the stimuli they receive. Thus, there must be a mechanism that normally keeps cell size constant while allowing a shift to an alternative size when necessary. To investigate the molecular basis of mammalian cell size control, we conducted a genetic screen in a human T cell line to identify genes involved in cell size regulation. A prime candidate emerging from this screen increases cell size when it is overexpressed but reduces cell size when subjected to siRNA knockdown. Several lines of evidence indicate that the product of this gene, which we called "Largen", regulates mRNA translation in a manner associated with the upregulation of a specific subset of mRNAs, many of which affect mitochondrial function. In fact, cells overexpressing Largen increase both mitochondrial mass and activity, enhancing ATP production. These in vitro observations have been replicated in vivo using transgenic mouse models. With a focus on these findings, we discuss the possible contribution of mitochondria to the control of mammalian cell size.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".