Bibliographic record
Abstract
During times of stress, systemic pro-inflammatory cytokine levels are increased, which may prevent optimal growth and development of muscle and/or induce muscle atrophy. Pro-inflammatory cytokines can induce negative responses in muscle by altering the balance of protein synthesis and degradation in established myofibers, or by influencing the proliferation and differentiation of myoblasts. Interleukin-1 beta (IL-1β) is a pro-inflammatory cytokine involved in stress and disease responses, but little is known about how IL-1β affects myoblast function. We hypothesized that IL-1β would decrease myoblast proliferation and/or differentiation. To test this hypothesis, C2C12 mouse myoblasts were treated with 0.1 ng/mL or 1.0 ng/mL of IL-1β, or carrier only (control). To determine proliferation rate, myoblasts were plated at 2.6x103 cells/cm2 and cultured for 48 h in the presence or absence of IL-1β. Cells were pulsed with bromodeoxyuridine (BrdU), fixed, and immunostained. The number of BrdU positive cells was quantified as a percent of total nuclei (identified by Hoescht 33342). To determine if IL-1β affected fusion, myoblasts were plated at 2.0x104 cells/cm2 in growth media for 48 h at which time media was changed into differentiation media supplemented with 0.1
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) | 0.000 | 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) | 0.004 | 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".