Examining the Effect of Doxorubicin on Apoptotic Signaling in Skeletal Muscle of ARC KO Mice
Bibliographic record
Abstract
Apoptosis repressor with caspase recruitment domain (ARC) is an anti‐apoptotic protein that inhibits both the intrinsic and extrinsic apoptotic pathways. Along with its potent anti‐apoptotic actions, ARC has a unique tissue expression with high levels found in skeletal muscle, cardiac muscle, neurons, as well as some cancers. Within skeletal muscle, ARC content is fiber‐type specific with highest expression in slow‐twitch fibers. Despite its high expression, ARC's anti‐apoptotic role in skeletal muscle is not fully understood. To determine whether ARC contributes to apoptotic resistance, wild‐type (WT) and ARC knockout (KO) mice were given intraperitoneal injections of doxorubicin (DOX) (20 mg/kg) or saline. 24 hrs post injection, mice were sacrificed, the gastrocnemius was removed, and separated into red (RG) and white (WG) portions. DOX treated mice lost significantly more weight than saline controls (p<0.0001); however, whole gastrocnemius weights did not change. In RG, DOX increased ROS (p<0.01), with post hoc analysis revealing a significant increase between KO saline and KO DOX mice (p<0.05). DOX treatment significantly increased caspase‐9 (p<0.01) and calpain (p<0.05) activity; however, caspase‐8 and ‐3 activity remained unchanged. There was also a significant increase in calpain activity between KO saline and KO DOX treated mice (p<0.05). In WG, DOX had no influence on ROS levels. Interestingly, there was an increase in caspase‐3 activity in KO DOX and KO saline treated mice (p<0.05) despite no change in upstream initiator caspases. Taken together, 24 hr DOX treatment affected apoptotic signaling in RG to a greater extent than in WG. Furthermore, a lack of ARC led to higher ROS, as well as increased calpain activity in RG following DOX treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".