Changes in serial sarcomere number of five hindlimb muscles across adult aging in rats
Bibliographic record
Abstract
Abstract Introduction Aging is associated with a reduction in muscle fascicle length (FL), which contributes to sarcopenia: the age-related loss of muscle mass and function. Studies on rodents have confirmed this reduced FL is driven by a loss of sarcomeres aligned in series (serial sarcomere number; SSN) along a muscle. However, studies on rodents have focused primarily on rat plantar flexor SSN at two aging timepoints, leaving an incomplete view of age-related changes in SSN. Hence, this study investigated SSN as a contributor to the age-related loss of muscle mass in five hindlimb muscles across four aging timepoints in rats. Methods The soleus, medial gastrocnemius (MG), plantaris, tibialis anterior (TA), and vastus lateralis (VL) were obtained from 5 young (8 months), 5 middle-aged (20 months), 5 old (32 months), and 5 very old (36 months) male F344BN rats. After fixation of muscles in formalin and digestion in nitric acid, fascicles were teased out end-to-end to measure FL. SSN was determined by dividing FL by sarcomere length measured via laser diffraction. Muscle wet weight, anatomical cross-sectional area (ACSA), and physiological cross-sectional area (PCSA) were also determined for insight on age-related losses of whole-muscle mass and in-parallel muscle morphology. Results Age-related SSN loss was apparent after middle age for all muscles, with the plantaris showing the smallest (8%) and the VL the greatest (21%) loss. The MG and VL appeared to plateau in their SSN loss by 32 months, while the soleus and TA underwent continued loss from 32 to 36 months. In all muscles, SSN loss evidently contributed in part to the loss of muscle mass, alongside losses of contractile tissue in parallel (indicated by ACSA and PCSA). Conclusion As SSN is closely tied to biomechanical function, these findings present SSN as a distinct target for improving muscle performance in older adults.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".