News from the old: Aging features in the intracardiac, musculoskeletal, and enteric nervous systems
Bibliographic record
Abstract
Aging strongly affects the peripheral nervous system (PNS), triggering alterations that vary depending on the innervated tissue. The most frequent alteration in peripheral nerve aging is reduced nerve fiber and glial density which can lead to abnormal nerve functionality. Interestingly, the activation of a destructive phenotype takes place in macrophages across the PNS while a reduced number of neuronal bodies is a unique feature of some enteric ganglia. Single cell/nucleus RNA-sequencing has unveiled a striking complexity of cell populations in the peripheral nerves, and these refined cell type annotations could facilitate a better understanding of PNS aging. While the effects of senescence on individual PNS cell types requires further characterization, the use of senolytics appears to improve general PNS function in models of aging. Here, we review the current understanding of age-related changes of the intracardiac, musculoskeletal, and enteric nervous system sub-sections of the PNS, highlighting their commonalities and differences. • The most common feature of aging in PNS is a reduction of nerve fiber and glial cell density. • PNS macrophages activate a destructive phenotype during aging. • Different sections of the PNS present different ECM protein deposits with age. • The molecular mechanisms behind PNS degeneration during aging remain unclear. • While promising, the effects of senolytics in the PNS need further investigation.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".