Perineuronal Net Changes Reveal a Distinct Right and Left Spinal Phrenic Circuit
Bibliographic record
Abstract
Respiratory failure is one of the greatest causes of morbidity and mortality after cervical lesions, the most common type of spinal cord injury (SCI). Fortunately, several pre-clinical and clinical studies have shown spontaneous, but limited, respiratory recovery after injury. However, there are still many unanswered questions about what is driving this recovery, so there is a growing need to further elucidate the neuroplastic potential of the phrenic network. Here, we investigated the structural plasticity of the right and left phrenic networks by analyzing perineuronal net (PNN) changes after a C2 hemisection (C2Hx) in mice. For this purpose, the right and left phrenic systems were traced with a pseudorabies virus, a trans-synaptic retrograde tracer applied to the diaphragm muscle, labeling the entire phrenic motor network. We found most PNN-bearing neurons within the ventral horn in naïve animals, specifically around phrenic motoneurons (PhMNs), but not phrenic spinal interneurons. Right, but not left, C2Hx resulted in a significant increase in PNNs and glutamatergic synapses around ipsilateral PhMNs, suggesting that the right C2Hx requires greater neuroplasticity to overcome respiratory dysfunction. The results from this study uncover profound anatomical and functional asymmetries in left- and right-sided phrenic networks, underlying the complex nature of the spinal respiratory system, and contribute to a more advanced understanding of how the phrenic network adapts to trauma. Overall, this work underscores the importance of studying neuroplasticity and how it holds the potential to help improve outcomes for individuals living with SCI.
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) | 0.002 | 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".