Longitudinal in vivo imaging of perineuronal nets
Bibliographic record
Abstract
SignificancePerineuronal nets (PNNs) are extracellular matrix structures implicated in learning, memory, information processing, synaptic plasticity, and neuroprotection. However, our understanding of mechanisms governing the evidently important contribution of PNNs to central nervous system function is lacking. A primary cause for this gap of knowledge is the absence of direct experimental tools to study their role in vivo.AimWe introduce a robust approach for quantitative longitudinal imaging of PNNs in brains of awake mice at subcellular resolution.ApproachWe label PNNs in vivo with commercially available compounds and monitor their dynamics with two-photon imaging.ResultsUsing our approach, we show that it is possible to longitudinally follow the same PNNs in vivo while monitoring degradation and reconstitution of PNNs. We demonstrate the compatibility of our method to simultaneously monitor neuronal calcium dynamics in vivo and compare the activity of neurons with and without PNNs.ConclusionOur approach is tailored for studying the intricate role of PNNs in vivo, while paving the road for elucidating their role in different neuropathological conditions.
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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) | 0.000 | 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) | 0.001 | 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".