Near-infrared nanoscopy with carbon-based nanoparticles for the exploration of the brain extracellular space
Bibliographic record
Abstract
Understanding the physiology and pathology of the brain requires detailed knowledge of its complex structures as well as dynamic internal processes at very different scales from the macro down to the molecular dimensions. A major yet poorly described brain compartment is the brain extracellular space (ECS). Signalling molecules rapidly diffuse through the brain ECS which is complex and dynamic structure at numerous lengths and time scales. In recent years, characterization of the ECS using nanomaterials has made remarkable progress, including local analysis of nanoscopic dimensions and diffusivity as well as local chemical sensing. In particular, carbon nanomaterials combined with advanced optical technologies, biochemical and biophysical analysis, offer novel promises for understanding the ECS morphology as well as neuron connectivity and neurochemistry. In this review, we present the state-of-the-art in this quest, which mainly focuses on a type of carbon nanomaterial, single walled carbon nanotubes, as fluorescent nanoprobes to unveil the ECS features in the nanometre domain.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".