Imaging the brain in vivo with reflectance phase-contrast confocal microscopy at 1650nm
Bibliographic record
Abstract
Recent advancements in sources and detectors operating in the NIR-II wavelengths have driven the emergence of NIR-II intrinsic microscopy. These significant technological strides were necessary because longer wavelengths are known to experience reduced scattering and absorption in biological tissue. Leveraging this optical advantage, the application of the NIR-II spectral domain in microscopy holds the potential to improve the depth of imaging and preserve coherence depth. In this study, we showcase the integration of phase-contrast imaging into a NIR-II reflectance confocal microscope for cortical imaging. By capturing images of cortical cell bodies at depths of up to 800 μm, we demonstrate that the implementation of phase contrast provides clear delineation of cortical cell edges, including myelinated axons, blood vessels, and cortical cell bodies. Additionally, we devised a computational method to enhance dynamic components and generate a digitized vascular network from the acquired images.
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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 | 0.000 |
| 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".