In Vivo Imaging of Lymphatic Drainage of Cerebrospinal Fluid in Mouse
Bibliographic record
Abstract
BACKGROUND: Mouse models are commonly used to study central nervous system disorders, in which cerebrospinal fluid (CSF) drainage may be disturbed. However, mouse CSF drainage into lymphatics has not been thoroughly characterized. We aimed to image this using an in vivo approach that combined quantum dot fluorescent nanoparticles with hyperspectral imaging. FINDINGS: Quantum dot 655 was injected into the CSF of the cisterna magna in seven mice and visualized by in vivo hyperspectral imaging at time points 20 and 40 min, 1, 2, and 6 h after injection. In controls (n = 4), quantum dots were applied directly onto intact dura mater covering the cisterna magna. After imaging, lymph nodes in the neck were harvested and processed post-mortem for histological analysis. After injection into the CSF, quantum dot signal was detected in vivo in submandibular lymph nodes of all mice studied as early as 20 min, but not in controls. Post-mortem gross and histological examination of lymph nodes confirmed in vivo observations. CONCLUSIONS: Non-invasive in vivo hyperspectral imaging is a useful tool to study CSF lymphatic drainage and is relevant to understanding this pathway in CNS disease models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".