Optimizing Photoacoustic Measurement of Lymphatic Drainage of Cerebrospinal Fluid in Pigmented Mice for Microgravity Experiments
Bibliographic record
Abstract
Non-invasive dynamic imaging techniques are needed to study the lymphatic drainage of cerebrospinal fluid, whose role in various diseases is actively being investigated. The optimized set of optical wavelengths and spectral unmixing algorithm is pursued in this study to accurately quantify the lymphatic drainage in pigmented mice by multispectral optoacoustic tomography. Several optical wavelength sets and spectral unmixing algorithms for multispectral photoacoustic tomography with a near-infrared tracer were compared. The combination of 11 wavelengths, selected based on absorption spectra of chromophores and exclusion of melanin from the linear regression unmixing algorithm, provided the best spatial similarity to reference images obtained using 151 wavelengths. Furthermore, the 11 wavelengths without melanin in linear regression unmixing algorithm showed the most accurate quantification of mean pixel intensity of the tracer in the neck lymph nodes and its change, compared to other sets of wavelengths and unmixing algorithms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".