Perfusion fixation methods for preclinical biodistribution studies: A comparative assessment using automated image processing
Bibliographic record
Abstract
requires effective and consistent perfusion and fixation of major organs. Standard methods for removing red blood cells (RBCs) and fixing tissue often involve transcardial perfusion, such as brain-targeted perfusion (via the left ventricle) or lung-targeted perfusion (via the right ventricle). Using autofluorescence measurements and a bespoke ImageJ macro to quantify RBC content from histology, we compared the efficacy and consistency of three whole-body perfusion techniques. We show that lung-targeted perfusion evacuates more blood from the lung vasculature than brain-targeted perfusion (20 ± 54% fewer RBCs), and that our novel approach of 'dual-targeted' perfusion (via the right and left ventricles sequentially) had even higher efficacy (30 ± 6% fewer RBCs). Furthermore, by combining aspects of brain- and lung-targeted methods, dual-targeted perfusion achieved the highest consistency in autofluorescence emissions from major organs (64% and 65% lower variance than brain- and lung-targeted perfusion respectively). Since RBC content and autofluorescence can be confounding factors in biodistribution studies using fluorescent probes, our findings and proposed novel approach offer insight into perfusion fixation techniques for pre-clinical studies.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".