A Microvolumetric $\beta$ Blood Counter for Pharmacokinetic PET Studies in Small Animals
Bibliographic record
Abstract
Radiotracer kinetic modeling in small animals with Positron Emission Tomography (PET) requires the determination of the blood tracer concentration as a function of time. A continuous blood counting system was designed to measure the input function from rats and mice in real time. The system consists of a flow-through beta counter made of silicon PIN photodiodes and a mul syringe pump. The latter draws blood continuously from an implanted venous or arterial catheter, at a user selected rate. The direct beta detection by photodiodes minimizes the shield footprint next to the animal and reduces the counter sensitivity to ambient gamma radiation. The device is entirely remote controlled for sampling protocol selection, tuning, and real time monitoring of measured parameters. It can be hooked to a computer or fully integrated with the LabPETtrade scanner for blood counting during dynamic PET imaging experiments. The counter sensitivity to the most popular PET radioisotopes (18F,64Cu,13N,11C) ranges from 7.1 to 46.8 Bq/mul. Its linearity is better than 98% up to 46 kBq/mul for18F, and a 1.4 s dispersion constant was measured at a rate of 250 mul/min with rat whole blood in a PE10 catheter. Due to its optimized mechanical design and compact shielding, the counter sensitivity to radioactive background is only 5 counts per second (cps) for a 37 MBq18F source 10 cm away from the detector. Accurate time-activity curves have been obtained from rats and mice in dynamic PET imaging 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".