Real-Time Microfluidic Blood-Counting System for PET and SPECT Preclinical Pharmacokinetic Studies
Bibliographic record
Abstract
UNLABELLED: Small-animal nuclear imaging modalities have become essential tools in the development process of new drugs, diagnostic procedures, and therapies. Quantification of metabolic or physiologic parameters is based on pharmacokinetic modeling of radiotracer biodistribution, which requires the blood input function in addition to tissue images. Such measurements are challenging in small animals because of their small blood volume. In this work, we propose a microfluidic counting system to monitor rodent blood radioactivity in real time, with high efficiency and small detection volume (∼1 μL). METHODS: A microfluidic channel is built directly above unpackaged p-i-n photodiodes to detect β-particles with maximum efficiency. The device is embedded in a compact system comprising dedicated electronics, shielding, and pumping unit controlled by custom firmware to enable measurements next to small-animal scanners. Data corrections required to use the input function in pharmacokinetic models were established using calibrated solutions of the most common PET and SPECT radiotracers. Sensitivity, dead time, propagation delay, dispersion, background sensitivity, and the effect of sample temperature were characterized. The system was tested for pharmacokinetic studies in mice by quantifying myocardial perfusion and oxygen consumption with (11)C-acetate (PET) and by measuring the arterial input function using (99m)TcO4 (-) (SPECT). RESULTS: Sensitivity for PET isotopes reached 20%-47%, a 2- to 10-fold improvement relative to conventional catheter-based geometries. Furthermore, the system detected (99m)Tc-based SPECT tracers with an efficiency of 4%, an outcome not possible through a catheter. Correction for dead time was found to be unnecessary for small-animal experiments, whereas propagation delay and dispersion within the microfluidic channel were accurately corrected. Background activity and sample temperature were shown to have no influence on measurements. Finally, the system was successfully used in animal studies. CONCLUSION: A fully operational microfluidic blood-counting system for preclinical pharmacokinetic studies was developed. Microfluidics enabled reliable and high-efficiency measurement of the blood concentration of most common PET and SPECT radiotracers with high temporal resolution in small blood volume.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".