Arterial input function sampling without surgery in rats for positron emission tomography molecular imaging
Bibliographic record
Abstract
AIM: The purpose of this study was to develop a minimally invasive procedure to derive an arterial input function (AIF) in rats through tail artery blood sampling for pharmacokinetic modeling in preclinical PET molecular imaging studies. The procedure involved a microvolumetric blood counter (μBC) and a correction to compensate for delay and dispersion of the automatic blood sampling. MATERIALS AND METHODS: AIFs were simultaneously obtained from femoral and tail arteries in rats, manually and using a μBC, after (18)F-FDG injection (n=6) in order to compare the shape of the AIFs and the kinetic analysis results at equilibrium and after implementation of a dispersion correction method. These AIFs were used to estimate the myocardial metabolic rate of glucose (MMRG). AIFs were also obtained from a single withdrawal site by three methods to confirm accurate MMRG values: manual tail artery (n=5), μBC tail artery (n=5), and μBC femoral artery (n=3). RESULTS: Simultaneous withdrawal at equilibrium results in similar AIF shapes and influx rate constants (Ki) from Patlak analysis (P>0.05). Manually withdrawn and dispersion-corrected μBC AIFs in the simultaneous experiment did not reveal statistically different shapes and constants (K(1), K(i)) from a three-compartment kinetic analysis, regardless of the withdrawal methods or sites (P>0.05). Kinetic analysis of the three single-site blood sampling methods yielded similar MMRG (one-way ANOVA; Patlak, P=0.52; three-compartment, P=0.10). CONCLUSION: Both minimally invasive manual withdrawal and dispersion-corrected μBC-based blood sampling in the tail artery are reliable methods for deriving AIFs for pharmacokinetic follow-up studies in the same animal.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".