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 Manually withdrawn and dispersion-corrected μBC AIFs in the simultaneous experiment did not reveal statistically different shapes and constants (K(1), K(i)) from a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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".