Characterization of 5-(2-18F-fluoroethoxy)-L-tryptophan for PET imaging of the pancreas
Bibliographic record
Abstract
Purpose : In diabetes, pancreatic beta cell mass declines significantly prior to onset of fasting hyperglycemia. This decline may be due to endoplasmic reticulum (ER) stress, and the system L amino acid transporter LAT1 may be a biomarker of this process. In this study, we used 5-(2- 18 F-fluoroethoxy)-L-tryptophan ( 18 F-L-FEHTP) to target LAT1 as a potential biomarker of beta cell function in diabetes. Procedures: Uptake of 18 F-L-FEHTP was determined in wild-type C57BL/6 mice by ex vivo biodistribution. Both dynamic and static positron emission tomography (PET) images were acquired in wild-type and Akita mice, a model of ER stress-induced diabetes, as well as in mice treated with streptozotocin (STZ). LAT1 expression in both groups of mice was evaluated by immunofluorescence microscopy. Results: Uptake of 18 F-L-FEHTP was highest in the pancreas, and static PET images showed highly specific pancreatic signal. Time-activity curves showed significantly reduced 18 F-L-FEHTP uptake in Akita mice, and LAT1 expression was also reduced. However, mice treated with STZ, in which beta cell mass was reduced by 62%, showed no differences in 18 F-L-FEHTP uptake in the pancreas, and there was no significant correlation of 18 F-L-FEHTP uptake with beta cell mass. Conclusions: 18 F-L-FEHTP is highly specific for the pancreas with little background uptake in kidney or liver. We were able to detect changes in LAT1 in a mouse model of diabetes, but these changes did not correlate with beta cell function or mass. Therefore, 18 F-L-FEHTP PET is not a suitable method for the noninvasive imaging of changes in beta cell function during the progression of diabetes.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".