Potential of Nuclear Imaging Techniques to Study the Oral Delivery of Peptides
Bibliographic record
Abstract
Peptides are small biomolecules known to stimulate or inhibit important functions in the human body. The clinical use of peptides by oral delivery, however, is very limited due to their sensitive structure and physiological barriers present in the gastrointestinal tract. These barriers can be overcome with chemical and mechanical approaches protease inhibitors, permeation enhancers, and polymeric encapsulation. Studying the success of these approaches pre-clinically with imaging techniques such as fluorescence imaging (IVIS) and optical microscopy is difficult due to the lack of in-depth penetration. In comparison, nuclear imaging provides a better platform to observe the gastrointestinal transit and quantitative distribution of radiolabeled peptides. This review provides a brief background on the oral delivery of peptides and states examples from the literature on how nuclear imaging can help to observe and analyze the gastrointestinal transit of oral peptides. The review connects the fields of peptide delivery and nuclear medicine in an interdisciplinary way to potentially overcome the challenges faced during the study of oral peptide formulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".