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Record W4311616591 · doi:10.3390/pharmaceutics14122809

Potential of Nuclear Imaging Techniques to Study the Oral Delivery of Peptides

2022· review· en· W4311616591 on OpenAlexaff
Tanya Saxena, Claire Sie, Kristine Lin, Daisy Ye, Katayoun Saatchi, Urs O. Häfeli

Bibliographic record

VenuePharmaceutics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsUniversity of British Columbia
FundersH. Lundbeck A/SNovo Nordisk FondenScience and Engineering Research BoardNovo NordiskLundbeckfonden
KeywordsNuclear imagingPeptideMolecular imagingMedicineChemistryNanotechnologyBiochemistryMaterials scienceBiologyNuclear medicineBiotechnologyIn vivo

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.402
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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