Development and characterisation of novel oxytocin analogues for PET imaging
Bibliographic record
Abstract
Abstract The oxytocin/oxytocin receptor (OT/OTR) signalling system is involved in socioemotional behaviours, garnering interest as a therapeutic target across multiple clinical conditions. Despite its potential, our limited understanding of how to optimally target it and the scarcity of molecular tools for in vivo studies hinder therapeutic development. Molecular imaging techniques, such as Positron Emission Tomography (PET), can bridge this gap by furnishing direct insights into ligand biodistribution, receptor visualisation and ligand-receptor engagement. Here, we report the design, synthesis and biochemical and pharmacological characterisation of five OT-like peptides as novel PET tracers for investigating the OT/OTR signalling system. dOTK 8 [SFB] emerged as the most promising OT-like lead. The radioactive version [ 18 F]dOTK 8 [SFB] was produced using a microfluidic reaction approach and validated by preclinical PET imaging of healthy rats after intravenous ligand administration. [ 18 F]dOTK 8 [SFB] exhibited specific accumulation in OTR-rich tissues, affirming OTR-specificity and suitability as a new OT-like PET radiotracer for investigating OT/OTR biodistribution in humans.
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 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".