Evaluation of Phosphatidylserine-Binding Peptides Targeting Apoptotic Cells
Bibliographic record
Abstract
The inhibition or dysregulation of apoptosis plays an intimate role in the initiation and progression of cancer by confounding normal tissue homeostasis. We currently do not have a clinical method to assess apoptosis induced by cancer therapies. Phosphatidylserine (PS) is an attractive target for imaging apoptosis because it is on the exterior of the apoptotic cells and PS externalization is an early marker of apoptosis. PS-binding peptides are an attractive option for developing an imaging probe to detect apoptosis using positron emission tomography. In this study, four peptides were evaluated for PS-binding characteristics using a plate-based assay system, a liposome mimic of cell membrane PS presentation, and a cell assay of apoptosis. This work also describes two screening techniques to enable researchers to identify and optimize compounds that bind to PS. The results of our study indicate that all four peptides bind to PS and are specific to apoptotic cells. Two of the peptides in particular that have an additional cysteine residue are good potential candidates for development into imaging probes because they bind to PS with high affinity and specificity and they can be easily radiolabelled with (18)F.
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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 | 0.000 | 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".