The effect of UVA light/8-methoxypsoralen exposure used in Extracorporeal Photopheresis treatment on platelets and extracellular vesicles
Bibliographic record
Abstract
Abstract Extracorporeal Photopheresis (ECP) is a leukapheresis based treatment for Cutaneous T-Cell Lymphoma, which takes advantage of the cellular lethal effects of UVA light in combination with a photoactivated drug, 8-methoxypsoralen. 25% of patients treated with ECP do not respond to treatment, however the underlying mechanisms for this lack of response remain unknown. Platelets, a rich source of extracellular vesicles (EVs) and key mediators in thromboinflammatory oncological progression, as well as leukocytes, are both processed through ECP and are subsequently transfused back into the patient, delivering potent immunomodulation. The effect of exposing platelets and their EVs directly to UVA/8-methoxypsoralen is currently unknown. Platelet-rich plasma (PRP) was isolated from healthy donors and exposed to UVA light and/or 8-methoxysporalen in vitro and platelet activation and aggregation was assessed. EV size and concentration were also characterised by Nanoparticle Tracking Analysis and Flow Cytometry. We found that UVA light and 8-methoxypsoralen treatment in vitro did not induce platelet aggregation or alter significantly levels of the platelet activation markers soluble P-selectin or platelet factor 4, with circulating levels of small and large EV size and concentration remaining constant. Therefore, utilising the combination of UVA light and 8-methoxypsoralen used in ECP in vitro does not activate platelets or alter important circulating EVs. Further studies will be needed to validate if our observations are consistent in vivo .
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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) | 0.002 | 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".