Inhibition of xenogeneic GVHD by PEN110 treatment of donor human PBMNCs
Bibliographic record
Abstract
BACKGROUND: Development and characterization of methods for preventing transfusion-associated GVHD have utilized in vitro studies with human WBCs and in vivo studies in animal models. The limitation of these assays is that the in vivo GVHD response of treated human WBCs has not been tested directly. STUDY DESIGN AND METHODS: PBMNCs isolated from nonleukoreduced RBC units exposed to gamma irradiation, treated with PEN110 or PBS, were tested for their ability to induce xenogeneic GVHD when injected into severe combined immunodeficient (SCID) mice. RESULTS: These studies showed that the SCID mice injected with PBS-treated PBMNCs developed serum levels of human immunoglobulin that were followed by weight loss and display of ruffled fur characteristic of xenogeneic GVHD in these mice. In contrast, SCID mice injected with PEN110-treated or gamma-irradiated PBMNCs did not exhibit any of these responses. CONCLUSIONS: In these studies PEN110 treatment and gamma irradiation were equally effective at preventing in vivo GVHD responses when the treated cells were injected into SCID recipients. These results are consistent with previous results obtained when these two treatment methods were compared with in vitro studies with PBMNCs and in vivo studies in mouse models.
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) | 0.002 | 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".