Bilirubin Nanoparticle as an anti-inflammatory therapy for graft versus host disease
Bibliographic record
Abstract
Abstract Graft versus host disease (GvHD) caused by alloreactive donor lymphocytes is a fatal complication of hematopoietic stem cell transplant (HSCT). Conditioning regimen consisting of chemotherapy and/or radiation given prior to HSCT can cause the initial tissue damage, which triggers the cross-presentation of alloantigens to the donor immune cells. Previous studies have shown that water-soluble PEGylated bilirubin nanoparticles (BRNP) selectively accumulate at the site of inflammation and prevent further tissue damage through scavenging reactive oxygen species. Therefore we, hypothesized that BRNP treatment after conditioning regimen can reduce GvHD by abating the initial tissue damage in HSCT, and investigated therapeutic efficacy of BRNP using murine GVHD model. Sublethally irradiated recipient mice (Balb/c) were infused with 4×106 bone marrow and 1×106 splenic T cells from donor mice (C57/B6) on day 1, and with or without BRNP (10mg/kg) on days 0, 2, and 4. GvHD symptoms were monitored for 60 days, and scored for changes in fur, skin, posture, activity, and weight. Untreated recipient mice (n=9) developed significantly worse GVHD (mean GVHD score=4.667) compared to BRNP treated recipient mice (n=9, mean GVHD score=1.556) (p=0.0028). This translated into the significantly better survival of BRNP treated mice with day 60 survival of 65% as compared to the untreated recipient mice with day 60 survival of 11% (p=0.03). In summary, we show that BRNP treatment can ameliorate clinical GvHD symptoms and improve survival in murine GVHD model. In the future, we plan to investigate the role of reactive oxygen species in GVHD and mechanism of action on anti-GVHD effects by BRNP.
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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".