Interleukin-10 delivery via mesenchymal stem cells (MSC) to prevent ischemia/reperfusion injury in lung transplantation (141.46)
Bibliographic record
Abstract
Abstract Ischemia-reperfusion injury (IR) is an important cause for lung graft loss (~30%). In this study, MSC & viral interleukin-10 (vIL-10) engineered MSC were tested for their ability to prevent lung IR injury. Bone marrow derived MSC from Lewis rat were transduced with rvIL-10-retrovirus & selected on neomycin. Following 120 min of left lung ischemia induction, Group A, rats received vIL-10-MSC (~15 x 106; i.v.); Group B, empty vector engineered MSC; Group C, MSC; Group D, saline; and Group E, no ischemia or MSC. Mean blood oxygenation (PaO2/FiO2 ratio, mmHg) was reduced (P<0.05) at 24h post-IR injury in Group B (138±86; n=9) & Group D (87±39; n=10), compared to MSC-vIL10 (353±105; Group A; n=10) group. By days 3 & 7 with MSC-vIL10 oxygenation was normal (475±55 & 435±33; n>9); by 4h it was 319±94 (n=7). MSC (passage ≤6) increased PaO2/FiO2 (454 ± 59; n=5) by 24h post-IR. Bronchoalveolar lavage at 24h post-MSC-vIL10 therapy reduced (P<0.05) granulocytes, CD4 & CD8 T cells. Lung injury score (histopathology) was higher (P<0.05) with no treatment (3.5 ± 1.3; n=5) compared to MSC-vIL10 (1.21± 0.6; n=7) & MSC (1.6±0.9; n=6) treated groups. Lung microvascular permeability & wet:dry ratio were lower (P<0.05) in MSC-vIL10 group. IL-1α, MCP-1α, MIP-1α, & IL1-β were increased in IR injured lung. ISOL (in situ staining for DNA fragmentation) & CASPACE-3 demonstrated reduced (p<0.05) number of apoptotic cells in MSC-vIL10 treated lungs. Ex vivo, expanded MSC were CD34-, CD31+ & CD45+ (5-10%), CD29+, CD90+ & CD44+ (65-95%), CD80 (0%), CD 86 (8%), MHC Class I+ (23-57%), & MHC Class II-. MSC & IL-10 delivery via MSC to prevent lung transplant IR injury seems promising.
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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 | 0.001 |
| 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".