Human progenitor cells with high aldehyde dehydrogenase activity efficiently engraft into damaged liver in a novel model†
Bibliographic record
Abstract
UNLABELLED: Human cord blood stem cells (hCBSCs) have been reported to generate hepatocyte-like cells and thus hold promise for repairing damaged liver. However, the frequency of hCBSC-derived hepatocytes varies tremendously between different studies, and it is still controversial as to whether hCBSC-derived cells can transdifferentiate into hepatocytes or simply fuse to recipient hepatocytes. We used the beta-glucuronidase-deficient nonobese diabetic/severe combined immunodeficient/mucopolysaccharidosis type VII (NOD/SCID/MPSVII) mouse model for better identification of engrafted cells. We transplanted lineage-depleted human umbilical cord blood-derived cells with high aldehyde dehydrogenase activity (ALDH(hi)Lin(-)) into irradiated NOD/SCID/MPSVII mice followed by carbon tetrachloride administration to induced liver damage. ALDH(hi)Lin(-) cells were efficiently engrafted in the recipient mouse livers and improved recovery of the mice from toxic insult. The percentage of human cells in these livers ranged between 3% and 14.2% using quantitative real-time polymerase chain reaction. Furthermore, human-originated cells expressing liver-specific alpha1-antitrypsin messenger RNA, albumin and hepatocyte nuclear factor 1 protein were detected in the recipient livers. Interestingly, human versus murine centromeric fluorescent in situ hybridization analysis on the liver sections demonstrated that most human cells were not fused to mouse cells. However, the majority of the human originated albumin-expressing cells also carried mouse genetic material, hence were the product of cell fusion. CONCLUSION: hCBSCs or their progeny may home to the injured liver and release trophic factors that hasten tissue repair, whereas fusion of these cells with hepatocytes may occur rarely and contribute to a lesser extent to liver repair.
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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 | 0.001 |
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".