Blood-Derived ALDHhi Cells in Tissue Repair
Bibliographic record
Abstract
Cell sorting based on high aldehyde dehydrogenase (ALDH) activity has emerged as a clinically applicable method to purify human bone marrow (BM) and umbilical cord blood (UCB) progenitors based on a conserved stem cell function. Although rare, ALDH hi cells are highly enriched for progenitors of hematopoietic, endothelial, and mesenchymal stromal cell (MSC) lineages. Transplanted ALDH hi progenitors are under investigation in clinical trials to enhance UCB engraftment in adults undergoing transplantation. Transplanted BM ALDH hi cells also recruited to areas of tissue ischemia and augment endogenous revascularization and recovery after femoral artery ligation. Moreover, ex vivo expanded MSCs from ALDH-purified cells stimulated the neogenesis of small beta cell clusters in models of pancreatic injury. Understanding how angiogenic and regenerative programs are stimulated by ALDH hi progenitor subsets may provide new approaches in progenitor cell therapy for tissue repair. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".