Bibliographic record
Abstract
Abstract Abstract SCI-42 “Self-renewal” is a process central to the expansion of normal and cancerous stem cells and its understanding is critical for future advances in transplantation-based therapies and cancer treatment. Even today, many patients are deprived of the benefit of a successful blood stem cell transplant because the number of allogeneic or autologous stem cells available is insufficient, which results in delayed hematopoietic recovery post-transplant, or exclusion of a transplant-based therapeutic option altogether. The molecular machinery controlling self-renewal of hematopoietic stem cells (HSCs) remains poorly defined with the exception of a few genes such as HOX4 and Bmi1. We and others recently demonstrated the capacity of a recombinant HOXB4 protein (TAT-HOXB4 fusion protein) to stimulate mouse and human HSC self-renewal divisions in culture. Technical difficulties inherent to this recombinant protein have postponed the initiation of clinical trials. In part to overcome this hurdle, we have developed a novel in vitro/in vivo gain-of-function screen and identified several nuclear factors which expand hematopoietic stem cell ex vivo. A significant proportion of these factors display HOXB4-like properties and show non-cell autonomous activity. Initial results suggest that some of these new factors are also active with human cord blood derived HSCs. The generation of novel TAT fusion proteins will open new possibilities in the therapeutic expansion of human HSCs. Disclosures No relevant conflicts of interest to declare.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".