Controlling Culture Dynamics for the Expansion of Hematopoietic Stem Cells
Bibliographic record
Abstract
The ex vivo expansion of hematopoietic stem cells (HSCs) is the subject of intense commercial and academic interest due to the potential of HSCs to be a renewable source of material for cellular therapeutics. Unfortunately, because methodologies have not yet been developed to grow clinically relevant numbers of HSCs (or their derivatives) consistently, the potential of this technology is limited. Manipulation of the in vitro culture microenvironment, primarily through cytokine supplementation, has been the predominant approach in studies attempting to expand primary human HSC numbers in vitro. While promising results have been obtained, it is becoming clear that novel methods must be developed before cellular therapies using these stem cells can become routine. Ideally, bioprocesses must be designed to target specifically the growth of stem cell populations while incorporating positive and negative feedback from potentially dynamic mature and maturing cell populations. The product of these culture systems should consist of not only HSCs, but also of cells that allow the engraftment of HSCs and, ideally, cells responsible for the immediate or accelerated functional support of patients. Development of such "designer transplants" will require combining optimal culture conditions capable of amplifying HSC numbers with novel approaches for finely controlling the number, functional capabilities, and characteristics of potentially therapeutic cells in these very complex cell culture systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".