Neutrophils generated in vitro from hematopoietic stem cells isolated from apheresis samples and umbilical cord blood form neutrophil extracellular traps
Bibliographic record
Abstract
Neutrophils release neutrophil extracellular traps (NET) comprising of decondensed chromatin that immobilizes and kills pathogens. In vitro generation of neutrophils on a large scale from hematopoietic stem cells (HSCs) may be a useful strategy for treating neutropenic patients in future, though it is not in clinical practice yet. Microbial infections lead to major cause of morbidity and mortality in these patients. Despite the importance of NET in preventing infection, efficacy of in vitro-generated neutrophils from HSCs to form NET is not tested. We show that functional neutrophils could be generated in vitro from HSCs/MNCs isolated from umbilical cord blood (UCB) and apheresis-derived peripheral blood (APBL). Neutrophils generated from UCB showed properties comparable to those isolated from peripheral blood. We also show that isolation of HSCs is not absolutely essential for in vitro neutrophil generation. Further, we show that neutrophils generated from HSCs express PADI4 enzyme and their NET-forming ability is comparable to peripheral blood neutrophils. Taken together, our data show that fully functional neutrophils can be generated in vitro from HSCs. NET-forming ability of in vitro-generated neutrophils is an important parameter to determine their functionality and thus, should be studied along with other standard functional assays.
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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) | 0.002 | 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".