Magnetic carbon nanotube labelling for haematopoietic stem/progenitor cell tracking
Bibliographic record
Abstract
Haematopoietic stem and progenitor cell (HSPC) research has significantly contributed to the understanding and harnessing of haematopoiesis for regenerative medicine. However, the methodology for real-time tracking HSPC in vivo is still lacking, which seriously restricts the progress of research. Recently, magnetic carbon nanotubes (mCNT) have generated great excitement because they have been successfully used as vehicles to deliver a lot of biomolecules into various cells. There is, however, no report about mCNT being used for tracking HSPC. In this paper, we investigated the uptake efficiency of fluorescein-isothiocyanate-labelled mCNT (FITC-mCNT) into HSPC and their effect on the cytotoxicity and differentiation of HSPC. We found that cellular uptake of FITC-mCNT was concentration-and time-dependent. The uptake of FITC-mCNT into HSPC reached up to 100% with the highest mean fluorescence (MF). More importantly, efficient FITC-mCNT uptake has no adverse effect on the cell viability, cytotoxicity and differentiation of HSPC as confirmed by colony-forming unit assay (CFU). In conclusion, the results reported here suggest the further tailoring of mCNT for their use in HSPC labelling/tracking in vivo or gene delivery into HSPC.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".