Noninvasive bioluminescent imaging of primary patient acute lymphoblastic leukemia: a strategy for preclinical modeling
Bibliographic record
Abstract
The efficient engraftment in immune-deficient mice achieved with both acute lymphoblastic leukemia (ALL) cell lines and primary samples has facilitated identification of the antileukemia activity of a wide variety of agents. Despite widespread usage, however, little is known about the early ALL localization and engraftment kinetics in this model, limiting experimental read-outs primarily to survival and endpoint analysis at high disease burden. In this study, we report that bioluminescent imaging can be reproducibly achieved with primary human ALL samples. This approach provides a noninvasive, longitudinal measure of leukemia burden and localization that enhances the sensitivity of treatment response detection and provides greater insight into the mechanism of action of antileukemia agents. In addition, this study reveals significant cell line- and species-related differences in leukemia migration, especially early in expansion, which may confound observations between various leukemia models. Overall, this study demonstrates that the use of bioluminescent primary ALL allows the detection and quantitation of treatment effects at earlier, previously unquantifiable disease burdens and thus provides the means to standardize and expedite the evaluation of anti-ALL activity in preclinical xenograft studies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".