19F-perfluorocarbon-labeled human peripheral blood mononuclear cells can be detected in vivo using clinical MRI parameters in a therapeutic cell setting
Bibliographic record
Abstract
Abstract A 19Fluorine (19F) perfluorocarbon cell labeling agent, when employed with an appropriate cellular MRI protocol, allows for in vivo cell tracking. 19F cellular MRI can be used to non-invasively assess the location and persistence of cell-based cancer vaccines and other cell-based therapies. This study was designed to determine the feasibility of labeling and tracking peripheral blood mononuclear cells (PBMC), a heterogeneous cell population. Under GMP-compliant conditions human PBMC were labeled with a 19F-based MRI cell-labeling agent in a manner safe for autologous re-injection. Greater than 99% of PBMC labeled with the 19F cell-labeling agent without affecting functionality or affecting viability. The 19F-labeled PBMC were detected in vivo in a mouse model at the injection site and in a draining lymph node. A clinical cellular MR protocol was optimized for the detection of PBMC injected both at the surface of a porcine shank and at a depth of 1.2 cm, equivalent to depth of a human lymph node, using a dual 1H/19F dual switchable surface radio frequency coil. This study demonstrates it is feasible to label and track 19F-labeled PBMC using clinical MRI protocols. Thus, 19F cellular MRI represents a non-invasive imaging technique suitable to assess the effectiveness of cell-based cancer vaccines.
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) | 0.000 | 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) | 0.002 | 0.001 |
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".