Optimization of Targeted Cell Replacement Therapy: A New Approach for Lung Disease
Bibliographic record
Abstract
Cell replacement therapy is a promising approach for treatment of lung disease such as cystic fibrosis, although rates of engraftment need to be improved. We previously showed improved cell retention in the lung using transtracheal delivery compared to intravenous injection. Here, we optimized other parameters of cell delivery using 7-day cultured bone marrow cells (BMCs). Retention of BMC in the lung was dose-dependent. Naphthalene treatment had maximal effects on BMC retention when given 2 days before cell delivery. Naphthalene treatment of the donor amplified a CCSP(+) population and increased retention efficiency in the recipient. Repeated naphthalene treatment and repeated cell delivery both resulted in greater retention. The contribution of the second cell dose was minimal suggesting that a second delivery of BMC promotes proliferation of the first. Busulfan-induced myelosuppression augmented retention of exogenous BMC by up to 20-fold. These BMC helped CCSP reconstitution. Using the optimal delivery techniques and cytokeratin-18-driven green fluorescent protein (GFP) reporter mice, we detected threefold more GFP suggesting more BMC differentiated to epithelial cells. We propose that improved engraftment in the lung will increase cell replacement and thus be a more efficient therapeutic approach for various lung diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".