Gene transfer using artificial chromosomes containing a marker gene in primary cells and rat adjuvant arthritis
Bibliographic record
Abstract
Transfer efficiency and optimal dose of transfection agent were determined using iododeoxyuridine (IdUrd)-incorporated AC es complexed to Superfect (Qiagen) (Cytometry Vol 44:100–105, 2001). Following transfection, AC es were antibody-labeled and cells were analyzed by flow cytometry and fluorescent photomicroscopy. Using optimized transfection conditions and after growing the cells under selection, we expanded the colonies and determined reporter gene expression. Cell lines or primary cells transfected with Aces containing the LacZ gene or control cells were injected into the right ankle joints of rats with adjuvant arthritis on day 12 after adjuvant immunization. Joints were harvested 2 and 7 days later and assayed for beta-gal activity. The delivery of intact artificial chromosomes was detected within 24 h post transfection. Maximum delivery rates of 27% were observed. Flow cytometry data correlated well with microscopic analysis. After growing cell lines and primary cells under selection, clones expressing LacZ and RFP were identified. Ex vivo transfer of the LacZ gene to the ankle joints of rats with AA resulted in expression of the marker gene in the synovium. In the other organs, LacZ expression was not detected two days after injection of the cells. The results demonstrate that IdUrd-labelled ACes can be detected 24 h after transfection. Both cell lines and primary cells can be efficiently transfected with AC es and express the transgene. In addition, we show successful local ex vivo gene transfer in a rat AA model using AC es containing a marker gene. This work demonstrates the potential feasibility of treating arthritis and other connective tissue diseases utilizing AC es as nonviral vectors for gene therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".