Polyethylenimine-mediated gene delivery into human bone marrow mesenchymal stem cells from patients
Bibliographic record
Abstract
Transplantation of mesenchymal stem cells (MSCs) derived from adult bone marrow has been proposed as a potential therapeutic approach for post-infarction left ventricular (LV) dysfunction. However, age-related functional decline of stem cells has restricted their clinical benefits after transplantation into the infarcted myocardium. The limitations imposed on patient cells could be addressed by genetic modification of stem cells. This study was designed to improve our understanding of genetic modification of human bone marrow derived mesenchymal stem cells (hMSCs) by polyethylenimine (PEI, branched with Mw 25 kD), one of non-viral vectors that show promise in stem cell genetic modification, in the context of cardiac regeneration for patients. We optimized the PEI-mediated reporter gene transfection into hMSCs, evaluated whether transfection efficiency is associated with gender or age of the cell donors, analysed the influence of cell cycle on transfection and investigated the transfer of therapeutic vascular endothelial growth factor gene (VEGF). hMSCs were isolated from patients with cardiovascular disease aged from 41 to 85 years. Optimization of gene delivery to hMSCs was carried out based on the particle size of the PEI/DNA complexes, N/P ratio of complexes, DNA dosage and cell viability. The highest efficiency with the cell viability near 60% was achieved at N/P ratio 2 and 6.0 μg DNA/cm(2) . The average transfection efficiency for all tested samples, middle-age group (<65 years), old-age group (>65 years), female group and male group was 4.32%, 3.85%, 4.52%, 4.14% and 4.38%, respectively. The transfection efficiency did not show any correlation either with the age or the gender of the donors. Statistically, there were two subpopulations in the donors; and transfection efficiency in each subpopulation was linearly related to the cell percentage in S phase. No significant phenotypic differences were observed between these two subpopulations. Furthermore, PEI-mediated therapeutic gene VEGF transfer could significantly enhance the expression level.
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 | 0.000 | 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".