Polymer Self‐Assembled BMSCs with Cancer Tropism and Programmed Homing
Bibliographic record
Abstract
Targeted therapy can improve the accuracy of diagnosis and treatment in the field of cancer management. Cellular surface engineering can enhance cell functions via mounting functional molecules onto cellular membranes. A novel amphiphilic hyperbranched polymer (AHP) conjugated with oleic acid (OA) and tumor-targeted ligand folic acid (FA) is employed. The lipophilic chain can self-assemble and infuse with the cytomembrane of bone marrow mesenchymal stem cells (BMSCs) with the end of FA left on the outside for targeting. The polymer tailored BMSCs can enhance tumor tropism in gastric cancer. BMSCs are characterized by the low immunogenicity and tumor tropism, which makes them promising targeting carriers. Regarding the integrated advantages of these two vectors, it is demonstrated that the functional amphiphilic AHP-OA-FA enhances the tumor tropism of BMSCs. Flow cytometry, standard MTT assay, and wound-healing assay show that AHP-OA-FA has no influence on CD expression, proliferative capacity, and cell motility of BMSCs, respectively. Furthermore, in vitro transwell assay and ex vivo fluorescence image verify that AHP-OA-FA enhances tumor tropism of BMSCs compared to BMSCs and AHP-OA-Rhodamine B-BMSCs. Finally, histological analysis demonstrates that AHP-OA-FA causes no damage to major organs. The results of this study suggest that living BMSCs self-assembled with a polymer might be a promising vehicle for targeted delivery to cancer cells.
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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) | 0.000 | 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".