Doxorubicin-loaded NK exosomes enable cytotoxicity against triple-negative breast cancer spheroids.
Bibliographic record
Abstract
Objectives: Natural killer (NK) cells are the most professional innate immune cells that initiate extracellular apoptosis via cytotoxic granules in malignant cells. Antitumoral properties of NK-derived exosomes (Exos) are attributed to their parent cells. Loading drugs into Exos as a carrier can enhance their effect and enable targeted delivery. In the present study, we aim to deliver Doxorubicin (DOX) to the breast cancer spheroids by NK-Exos. Materials and Methods: Peripheral blood mononuclear cells (PBMC) were used to harvest NK cells, and NK-Exos were isolated from NK cell expansion medium using an Exo-spinTM kit. DOX was loaded via the ultrasonication method. AO/EtBr, Annexin/PI, DAPI, MTT, and spheroids of human breast cancer were used to track the cytotoxic effect of DOX-NK-Exos. The colony formation assay, scratch and transwell assays, Real-Time PCR for p53 and VEGF-A, and WB for protein expression were also performed. Results: When compared to free DOX, all viability tests validated the inhibitory effects of DOX-NK-Exos. The obtained results indicated that DOX-NK-Exos selectively reduced tumor cell viability and spared fibroblast and MCF-10A as noncancerous cells. Long after spheroid treatment, DOX-NK-Exos' remarkable effect persisted. Conclusion: Human breast carcinoma mass treated with DOX-NK-Exos underwent apoptosis and showed a strong inhibitory effect on proliferation. Thus, they can reduce the side effects of chemotherapeutics and can be used as drug carriers with selective toxicity. Additionally, the additive action of this combination formula results in a more severe loss in cell viability.
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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".