Active Hydrophilic Graphene Oxide Nanocomposites Delivery Mediated by Adipose-Derived Stem Cell for Elevated Photothermal Therapy of Breast Cancer
Bibliographic record
Abstract
Purpose: Graphene oxide (GO) and its derivatives have recently been identified as promising candidates for early disease diagnosis and therapy. However, the physiological stability and precise launch requirements present limitations on further clinical practices. Adipose-derived stem cells (ADSCs) were employed as an unobstructed biological vehicle to address the validate this ADSC-based tumor-targeting system for highly efficient GO delivery combined with two-stage NIR radiation for superior tumor ablation. Methods: GO was modified with poly-ethylene glycol (PEG) and folic acid (FA). Afterward, the GO nanocomposite was internalized into ADSCs. The GO-PEG-FA-laden ADSCs were injected into the tail veins of the tumor-bearing mice. Subsequently, first-stage NIR radiation was utilized to disrupt the ADSCs for GO-PEG-FA release. After this, the heat generated by secondary-stage NIR radiation destroy the malignant cells and shrink the tumor, and the cascade process could be recycled until complete tumor ablation if necessary. Results: The GO-PEG-FA nanocomposite exhibited negligible cytotoxicity and could be internalized into ADSCs to target specific tumor sites after 32 days of intravenous injection. The nanocomposite was released from the ADSCs and taken up into cancer cells again with the assistance of FA after the first dose of near-infrared radiation. Then, the second radiation dose could directly strike the cancer cell for cancer ablation. Conclusion: In summary, we reported a stem cell-based anticancer system that used GO-PEG-FA-laden ADSCs for breast cancer therapy through NIR treatment in mice potentially opens a new avenue not only to address precise drug targeting in tumor therapy, but also future clinical practice in diverse areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".