Graphene Oxide‐Based Gene Modulation in Preferential Elimination of Lung Cancer Cells in a 3D Tumor Microenvironment Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer‐related mortality worldwide, owing to its aggressive nature, late‐stage diagnosis, and resistance to conventional therapies. Gene therapy offers a promising alternative by modulating specific genetic pathways to target cancer cells while sparing healthy ones. This study investigates the potential of chemically functionalized nanoscale graphene oxide (GO) as carriers for delivering therapeutic genes in a 3D tumor microenvironment (TME) model, incorporating lung cancer cells, human lung fibroblasts, and macrophages in a Matrigel‐collagen matrix to mimic the structural properties and immune functions. These therapeutic genes, including small interfering RNAs and plasmid DNAs, regulate immune evasion markers (CD47 and CD24) and apoptosis‐inducing proteins (ANT1). GO nanocarriers demonstrate preferential uptake in cancer cells, achieving transfection and gene modulation within the TME model. The individual delivery of genes downregulates cancer markers and induces ANT1 expression, resulting in lung cancer cell elimination. Co‐delivery of CD47_siRNA and ANT1_pDNA produces synergistic efficacy, enhancing cancer cell elimination. These findings highlight the potential of GO‐based gene therapies as a targeted and effective approach for lung cancer treatment, setting the stage for in vivo validation and clinical translation.
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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 | 0.001 | 0.001 |
| 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 it