Anti‐EpCAM Gold Nanorods and Femtosecond Laser Pulses for Targeted Lysis of Retinoblastoma
Bibliographic record
Abstract
Abstract Retinoblastoma is a cancerous disease that affects the retina, and primarily affects young children. To date, the primary treatment goal of retinoblastoma is to save the child's life, while the preservation of the eye and its functionality are the secondary goals. Reoccurrence of tumors is mainly attributed to the persistence of cancer stem cells. EpCAM+ Y79 retinoblastoma cells behave like cancer stem cells and are recognized as cells that are resistant to treatment. We demonstrate an effective technique to treat retinoblastoma cancer cells, using femtosecond laser pulses and epithelial cell adhesion molecule (EpCAM)‐targeting gold nanorods (Au‐NRs). Complete assessment of the optimal laser parameters required for the development of a translational retinoblastoma cancer treatment is provided. Both an MTS cellular metabolism assay and a fluorescence viability assay demonstrate an astonishing cellular viability drop, to ≈10%. Right after laser irradiation the cellular membrane ruptures. Calculations and field‐emission scanning electron microscopy (FESEM) imaging show that Au‐NRs reach melting temperature after laser pulse exposure. Delivering femtosecond laser pulses directly onto the retina to treat retinoblastoma through the medium of the eye is possible without interacting with its compartments—making this treatment ideal for this type of cancer. This treatment methodology would be an invaluable tool for treatment of chemotherapy‐resistant and radiation‐resistant cancers.
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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".