Cell-specific optoporation with near-infrared ultrafast laser and functionalized gold nanoparticles
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
Selective targeting of diseased cells can increase therapeutic efficacy and limit off-target adverse effects. We developed a new tool to selectively perforate living cells with functionalized gold nanoparticles (AuNPs) and near-infrared (NIR) femtosecond (fs) laser. The receptor CD44 strongly expressed by cancer stem cells was used as a model for selective targeting. Citrate-capped AuNPs (100 nm in diameter) functionalized with 0.01 orthopyridyl-disulfide-poly(ethylene glycol) (5 kDa)-N-hydroxysuccinimide (OPSS-PEG-NHS) conjugated to monoclonal antibodies per nm(2) and 5 μM HS-PEG (5 kDa) were colloidally stable in cell culture medium containing serum proteins. These AuNPs attached mostly as single particles 115 times more to targeted CD44(+) MDA-MB-231 and CD44(+) ARPE-19 cells than to non-targeted CD44(-) 661W cells. Optimally functionalized AuNPs enhanced the fs laser (800 nm, 80-100 mJ cm(-2) at 250 Hz or 60-80 mJ cm(-2) at 500 Hz) to selectively perforate targeted cells without affecting surrounding non-targeted cells in co-culture. This novel highly versatile treatment paradigm can be adapted to target and perforate other cell populations by adapting to desired biomarkers. Since living biological tissues absorb energy very weakly in the NIR range, the developed non-invasive tool may provide a safe, cost-effective clinically relevant approach to ablate pathologically deregulated cells and limit complications associated with surgical interventions.
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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 | 0.000 | 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 it