Cell-specific optoporation with near-infrared ultrafast laser and functionalized gold nanoparticles
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".