A needle-like optofluidic probe enables targeted intracellular delivery by confining light-nanoparticle interaction on single cell
Bibliographic record
Abstract
Intracellular delivery of molecular cargo is the basis for a plethora of therapeutic applications, including gene therapy and cancer treatment. A very efficient method to perform intracellular delivery is the photo-activation of nanomaterials that have been previously directed to the cell vicinity and bear releasable molecular cargo. However, potential in vivo applications of this method are limited by our ability to deliver nanomaterials and light in tissue. Here, we demonstrate intracelullar delivery using a needle-like optofluidic probe capable of penetrating soft tissue. Firstly, we used the optofluidic probe to confine an intracellular delivery mixture, composed of 100 nm gold nanoparticles (AuNP) and membrane-impermeable calcein, in the vicinity of cancer cells. Secondly, we delivered nanosecond (ns) laser pulses (wavelength: 532 nm; duration: 5 ns) using the same probe and without introducing a AuNP cells incubation step. The AuNP photo-activation caused localized and reversible disruption of the cell membrane, enabling calcein delivery into the cytoplasm. We measured 67% intracellular delivery efficacy and showed that the optofluidic probe can be used to treat cells with single-cell precision. Finally, we demonstrated targeted delivery in tissue (mouse retinal explant) ex vivo. We expect that this method can enable nanomaterial-assisted intracellular delivery applications in soft tissue (e.g. brain, retina) of small animals.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".