Femtosecond laser‐induced cell‐cell surgical attachment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Laser-induced cell-cell surgical attachment using femtosecond laser pulses is reported. STUDY DESIGN/MATERIALS AND METHODS: We have demonstrated the ability to attach single cells using sub-10 femtosecond laser pulses, with 800 nm central wavelength delivered from a Ti:Sapphire laser. To check that the cells did not go through a cell-fusion process, a fluorescent dye Calcein AM was used to verify that the fluorescent dye did not migrate from a dyed cell to a non-dyed cell. The mechanical integrity of the attached joint was assessed using an optical tweezer. RESULTS: Attachment of cells was performed without the induction of cell-cell fusion, with attachment efficiency of 95%, and while preserving the cells' viability. Cell-cell attachment was achieved by delivery of one to two trains of femtosecond laser pulses lasting 15 ms each. CONCLUSIONS: Laser-induced ionization process led to an ultrafast reversible destabilization of the phospholipid layer of the cellular membrane. The inner cell membrane remained intact during the attachment procedure, and isolation of the cells' cytoplasm from the surrounding medium was obtained. A strong physical attachment between the cells was obtained due to the bonding of the membranes' ionized phospholipid molecules and the formation of a joint cellular membrane at the connection point. The cellular attachment technique, femtosecond laser-induced cell-cell surgical attachment, can potentially provide a platform for the creation of engineered tissue and cell cultures.
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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) | 0.002 | 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".