Characterization of femtosecond-laser pulse induced cell membrane nanosurgical attachment
Bibliographic record
Abstract
This article provides insight into the mechanism of femtosecond laser nanosurgical attachment of cells. We have demonstrated that during the attachment of two retinoblastoma cells using sub-10 femtosecond laser pulses, with 800 nm central wavelength, the phospholipid molecules of both cells hemifuse and form one shared phospholipid bilayer, at the attachment location. In order to verify the hypothesis that hemifusion takes place, transmission electron microscope images of the cell membranes of retinoblastoma cells were taken. It is shown that at the attachment interface, the two cell membranes coalesce and form one single membrane shared by both cells. Thus, further evidence is provided to support the hypothesis that laser-induced ionization process led to an ultrafast reversible destabilization of the phospholipid layer of the cellular membrane, which resulted in cross-linking of the phospholipid molecules in each membrane. This process of hemifusion occurs throughout the entire penetration depth of the femtosecond laser pulse train. Thus, the attachment between the cells takes place across a large surface area, which affirms our findings of strong physical attachment between the cells. The femtosecond laser pulse hemifusion technique can potentially provide a platform for precise molecular manipulation of cellular membranes. Manipulation of the cellular membrane is an important procedure that could aid in studying diseases such as cancer; where the expression level of plasma proteins on the cell membrane is altered.
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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".