Hemifusion of cells using femtosecond laser pulses
Bibliographic record
Abstract
Attachment of single cells via hemifusion of cellular membranes using femtosecond laser pulses is reported in this manuscript. This is a method to attach single cells using sub-10 femtosecond laser pulses, with 800 nm central wavelength delivered from a Ti:Sapphire laser is described. A fluorescent dye, Calcein AM, was used to verify that the cell’s cytoplasm did not migrate from a dyed cell to a non-dyed cell, in order to ascertain that the cells did not go through cell-fusion process. An optical tweezer was used in order to assess the mechanical integrity of the attached joint membranes. Hemifusion of cellular membranes was successful without initiating full cell fusion. Attachment efficiency of 95% was achieved, while the cells’ viability was preserved. The attachment was performed via the delivery of one to two trains of sub-10 femtosecond laser pulses lasting 15 milliseconds each. An ultrafast reversible destabilization of the phospholipid molecules in the cellular membranes was induced due to a laser-induced ionization process. The inner phospholipid cell membrane remained intact during the attachment procedure, and cells’ cytoplasm remained isolated from the surrounding medium. The unbounded inner phospholipid molecules bonded to the nearest free phospholipid molecule, forming a joint cellular membrane at the connection point. The cellular membrane hemifusion technique 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 | 0.001 |
| 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".