Cell Membrane Repair Assay Using a Two-photon Laser Microscope
Bibliographic record
Abstract
Numerous pathophysiological insults can cause damage to cell membranes and, when coupled with innate defects in cell membrane repair or integrity, can result in disease. Understanding the underlying molecular mechanisms surrounding cell membrane repair is, therefore, an important objective to the development of novel therapeutic strategies for diseases associated with dysfunctional cell membrane dynamics. Many in vitro and in vivo studies aimed at understanding cell membrane resealing in various disease contexts utilize two-photon laser ablation as a standard for determining functional outcomes following experimental treatments. In this assay, cell membranes are subjected to wounding with a two-photon laser, which causes the cell membrane to rupture and fluorescent dye to infiltrate the cell. The intensity of fluorescence within the cell can then be monitored to quantify the cell's ability to reseal itself. There are several alternative methods for assessing cell membrane response to injury, as well as great variation in the two-photon laser wounding approach itself, therefore, a single, unified model of cell wounding would beneficially serve to decrease the variation between these methodologies. In this article, we outline a simple two-photon laser wounding protocol for assessing cell membrane repair in vitro in both healthy and dysferlinopathy patient fibroblast cells transfected with or without a full-length dysferlin plasmid.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".