Destruction of polymer growth substrates for cell cultures in two‐photon microscopy
Bibliographic record
Abstract
The choice of the growth substrate for cell cultures used in fluorescence microscopy is guided by several factors including the type of cells studied and the type of microscopy used. Usually, cells can be cultured on either polymer or glass substrates. One type of polymer, termed Aclar, presents several attractive features: the adhesive properties are better than those of glass, the optical properties are comparable to those of glass, it is biochemically inert, unbreakable, flexible and has a high surface tension, convenient for seeding cells on the cover slip. However, here we show that when imaging with two-photon microscopy, which is based on a femtosecond pulsed laser source, local damage of the Aclar substrate occurs, starting at an average intensity of 10(5) W cm(-2) at the focal point and for exposure times insufficient to cause cell damage. This leads to the appearance of gas bubbles on cultures plated on Aclar cover slips, which perturb the imaging. By contrast, this phenomenon does not occur on borosilicate cover slips, probably because of their different physical (thermal conductivity, absorbance, melting point) and material homogeneity properties. Thus, for cell culture applications using pulsed lasers with high intensities, the use of glass is preferable to Aclar. The results also reveal that substrates can be more susceptible to thermal damage than the cells themselves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".