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Destruction of polymer growth substrates for cell cultures in two‐photon microscopy

2005· article· en· W2046616114 on OpenAlexafffund
C. THIBAUD, V. Koubassov, Paul De Koninck, S. L. Chin, Yves De Koninck

Bibliographic record

VenueJournal of Microscopy · 2005
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMaterials scienceMicroscopyBorosilicate glassOptical microscopePolymerFemtosecondSubstrate (aquarium)AdhesiveMicroscopeComposite materialLaserOpticsOptoelectronicsScanning electron microscope

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.275
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2005
Admission routes2
Has abstractyes

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