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Record W2168182874 · doi:10.4161/cam.36224

An introduction to the wound healing assay using live-cell microscopy

2014· article· en· W2168182874 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Adhesion & Migration · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsAlberta Bible CollegeUniversity of CalgaryUniversity Health Network
FundersUniversity of Calgary
KeywordsWound healingBiomedical engineeringStandardizationComputer scienceConsistency (knowledge bases)PopulationWorkflowSeedingMaterials scienceNanotechnologyArtificial intelligenceMedicineBiologySurgery

Abstract

fetched live from OpenAlex

The wound healing assay is used in a range of disciplines to study the coordinated movement of a cell population. In this technical review, we describe the workflow of the wound healing assay as monitored by optical microscopy. Although the assay is straightforward, a lack of standardization in its application makes it difficult to compare results and reproduce experiments among researchers. We recommend general guidelines for consistency, including: (1) sample preparation including the creation of the gap, (2) microscope equipment requirements, (3) image acquisition, and (4) the use of image analysis to measure the gap size and its rate of closure over time. We also describe parameters that are specific to the particular research question, such as seeding density and matrix coatings. All of these parameters must be carefully controlled within a given set of experiments in order to achieve accurate and reproducible results.

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.

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 score0.225
Threshold uncertainty score0.425

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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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