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Record W4200341019 · doi:10.3233/jcb-210039

Establishment of optimal culture media in corneal epithelial wound healing models

2021· article· en· W4200341019 on OpenAlexaff
Emma V. Dare, Chung Ki Fung, David J. McCanna, Lakshman N. Subbaraman, Lyndon Jones

Bibliographic record

VenueJournal of Cellular Biotechnology · 2021
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWound healingIn vivoIn vitroKeratinocyteAndrologyCorneal epitheliumViability assayCell cultureEpitheliumChemistryMedicineCell biologyBiologyPathologySurgeryBiochemistryBiotechnology

Abstract

fetched live from OpenAlex

BACKGROUND: Wound healing needs to occur after injury to prevent vision loss. Models of wound healing need to be optimized to assure treatments for corneal wounds can be developed in vitro prior to investigating with in vivo studies. OBJECTIVE: The purpose of this study was to establish the optimum media to use as a control solution in wound healing models. METHODS: Immortalized human corneal epithelial cells were cultured in different growth media using a scratch and exclusion zone model. The effect of normoxic and hypoxic conditions on tight junctional integrity and metabolic activity of cells grown in different growth medium were also investigated. RESULTS: Wound healing with DMEMF12 media was significantly faster than both Keratinocyte serum-free media (p < 0.05) and EpiLife (p < 0.05) after 10 hours recovery under normoxic or hypoxic conditions using the scratch model and 9 days after wounding using the exclusion zone technique (p < 0.05). Using the culture media DMEMF12, cells stained for abundant ZO-1, Cx43 and had a high metabolic activity indicating significant epithelial barrier formation, gap junction formation and high cell viability. CONCLUSIONS: DMEMF12 led to superior wound healing under hypoxic and normoxic conditions and in two different wound healing models.

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 score0.042
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.253
Teacher spread0.232 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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