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Record W2772013801 · doi:10.1111/joa.12744

Towards understanding the dose and timing effect of hydrocortisone treatment on the scleral ossicle system within the chicken eye

2017· article· en· W2772013801 on OpenAlexafffund
Christine Hammer, Tamara A. Franz‐Odendaal

Bibliographic record

VenueJournal of Anatomy · 2017
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsMount Saint Vincent UniversitySaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOssicleMedicineComputer scienceHydrocortisoneAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Previous work, almost four decades ago, showed that hydrocortisone (HC) treatment reduces the number of skeletogenic condensations that give rise to the scleral ossicles in the chicken eye. The scleral ossicles are a ring of overlapping intramembranous bones, the sclerotic ring, and are present in most reptiles, including birds. The scleral condensations that give rise to the scleral ossicles are induced by a series of overlying thickenings (or papillae) of the conjunctival epithelium. Here, we further explore the effects of altering the dosage and timing of HC treatment on the morphology and number of skeletogenic condensations and conjunctival papillae. We show that high doses can completely obliterate the entire sclerotic ring. Significantly, the reduction in papillae number we observed was less extreme than that of the scleral condensations, indicating that additional factors contribute to the observed skeletogenic condensation loss. Via immunohistochemical analyses, we show that HC treatment alters the spatial expression pattern of several extracellular matrix components (tenascin-C, decorin and procollagen I) and also alters the vasculature network within the sclera. This research provides important insights into understanding the role of the scleral tissue components in ossicle development within the vertebrate eye.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.320
Teacher spread0.270 · 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 designObservational
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

Citations8
Published2017
Admission routes2
Has abstractyes

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