MétaCan
Menu
Back to cohort
Record W2888150420 · doi:10.1111/odi.12962

Ultrastructural analysis of collagen fibril diameter distribution in cleft lip

2018· article· en· W2888150420 on OpenAlexaff
Mohammad Faisal J. Khan, Julian Little, Tapas Chandra Nag, Peter Mossey, Luca Autelitano, Maria Costanza Meazzini, Ahmed Merajuddin, A Singh, Michele Rubini

Bibliographic record

VenueOral Diseases · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsUniversity of Ottawa
FundersUniversità degli Studi di Ferrara
KeywordsFibrilCollagen fibrilUltrastructureAnatomyTransmission electron microscopyOsmium tetroxideChemistryElectron microscopeMaterials scienceMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: A preliminary study to determine collagen fibril diameter (CF-ED) distribution on medial and lateral sides of cleft lip (CL). MATERIAL AND METHODS: Tissue samples from medial and lateral sides of CL were fixed in 2.5% glutaraldehyde and 1% osmium tetroxide and embedded in Araldite CY212 resin for transmission electron microscopy. The analysis of CF-ED was performed using the ImageJ program. To characterize the packaging of collagen fibrils (CFs) in the two tissues, we estimated the collagen number density (CF-ND) and fibril-area-fraction (FAF). Differences in measurements across the two sides were calculated using Wilcoxon signed-rank test. RESULTS: The CF-ED was statistically significantly (p < 0.001) smaller on the medial side (45.69 ± 7.89 nm) than on the lateral side (54.18 ± 7.62 nm). The medial side had a higher CF-ND and a higher percentage of FAF than the lateral side. CONCLUSION: Our finding of a smaller CF-ED and higher CF-ND and FAF for the medial side suggests possible differences in size and distribution of CFs between medial and lateral sides of CL. This finding provides knowledge toward underlying tissue biomechanics that may help reconstruction of perioral tissue scaffolds, ultimately resulting in better treatment of patients with oral clefts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.278

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 head0.013
GPT teacher head0.298
Teacher spread0.286 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueOral DiseasesSame topicCleft Lip and Palate ResearchFrench-language works237,207