MétaCan
Menu
Back to cohort
Record W4297495987 · doi:10.5772/intechopen.106966

Advances in Biomaterials for Corneal Regeneration

2022· book-chapter· en· W4297495987 on OpenAlexafffund
Kamal Malhotra, May Griffith

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéVetenskapsrådetStem Cell Network
KeywordsRegeneration (biology)Economic shortageCorneal transplantationMedicineCorneaCorneal diseaseBlindnessTransplantationCorneal DiseasesOphthalmologyExtracellular vesicleMicrovesiclesSurgeryOptometryBiologyCell biologymicroRNA

Abstract

fetched live from OpenAlex

The human cornea acts as a protective covering for the eye and plays an important role in light transmission into the eye for vision. Corneal defects due to trauma, infection, or disease can have detrimental effects on the vision, and severe cases lead to vision loss. Twenty-three million people are estimated to be affected by corneal blindness worldwide. Treatment involves corneal transplantation surgery, but there is a severe shortage of donor corneas worldwide. Furthermore, patients with severe pathologies risk rejecting conventional corneal transplantation, thus leaving them untreated. Therefore, there is an urgent need to develop new therapies to replace traditional corneal transplant surgery. This review focuses on recent potential biomaterials development for corneal regeneration and repair. It includes cell-based therapies, cell-free regeneration-inducing biomaterials, and injectable or in-situ gelation-based biomaterials for patients with a high risk of graft failure. It also consists of the emerging role of exosomes and extracellular vesicles in corneal infections and regeneration.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.967
Threshold uncertainty score0.999

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.302
Teacher spread0.264 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueIntechOpen eBooksSame topicCorneal Surgery and TreatmentsFrench-language works237,207