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Record W4391301646 · doi:10.1101/2024.01.27.575831

Evaluation of Autophagy in conjunctival fibroblasts

2024· preprint· en· W4391301646 on OpenAlexaff
Parvaneh Mehrbod, Paola Brun, Umberto Rosani, Andrea Leonardi, Saeid Ghavami

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyChildren's Hospital Research Institute of ManitobaCancerCare ManitobaResearch Manitoba
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsAutophagyVernal keratoconjunctivitisImmunocytochemistryImmune systemImmunologyPathophysiologyBiologyCell biologyMedicineApoptosisPathologyGenetics

Abstract

fetched live from OpenAlex

Abstract Vernal keratoconjunctivitis (VKC) is a serious eye allergy characterized by poorly understood pathogenic mechanisms and a lack of effective treatments. Autophagy, a process involved in both triggering and suppressing immune and inflammatory responses, plays a role in VKC’s pathophysiology. Understanding autophagy’s involvement in VKC could lead to new treatment possibilities, such as utilizing specific topical substances to induce or inhibit autophagy and prevent severe complications of this eye condition. In our current protocol, we present a robust methodology established in our laboratory for studying autophagy in primary conjunctival fibroblasts. We assess autophagy through techniques like immunocytochemistry, immunoblotting, and qPCR.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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