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Record W4416356301 · doi:10.1097/ico.0000000000004037

Exploring the Gut-Eye Axis: Microbial Dysbiosis in Vernal Keratoconjunctivitis

2025· article· en· W4416356301 on OpenAlexaff
Alireza Peyman‎, Mohsen Pourazizi, Mohammad Kazemi, Fatemeh Ghorbani, Faeze Ahmadi Beni, Pegah Noorshargh, Sarah Ghorbani

Bibliographic record

VenueCornea · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDysbiosisVernal keratoconjunctivitisProbioticMicrobiomeGut flora

Abstract

fetched live from OpenAlex

PURPOSE: To investigates the gut microbiota in patients with vernal keratoconjunctivitis (VKC) compared with normal subjects. METHOD: A case-control study was conducted involving 32 patients with VKC and 16 age- and sex-matched healthy controls. Fecal samples were collected and analyzed using quantitative PCR to measure the abundance of 5 bacterial groups: Prevotella, Firmicutes, Faecalibacterium, Bifidobacterium, and Bacteroides. In addition, clinical severity scores for VKC symptoms and signs were recorded and analyzed. RESULTS: Patients with VKC exhibited significant microbial dysbiosis compared with controls, including an upregulation of Prevotella (mean fold change 21.579; P <0.001) and downregulation of Firmicutes, Faecalibacterium, and Bifidobacterium (P < 0.01 for all). No significant correlations were observed between microbial levels and clinical severity scores. CONCLUSIONS: The study identifies significant gut microbial imbalances in patients with VKC, suggesting that dysbiosis may play a role in the disease. Microbiota-targeted interventions, such as probiotics and dietary modifications, may offer novel strategies for managing VKC.

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 score0.113
Threshold uncertainty score0.346

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 head0.039
GPT teacher head0.262
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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