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Mimicking the LOX-Related Autosomal Recessive Congenital Ichthyosis Skin Disease Using a CRISPR-Cas9 System and Unravelling 12S-LOX Function in the Skin

2025· article· en· W4414123292 on OpenAlexafffund
Carolyne Simard‐Bisson, Sébastien Larochelle, Véronique Moulin, Bernard Fruteau de Laclos

Bibliographic record

VenueDermatopathology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersFonds de Recherche du Québec - Santé
KeywordsEpidermis (zoology)Congenital ichthyosisIchthyosisHuman skinDiseaseAtopic dermatitisPhenotype

Abstract

fetched live from OpenAlex

(SC) formation in the human epidermis requires lipid processing. Lipoxygenases (LOXs) such as 12R-Lipoxygenase (12R-LOX) and Epidermis-type lipoxygenase 3 (eLOX-3) contribute to this process. Mutations in their genes cause Autosomal Recessive Congenital Ichthyosis (ARCI) in patients. On the other hand, 12S-lipoxygenase (12S-LOX) is expressed in the human epidermis, but its role still remains to be clarified. The involvement of eLOX-3, 12R, and 12S-LOX in conditions or processes such as skin photodamage, wound healing, psoriasis, and atopic dermatitis is suggested but still remains unclear. In order to eventually gain a better understanding of the role of these LOXs in such processes, models of Tissue-Engineered Skins (TESs) with an impaired expression for the native form of either eLOX-3, 12R-LOX, or 12S-LOX were produced using CRISPR-Cas9(D10A) technology. All three models showed impaired keratinocyte differentiation and changes in the prevalence or the size of lipid droplets within the most superficial layers, thus reproducing features observed in ARCI and supporting a role for 12S-LOX in SC formation. Since eLOX-3 and 12R-LOX depleted TES's reproduced features observed in ARCI, such models can be considered as reliable tools for the functional studies of these LOXs in the human epidermis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.477

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 routes2
Has abstractyes

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