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Record W4372348743 · doi:10.1002/adsr.202300015

In Situ Non‐Invasive Imaging of Neutrophil Myeloperoxidase and Skin Reactive Oxygen Species in Experimental Murine Atopic Dermatitis

2023· article· en· W4372348743 on OpenAlexafffund
Samuel Babity, Shihao Pei, Julia Galimi, Vanessa L.S. LaPointe, Davide Brambilla, Frédéric Couture

Bibliographic record

VenueAdvanced Sensor Research · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsUniversité LavalCentre intégré de santé et de services sociaux de Chaudière-AppalachesCégep de LévisOncolytics Biotech (Canada)Université de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMyeloperoxidaseReactive oxygen speciesAtopic dermatitisImmunologyPathologyChemistryBiologyInflammationMedicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract Neutrophils play a key role in the innate immune inflammatory response, notably through the release of the myeloperoxidase enzyme from azurophilic granules, locally generating reactive oxygen species. Although short‐lived, these reactive oxygen species are directly involved in local tissue damage in response to microbial intrusion. Neutrophil‐derived myeloperoxidase has been reported as an important factor in the elicitation of atopic dermatitis and is considered a potential target and biomarker. This study describes the use of in situ imaging techniques comprising both chemiluminescent resonance energy‐transfer and ratiometric fluorescent microtattoos to locally and non‐invasively image myeloperoxidase activity and skin reactive oxygen species in a murine model of calcipotriol‐induced atopic dermatitis. Using neutrophil depletion to assess granulocyte contribution to the observed imaging signals, the non‐invasive longitudinal data are found to correlate with endpoint biochemical activity assays for both myeloperoxidase and reactive oxygen species by‐products, as well as with immunohistochemical analysis.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.032
GPT teacher head0.358
Teacher spread0.326 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

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