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Record W2587795232 · doi:10.1089/jir.2016.0117

Interferon-Stimulated Gene Expression as a Preferred Biomarker for Disease Activity in Aicardi–Goutières Syndrome

2017· article· en· W2587795232 on OpenAlexaff
Ben X. Wang, Stephanie A. Grover, Pekka Kannus, Grace Yoon, Ronald M. Laxer, E. Ann Yeh, Eleanor N. Fish

Bibliographic record

VenueJournal of Interferon & Cytokine Research · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsISG15BiomarkerGeneMutationErythrocyte sedimentation rateInterferonMedicineImmunologyDiseaseBiologyPathologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Aicardi-Goutières syndrome (AGS) is an early-onset, genetic disease characterized by recurrent fever, multifocal lesions of the brain, and systemic autoimmunity. We report on 3 AGS patients, 2 siblings with an RNASEH2A gene mutation and 1 patient with a SAMHD1 gene mutation. Serial analysis of peripheral blood from all 3 AGS patients showed consistently elevated expression of the interferon-stimulated genes (ISGs): ISG15, RSAD2, and IFI27, not observed in unaffected family members. Enumeration of circulating white blood cells and platelets and examination of C-reactive protein showed no significant deviation from the normal range for Patient 2 with the RNASEH2A mutation and Patient 3 with the SAMHD1 mutation, even when Patient 2 had magnetic resonance imaging abnormalities and ongoing febrile episodes. Erythrocyte sedimentation rates fluctuated within the normal range for Patient 2, with some elevation, yet, were in the normal range during the second febrile episode when there were accompanying neurological abnormalities. These preliminary data suggest that ISG expression may be a more specific indicator of disease activity in comparison to standard inflammatory markers.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.408
Teacher spread0.306 · 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 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

Citations15
Published2017
Admission routes1
Has abstractyes

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