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Record W2403913513 · doi:10.1177/2055217316649981

Erythrocytes in multiple sclerosis – forgotten contributors to the pathophysiology?

2016· review· en· W2403913513 on OpenAlexfundno aff
Kira Groen, Vicki E. Maltby, Katherine Sanders, Rodney J. Scott, Lotti Tajouri, Jeannette Lechner‐Scott

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2016
Typereview
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsnot available
FundersTrish Multiple Sclerosis Research FoundationHunter Medical Research InstituteMultiple Sclerosis AustraliaCanadian Institutes of Health ResearchBond University
KeywordsMultiple sclerosisDiseaseAutoimmunityPathologicalMyelinGenetic predispositionImmune systemImmunologyPathophysiologyExperimental autoimmune encephalomyelitisAutoimmune diseaseMedicineNeuroscienceCentral nervous systemPathologyBiology

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is an autoimmune disease characterised by lymphocytic infiltration of the central nervous system and subsequent destruction of myelin and axons. On the background of a genetic predisposition to autoimmunity, environmental triggers are assumed to initiate the disease. The majority of MS research has focused on the pathological involvement of lymphocytes and other immune cells, yet a paucity of attention has been given to erythrocytes, which may play an important role in MS pathology. The following review briefly summarises how erythrocytes may contribute to MS pathology through impaired antioxidant capacity and altered haemorheological features. The effect of disease-modifying therapies on erythrocytes is also reviewed. It may be important to further investigate erythrocytes in MS, as this could broaden the understanding of the pathological mechanisms of the disease, as well as potentially lead to the discovery of novel and innovative targets for future therapies.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.210
GPT teacher head0.378
Teacher spread0.169 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2016
Admission routes1
Has abstractyes

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