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Record W2900090586 · doi:10.14740/jnr.v0i0.490

Is Epstein-Barr Virus a Risk Factor for Multiple Sclerosis?

2018· article· en· W2900090586 on OpenAlexvenueno aff
Aktham Ismail Alemam, Mostafa Saleh Maleek

Bibliographic record

VenueJournal of Neurology Research · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple sclerosisSerologyImmunologyAntigenAntibodyPopulationEpstein–Barr virusEpstein–Barr virus infectionVirusInternal medicineExpanded Disability Status ScaleRisk factor

Abstract

fetched live from OpenAlex

Background: The immune system is involved in the development of multiple sclerosis (MS) in genetically predisposed persons who are exposed to certain environmental stimuli that may include Epstein-Barr virus (EBV). The aim of the present study is to evaluate if EBV is an environmental risk factor in MS patients in Egyptian population. Methods: This is a prospective comparative study of 41 patients (18 - 50 years) including 25 females and 16 males versus 41 age-gender matched healthy controls tested for different EBV antibodies through serological examination using ELISA test after taking their informed consent. The data analysis was performed using Graphpad Prism 6.0 software. Results: Thirty-one patients were of relapsing-remitting MS (RRMS), eight cases were clinically isolated syndromes (CIS) and two cases were primary progressive MS (PPMS). Expanded disability status scale (EDSS) score of patients ranged from 0 to 8. Anti-EBV-viral capsid antigen (VCA)-IgG, anti-EBV-early antigen (EA)-IgG and anti-EBV-EBV nuclear antigen 1 (EBNA1)-IgG antibodies showed no statistically significant difference between MS and control groups with P values of 0.083, 0.517 and 0.833, respectively. No significant statistical correlation was found between level of EBV antibodies and MS clinical type, relapse or previous viral infection. Conclusion: According to our study, EBV infection might not be an independent factor in the development of MS in Eyptian population. J Neurol Res. 2018;8(3):19-25 doi: https://doi.org/10.14740/jnr490w

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.301
GPT teacher head0.441
Teacher spread0.140 · 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 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
Published2018
Admission routes1
Has abstractyes

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