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Record W2017796703 · doi:10.1089/aid.2006.0274

Comparative Expression of Human Endogenous Retrovirus-W Genes in Multiple Sclerosis

2007· article· en· W2017796703 on OpenAlexafffund
Joseph M. Antony, Yu Zhu, Maryam Izad, Kenneth G. Warren, Mohammed Vodjgani, François Mallet, Christopher Power

Bibliographic record

VenueAIDS Research and Human Retroviruses · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleCanada Research ChairsMultiple Sclerosis Society of Canada
KeywordsPathogenesisEndogenous retrovirusBiologyMultiple sclerosisRetrovirusGeneInnate immune systemVirologyImmune systemGeneticsImmunologyGenome

Abstract

fetched live from OpenAlex

Human endogenous retroviruses (HERVs) have been associated with multiple sclerosis (MS) pathogenesis. Several related HERV-W sequences have been implicated in disease occurrence and progression; the MS retrovirus (MSRV) is one such element whose envelope protein has been recently demonstrated to be involved in innate immune pathogenesis. To distinguish MSRV from other HERV-W sequences we analyzed the relative abundance of individual HERV-W env sequences by employing a real-time PCR approach using specific oligonucleotide primers and tissue samples from MS and non-MS patients. Our analyses reveal that ERVWE1 env-encoding DNA and RNA exhibited increased detection (p < 0.05) and expression (p < 0.01) in the brains of MS patients. Similarly, ERVWE1 env transcripts were inducible in glial cells (p < 0.05), while comparable changes in MSRV abundance were not observed. These results indicate that individual HERVs might have distinct roles in MS pathogenesis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.299
GPT teacher head0.355
Teacher spread0.056 · 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 source (direct Gemma or distilled Codex), 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

Citations67
Published2007
Admission routes2
Has abstractyes

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