Quantitative Analysis of Human Endogenous Retrovirus-W <i>env</i> in Neuroinflammatory Diseases
Bibliographic record
Abstract
Although human endogenous retroviruses (HERVs) constitute 8% of the human genome, their role(s) in health and disease remain uncertain. Nonetheless, increased HERV gene activity has been reported in neuroinflammatory diseases such as multiple sclerosis (MS). The human endogenous retrovirus (HERV)-W7q envelope gene encodes a glycosylated envelope protein, syncytin-1, which is expressed in many tissues. Analysis of HERV envelopes (env) revealed a selectively increased abundance of syncytin-1 encoding RNA in brains from patients with MS (p<0.01) relative to non-MS patients. However, HERV env expression from blood-derived leukocytes did not differ between groups. A quantitative PCR-based assay for syncytin-1 RNA showed that median viral RNA levels were higher in brains of MS patients (5.0 log10 copies/microg RNA) relative to non-MS patients (4.6 log10 copies/microg RNA) (p<0.05). Median syncytin-1 DNA levels in MS brains (9.8 log10/microg DNA) were higher than non-MS brain tissue (7.9 log10/microg DNA) (p<0.001) without evidence of new integration events. In contrast, there were no differences in syncytin-1 RNA copy numbers between groups in both CSF (non-MS: 5.0 log10/ml versus MS: 3.8 log10/ml) and plasma (non-MS: 5.033 log10/ml versus MS: 2.9 log10/ml). These observations emphasize the selective induction of syncytin-1 in brain tissue of MS patients but also illustrate the complex dynamics of this retroelement in neuroinflammatory processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".