The influence of North American Aboriginal ethnicity on pro‐inflammatory and anti‐inflammatory cytokine responses to IFN‐alpha
Bibliographic record
Abstract
North American Aboriginals have an enhanced propensity to clear HCV infection. Interferon (IFN)-alpha is a critical agent in the clearance of hepatitis C virus (HCV) and other viruses; therefore the influence of Aboriginal ethnicity on IFN-alpha responses was investigated in healthy Caucasian population control and Aboriginal cohorts. Cohort peripheral blood mononuclear cells produced similar levels of IFN-alpha upon culture with reovirus, an innocuous virus capable of triggering IFN-alpha synthesis. In addition, similar IFN-gamma synthesis was observed in the presence IFN-alpha or reovirus. In contrast, Caucasian supernatants exhibited greater IL-10 levels (P<0.005), contributing to the overall cytokine balance as assessed by IFN-gamma/IL-10 ratios being consistently elevated in the Aboriginal cohort. The potential of HCV proteins to alter IFN-alpha cytokine induction was also investigated. Although there was some indication that HCV proteins might increase IFN-alpha induced IL-10 synthesis in Caucasians and conversely, IFN-gamma synthesis in Aboriginals, the addition of HCV proteins did not influence IFN-gamma/IL-10 ratios. Finally, signal transducer and activator of transcription (STAT) 3 nuclear translocation was examined by western blot because it is a required intermediate in IFN-alpha induced IL-10 synthesis. Supporting the differential IL-10 production, IFN-alpha and core synergistically enhanced STAT3 nuclear translocation in Caucasian (P<0.05); whereas, nuclear translocation of STAT3 remained unchanged in Aboriginal cells. Taken together, these findings suggest that ethnicity may influence certain responses to IFN-alpha, possibly even in the presence of viral agents. These differences could impact early immune events allowing for enhanced viral clearance in Aboriginal populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".