Differential efficacy of antiretroviral drugs in HIV-1 infected human microglia
Bibliographic record
Abstract
Background: Immune activation in HIV-1-infected patients persists under suppressive antiretroviral treatment and may fuel comorbidities such as atherothrombosis, osteoporosis, metabolic syndrome, neurocognitive disorders and liver steatosis.Our hypothesis was that treated patients present with distinct profiles of immune activation and that each profile is linked to a specific comorbidity.We first explored the profile of immune activation that was associated with the presence of a metabolic syndrome.Methods: We measured by flow cytometry and ELISA the level of activation of CD4+ and CD8+ T cells, B cells, monocytes, NK cells and endothelial cells as well as of inflammation with a total of 68 soluble and cell surface markers in 120 virologically suppressed individuals and 20 healthy donors (aged ≥45 years).We used a hierarchical clustering analysis to classify the patients according to different markers of immune activation, and logistic regression with odds ratios (OR) and 95% confidence intervals (CI) to measure the association between immune activation profiles and metabolic syndrome.Results: We observed evidence of inflammation and immune activation in all the cell subpopulations analysed.Patients were clustered in five distinct immune activation profiles.Each one of these five profiles could be characterized by a marker of CD8+ T cell, NK cell, monocyte, endothelial cell activation or of inflammation, respectively, and could be distinguished between the other profiles by a signature of eight biomarkers.Only one of these immune profiles was significantly associated with marks of metabolic syndrome: hypertriglyceridemia (OR 4.18, 95% CI 1.08-16.19,P=0.038), hyperinsulinemia (OR 12.17, 95% CI 1.79-82.86,P=0.011) and lipodystrophy (OR 4.87, 95% CI 1.36-17.39,P=0.015).Conclusion: We have uncovered an immune signature that might be useful for the prevention and early diagnosis of metabolic syndrome in HIV-infected patients.A better knowledge of the links between immune activation profiles and their consequences might highlight biomarkers predictive of comorbidities, as well as new therapeutic targets in HIV-induced immune activation or other situations of chronic hyperactivity of the immune system including aging.
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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 | 0.001 | 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".