Immunologic non-response during HIV infection is characterized by systemic immune activation
Bibliographic record
Abstract
Abstract Introduction Despite effective treatment HIV infection is characterized by non-AIDS health burdens, linked to immune activation, gut damage and microbial translocation. Some HIV+ individuals achieve viral suppression but do not recover blood CD4 T cells; these immunologic non-responders (INRs) are at elevated disease risk. We hypothesized that INRs have higher immune activation and markers related to gut damage, as compared to HIV+ treatment responders (TRs). Methods Thirty-six men who have sex with men (MSM) were recruited through the Maple Leaf Medical Clinic in Toronto. INRs (n=15) were defined as having a CD4 T cell count <350/μl, despite >2 years of HIV suppression. TRs (n=15) had a nadir CD4 T cell count <350/μl, but restored blood CD4 T cell count >350/μl. HIV-negative MSM (n=6) were also recruited. T cell activation (%HLA-DR+CD38+) and other cellular markers were assessed by flow cytometry. Plasma markers of coagulation (D-Dimer), and microbial translocation-associated immune activation (sCD14) were quantified by immunosorbent assay. Results INRs had elevated levels of CD8 and CD4 T cell activation compared to TRs (p<0.05). INRs had a reduced CD4/CD8 ratio compared to TRs (p<0.001), and lower CD4 T cell count was associated with elevated CD8 T cell activation (p<0.05). There was a trend towards higher plasma D-dimer levels in INRs compared to TRs (p=0.12). Conclusion INR men had a reduced CD4/CD8 ratio, higher levels of systemic immune activation, and a trending elevation in markers of coagulation. These data highlight a need for characterization of tissue-specific gut immune function in the INR context. Ultimately gut-targeted clinical interventions may be useful to mitigate adverse health outcomes in INR individuals.
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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 | 0.000 |
| 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) | 0.003 | 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".