Pre-infection plasma cytokines and chemokines as predictors of HIV disease progression
Bibliographic record
Abstract
Previous studies have highlighted the role of pre-infection systemic inflammation on HIV acquisition risk, but the extent to which it predicts disease progression outcomes is less studied. Here we examined the relationship between pre-infection plasma cytokine expression and the rate of HIV disease progression in South African women who seroconverted during the CAPRISA 004 tenofovir gel trial. Bio-Plex 200 system was used to measure the expression of 47 cytokines/chemokines in 69 seroconvertors from the CAPRISA 004 trial. Cox proportional hazards regression analyses were used to measure associations between cytokine expression and CD4 decline prior to antiretroviral therapy initiation. Linear regression models were used to assess whether pre-infection cytokine expression were predictors of disease progression outcomes including peak and set-point viral load and CD4:CD8 ratio at less and greater than180 days post infection. Several cytokines were associated with increased peak HIV viral load (including IL-16, SCGFβ, MCP-3, IL-12p40, SCF, IFNα2 and IL-2). The strongest association with peak viral load was observed for SCGFβ, which was also inversely associated with lowest CD4:CD8 ratio < 180 days post infection and faster CD4 decline below 500 cells/µl (adjusted HR 4.537, 95% CI 1.475-13.954; p = 0.008) in multivariable analysis adjusting for age, study site, contraception, baseline HSV-2 status and trial arm allocation. Our results show that pre-infection systemic immune responses could play a role in HIV disease progression, especially in the early stages of infection.
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 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 | 0.003 |
| 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 | 0.001 |
| 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".