Low-Dose Acetylsalicylic Acid Reduces T Cell Immune Activation: Potential Implications for HIV Prevention
Bibliographic record
Abstract
Introduction Acetylsalicylic acid (ASA) is a well-known and safe anti-inflammatory. At low-dose, it is prescribed to prevent secondary cardiovascular events in those with pre-existing conditions and to prevent preeclampsia. Little is known about how low-dose ASA affects the immune response. In this study, we followed women to assess how ASA use modifies T cells immune phenotypes in the blood and at the genital tract. Methods HIV uninfected women from Kenya were enrolled in this study and followed for one month to assess baseline responses including systemic/mucosal baseline immune activation. Participants then received 81mg of ASA daily for 6 weeks to assess changes to T cell immune activation (systemic and mucosal) relative to baseline levels. Results The concentration of ASA measured in the blood was 58% higher than the level measured at the female genital tract. In the blood, the level of ASA was inversely correlated with the following: the proportion of Th17 expressing HLA-DR (p=0.04), the proportion of effector CD4+ T cells expressing CCR5 (p=0.03) and the proportion of CD8+Tc17 expressing CCR5 (p=0.04). At the genital tract, ASA use correlated with a decreased of activated CD4+T cells [CD4+CCR5+CD161+ (p=0.02) and CD4+CCR5+CD95+ (p=0.001)]. Conclusion This study shows that ASA use impacts the immune response in both the systemic and genital tract compartments. This could have major implications for the prevention of infectious diseases such as HIV, in which the virus targets activated T cells to establish an infection. This could inform guidelines on ASA use in women. Clinical Trial Registration ClinicalTrials.gov, identifier NCT02079077.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".