Investigating the Role of Interleukin 17 in HIV and TB Infections
Bibliographic record
Abstract
HIV and TB are endemic in many regions around the world but disproportionately affect low-income countries. HIV/TB co-infections significantly worsen disease outcome with TB being the leading cause of death in people living with HIV (PLWH). Currently, the only vaccine for TB is the BCG vaccine which is not protective against adult pulmonary TB and not recommended for PLWH. Antiretroviral therapy (ART) can suppress viral loads and improve the life span in PLWH but is ultimately unable to eliminate the virus. Therefore, better therapeutic options are needed to improve outcomes for both HIV and TB infections. IL-17 is a proinflammatory cytokine characteristically produced by Th17 cells. Utilizing a humanized mouse model, we aimed to investigate the role of IL-17 and Th17 cells in HIV and TB. HIV infection preferentially depleted Th17 cells in the blood and tissues of humanized mice. In TB infection, significantly increased numbers of Th17 cells were observed at 2 weeks post infection (p.i) in the lungs of M.tb infected mice compared to control and 4 weeks p.i. IL-17 levels trended higher in the lungs at 4 weeks p.i compared to control and 2 weeks. IL-17 depletion in acute TB infection showed slight decrease in bacterial load in the lungs of the treated mice compared to the control. This could suggest a potential pathological role of IL-17 in TB infection where IL-17 promotes M.tb replication. When exogenous IL-17 was administered to the lungs prior to TB infection, significantly higher numbers of CD4+ and CD8+ T cells in the lungs of treated mice were seen compared to the control but this did not affect bacterial load. Our current experiment administers exogenous IL-17 to HIV infected mice to assess how it would affect disease progression. Further investigation is needed to explore how IL-17 affects HIV and TB disease pathogenesis.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".