HIV infection dynamics with broadly neutralizing antibodies and CTL immune response
Bibliographic record
Abstract
HIV infection remains a significant global public health concern. Although current antiretroviral therapies and broadly neutralizing antibodies (bNAbs) can decrease plasma viral load, they are unable to completely eradicate the virus. Alongside these treatments, the cytotoxic T lymphocyte (CTL) immune response also contributes to viral control. However, the impact of antiretroviral drugs and bNAb therapies on HIV dynamics in the presence of CTL immune responses remains uncertain. In this paper, we develop and analyze a mathematical model that incorporates CTL immune response, bNAb, and drug therapies. We demonstrate that the basic reproduction number $ \mathcal{R}_0 $ and the CTL immune response reproduction number $ \mathcal{R}_c $ determine the existence and stability of the equilibria. Numerical investigation reveals that both antiretroviral drugs and bNAb therapies can reduce the viral load to below the detection limit. However, bNAb therapy can delay the time to viral rebound compared with antiretroviral therapy alone. Furthermore, bNAbs have a more significant impact on viral reduction than the CTL immune response. The CTL immune response increases the number of uninfected cells and reduces the number of infected cells and viral load. Analysis of the relative contributions shows that bNAb therapy can enhance the CTL immune response, similar to the direct stimulation of antigens. These findings suggest that bNAb therapy, combined with CTL immune response, plays a critical role in HIV control and has important implications for understanding HIV pathogenesis and developing more effective treatment strategies to manage or even eliminate the disease.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".