Understanding the role of CD8-cell response in HIV control through dynamical analysis
Bibliographic record
Abstract
In a world of raging epidemic spreads, HIV has a major impact on the health of the global public. Immune system cells play a major role in defending the body from the impact of the virus. HIV’s major effect is on CD4+ cells (or T cells) and CD8+ cells (or Z cells). The activation of CD8 cells has a major impact as it helps in fighting against this virus. Therefore, this paper focuses on the development of a novel mathematical model incorporating the immune cells(T and Z cells) and studies the dynamics of the model. The study of the dynamics of HIV-immune cells and the CD8 cell response can provide a rational selection of strategies for treatment and cure based on CD8 cells. A critical threshold (basic reproduction number) ρ0 is obtained along with the existence of disease-free equilibrium, and endemic equilibrium without and with the immune response for the model. Further, a Lyapunov function is constructed using the graph-theoretic approach to establish the global dynamics for endemic equilibrium(with and without immune response) point and matrix theoretic method for disease-free equilibrium. Local sensitivity analysis for ρ0 and E2 has also been carried out to recognize the sensitive parameters which may help in controlling the disease. The numerical discussion is carried out to validate our theoretical results. Sensitivity analysis has also been carried out for basic reproduction number and endemic equilibrium point with the immune response to recognize the sensitive parameters which may help in controlling the disease. Finally, the impact of the immune response of activated cells directly helping in suppressing the viral replication is shown by uncertainty analysis using PRCC. We were motivated to develop the novel model of HIV with immune response and study the dynamics of the model in the presence of CD8 cells as activated CD8-cells based immune response treatments can help control the disease at a mild stage itself due to its cytotoxic potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".