Spatiotemporal dynamics of immune responses to viral infection and re-infection
Bibliographic record
Abstract
To test the hypothesis that the chemotactic movement of immune cells is pivotal in controlling within-host viral infection and re-infection, we formulate a chemotaxis model with multiple chemokines and spatial heterogeneity to investigate spatiotemporal dynamics of immune responses. The system admits globally bounded classical solutions in one-dimensional space, and the global classical solvability is derived in a high-dimensional space. We discuss the existence and local stability of four equilibria in the homogeneous case. By constructing Lyapunov functionals, we prove the global stability and the convergence rates of equilibria. Numerical simulations verify the theoretical results and provide additional insights. With stronger chemotaxis, the fluctuation of viral load in the early stage of viral infection is significantly reduced and enters a stable state faster, which indicates that chemotaxis can inhibit viral infection. During re-infection, the chemotactic movement of memory CTLs is more important than that of effector CTLs, and vice versa in initial infection. Furthermore, spatial heterogeneity, due to different patients or organs, should inevitably be considered to guarantee the success of clinical treatments.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".