Modeling and dynamics of <i>Brucella</i> infection with macrophage apoptosis inhibition and immune recovery
Bibliographic record
Abstract
Brucella is a facultative intracellular bacterium being responsible for brucellosis, a zoonotic disease characterized by chronicity and frequent relapse. To identify the key factors governing the clearance or persistence of Brucella infection within the host, a mathematical model is developed that incorporates bacterial virulence, macrophage apoptosis, necrosis and immune recovery mechanisms. A deterministic system and its stochastic counterpart are derived to capture the infection dynamics under both deterministic and fluctuating immune responses. The deterministic system admits very rich dynamics and undergoes forward, backward, pitchfork, Hopf and codimension-2 Bogdanov–Takens bifurcations, which reflect the intricate transitions between infection clearance and persistence. The stochastic model has a unique global positive solution, exhibits persistence and possesses a unique ergodic stationary distribution, emphasizing the critical role of intrinsic immune noise. Numerical results indicate that the infection and apoptosis rates determine clearance thresholds, while immune recovery and necrosis regulate the severity and stability of infection. Furthermore, immune recovery of infected macrophages may amplify oscillatory dynamics, while stochastic perturbations can sustain persistent infection even when the basic reproduction number falls below unity. The key findings underscore the interplay between immune regulation and intracellular persistence, and offer insight into the mechanisms driving chronic Brucella infection.
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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) | 0.001 | 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 | 0.000 |
| 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".