Modeling and dynamics of <i>Brucella</i> infection with macrophage apoptosis inhibition and immune recovery
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it