Immunity Switches and Macrophage Manipulations: Trauma, Ovulation, and Depression as Latent Tuberculosis Reactivation Risks
Bibliographic record
Abstract
ABSTRACT Inflammation is the immune system's natural response to initial tuberculosis infection. Tuberculosis bacteria have gained adaptations to manipulate the inflammatory process, sometimes settling into latency and containment in granulomas, ensuring their survival. Grounded in an evolutionary framework, this hypothesis‐driven narrative synthesis centers upon immune‐related switches, macrophage manipulations, and the critical roles of vascular endothelial growth factor A (VEGFA) in the body, exploring how this pro‐inflammatory mitogen expressed by M1 macrophages frames risks for latent tuberculosis reactivation. The review focuses on trauma, ovulation, and depression, three case studies of pro‐inflammatory switches creating risks for reactivation because of M1 macrophage polarization, the up‐regulation of VEGFA expression, and angiogenesis (the sprouting of new blood vessels). A biological rationale is extended for why skeletal tuberculosis is so often connected with onsets in childhood, why adolescent and reproductive age females may experience heightened risks for latent tuberculosis reactivation relative to males, and why there is a potential for latent tuberculosis reactivation following onsets of depression. The immunity switches and reactivation risks of trauma, ovulation, and depression are problematic, particularly in contexts of endemic tuberculosis if large numbers of people are routinely latently infected, and among individuals with natural “high producer” VEGFA phenotypes, or those with strong type 1/M1/T H 1 or type 3/M1/T H 17 pro‐inflammatory switch tendencies, and in infections with tuberculosis bacteria possessing macrophage‐ and granuloma‐manipulating adaptations (virulence factors). Arguably, any disease or physiological state engaging pro‐inflammatory switches (common and sometimes chronic in the modern population) and M1 macrophage polarizations, and any drug treatments or therapeutics intending to alter VEGFA expression should be considered for latent tuberculosis reactivation risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".