Targeting immunometabolism in host defence against <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
In the face of ineffective vaccines, increasing antibiotic resistance and the decline in new antibacterial drugs in the pipeline, tuberculosis (TB) still remains pandemic. Exposure to Mycobacterium tuberculosis (Mtb), which causes TB, results in either direct elimination of the pathogen, most likely by the innate immune system, or infection and containment that requires both innate and adaptive immunity to form the granuloma. Host defence strategies against infectious diseases are comprised of both host resistance, which is the ability of the host to prevent invasion or to eliminate the pathogen, and disease tolerance, which is defined by limiting the collateral tissue damage. In this review, we aim to examine the metabolic demands of the immune cells involved in both host resistance and disease tolerance, chiefly the macrophage and T-lymphocyte. We will further discuss how baseline metabolic heterogeneity and inflammation-driven metabolic reprogramming during infection are linked to their key immune functions containing mycobacterial growth and instructing protective immunity. Targeting key players in immune cellular metabolism may provide a novel opportunity for treatments at different stages of TB disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".