Cellular and Humoral Immune Responses During Tuberculosis Infection: Useful Knowledge in the Era of Biological Agents
Bibliographic record
Abstract
In this review, recent insights into innate and adaptive cellular and humoral immune response to Mycobacterium tuberculosis (Mtb) are discussed and the role of specific cytokines such as tumor necrosis factor-α (TNF-α) is highlighted. According to recent findings, the immune system plays a key role in avoiding mycobacteria dissemination. The importance of different cell types (macrophages, dendritic cells, interferon-γ-producing T cells) as well as the production of proinflammatory cytokines such as interleukin 6 (IL-6), IL-12, and IL-23/IL-17 have been demonstrated. Alveolar macrophages are considered the first cells infected by Mtb during respiratory infection. Mtb proliferates within alveolar macrophages and dendritic cells and induces the release of cytokines such as TNF-α, IL-1, IL-6, and IL-12. Toll-like receptors-stimulated dendritic cells link innate and adaptive immunity by promoting polarization of effector T cells. The efficient induction of Th1 immunity is decisive in defense against Mtb. In fact, host effector immune response against Mtb is related to the presence of a Th1 response. The definition of the cellular and molecular mechanisms involved in the immune response to Mtb can be helpful in developing new preventive strategies to avoid infection relapse, particularly in patients treated with biological agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".