NEUTROPHILS PLASTICITY DURING <i>Mycobacterium tuberculosis</i> INFECTION IS RELATED TO THE DISEASE PROGRESSION
Bibliographic record
Abstract
Abstract Tuberculosis is an infectious disease that affects one third of world population. The bacilli are spread by the illness bearer and can cause a serious pulmonary infection. Together with resident macrophages, neutrophils appear to be an important cell in the control of Mycobacterium tuberculosis infection. Recently, some studies are breaking paradigms about neutrophils, revealing that these cells can polarize to N1 and N2 profiles. These findings opened a different perspective in the study of Mtb infection and progress to active disease. Thus, the aim of this study is to determine how the polarization of neutrophils in N1/N2 profiles could influence the control of Mtb infection. For this purpose, neutrophils were isolated from peripheral blood of ten healthy volunteers. These cells were stimulated for one hour with GM-CSF and INF-γ for differentiation in the N1 profile or with IL-13 and TGF-β for N2. The polarized neutrophils were characterized by their morphology and production of reactive oxygen species. After the differentiation neutrophils were infected with Mtb and were evaluated the formation of NET and cytokine production. As results, the caracterization of polarized neutrophils revealed a different nuclear morphology and metabolic activity. When neutrophils are challenged with Mtb the N1 group shows an increase of IL-8, IL-1-β, INF-γ and the formation of NETs. By contrast N2 profile reveals a decline of these inflammatory cytokines and an increase of IL-4 and TGF-β. Further, in this condition is no longer possible to observe the formation of NETs. This result supports the idea of neutrophils plasticity and helps to understand better how the neutrophils may have different actions against Mtb.
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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 | 0.000 |
| 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 | 0.001 |
| 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".