Editorial: Cellular immune response and escape mechanisms of intracellular parasites
Bibliographic record
Abstract
Infectious parasitic diseases are among the most prevalent diseases in the world. Some of them are among the biggest causes of death in the world, presenting relevant negative consequences for global public health. The immune response is the first form of protection against these diseases and its effectiveness and magnitude can be influenced by numerous factors, such as immunocompetence, in the case of hosts, and virulence, in the case of parasites.Recently, innate immune pathways have gained more attention in studies focusing on the comprehension of immunopathogenesis of infectious diseases and there is an increased consensus that both innate and adaptive pathways act together to maintain homeostasis. Although innate immune pathways contribute to the initial outcomes of infection, they might not be sufficient for host protection from infection.that mice deficient in the expression of a transcription factor that plays a critical role in regulating the function of lymphocytes (Blimp-1), produced higher expression of iNOS than wild-type mice; in contrast, the deficient mice presented higher parasitemia and mortality than wildtype mice. The[IC1] [UdW2] authors have also demonstrated that the deficiency of Blimp-1 in T cells from the mice, did not impair an effective Th1 response during T. cruzi infection, although the mice presented a failure in the activation of CD8+ T cell responses, which are pivotal for restricting parasite growth. In addition, the authors discussed that Blimp-1 prevents the recruitment and activation of inflammatory monocytes and the subsequent release of inflammatory mediators such as TNF and NO, which cause liver damage and dysfunction.Taken together, these data reinforce the hypothesis that host pathogen interaction modulates several pathways and the effective control of the infection depends on a balance between pro-and anti-inflammatory molecules. Excessive inflammatory stimulus may contribute to control infection, but, at the same time, may be deleterious for host cells.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".