Editorial: Emerging roles of extracellular vesicles in immunomodulation during host-pathogen interactions
Bibliographic record
Abstract
Emerging roles of extracellular vesicles in immunomodulation during host-pathogen interactionsExosomes or extracellular vesicles (EVs) are intercellular mediators of host-pathogen interaction that play a critical role in the dissemination of pathogen and host-derived molecules during infection (1).EVs contain microbial components that trigger innate immune responses by increasing the production of proinflammatory effector molecules like reactive oxygen species (ROS) or stimulating cytokine and chemokine release (2).EVs also contain pathogen-derived antigens and pathogen associated molecular patterns that induce cross-priming to activate antigen-specific CD4 + and CD8 + T cell expansion.In contrast, exosomes from infected cells also inhibit cytokine production by T cells (3).Thus, microbial and host components can spread beyond infected cells through exosomes to either activate or suppress immune responses, collectively influencing the outcome of infection (4).The focus of this Research Topic is to shed light on recent findings that illustrate the role of EVs in regulating immunostimulatory/immunosuppressive responses that are either essential for host immunity or for pathogen-mediated immune escape.Exosomes derived from Mycobacterium tuberculosis-infected macrophages activate various immune-related phenomena, from inflammatory responses to antigen presentation (5).In a study by Zhang et al., authors conduct proteomics profiling of serum exosomes isolated from normal individuals and patients of active tuberculosis (ATB).Subsequent pathway and functional analysis helps decipher the functions of differentially expressed proteins, suggesting that proteins are selectively packaged inside exosomes during various physiological conditions.These differentially expressed proteins include major histocompatibility complex (MHC) class I, CD36 (cluster of differentiation Frontiers in Immunology frontiersin.org01
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".