Host cytokine responses distinguish invasive from airway isolates of the Streptococcusmilleri/anginosis group
Bibliographic record
Abstract
BACKGROUND: The Streptococcus Milleri/Anginosus Group (SMG) colonize mucosal surfaces, especially the airways, and are considered to be normal mucosal microbiota; however, they are a major cause of abscesses, pneumonia and pleural empyema. The production of exoenzymes and virulence factors do not correlate with SMG pathogenicity. Since SMG infections are associated with robust inflammatory responses, we hypothesized that host immune responses might distinguish strains associated with asymptomatic carriage and those associated with fulminant disease. METHODS: We measured IL1β, TNF, IL10, IL12, IL23, IL17, and IL4 production from human peripheral blood mononuclear cells (PBMCs) stimulated with a panel of clinical isolates from the airways and infections and measured the ability of these isolates to stimulate TLR2. RESULTS: Isolates were categorized based on the levels of cytokines they induced from PBMCs (high, intermediate, low). Airway isolates predominantly induced low levels of cytokines and isolates from invasive disease induced higher levels, although about 10% of the strains produced divergent cytokine responses between donors. Interestingly, the donors were most divergent in their production of IL17, IL12 and IL23. CONCLUSIONS: We propose that the ability to inhibit or avoid an inflammatory response is associated with carriage in the airways and variability in responses between isolates and donors might contribute to susceptibility to disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".