The Study of Pro-Inflammatory Molecules Induced by Genetically and Phenotypically Diverse Strains of Haemophilus influenzae Type a in an in vitro Infection Model
Bibliographic record
Abstract
Introduction: Haemophilus influenzae type a (Hia) has recently emerged as a cause of invasive disease in North American Indigenous children. Factors determining the outcomes of exposure to the pathogen, from asymptomatic carriage to fatal disease, are poorly understood. The role of innate immune activation in the pathogenesis of invasive Hia disease remains unexplored. We used clinical Hia isolates to determine whether innate immune responses depended on the presence of the capsule, strain genetic background, and abilities to cause invasive disease. Methods: Differentiated THP-1 cells and HL-60 neutrophil-like cells were stimulated with four Hia strains (invasive or noninvasive; encapsulated or nonencapsulated), in comparison to 1 invasive and 1 noninvasive non-typeable H. influenzae. Surface expression of ICAM-1 and CD64 and release of pro-inflammatory cytokines TNF-α and IL-1β were quantified. Results: In vitro Hia infection resulted in robust activation of inflammatory responses in terms of expression of ICAM-1 and release of TNF-α and IL-1β, irrespective of the presence or absence of the capsule, or abilities to cause invasive disease. Inhibition of TLR4 decreased TNF-α release by THP-1 cells stimulated by Hia. Conclusion: Powerful activation of pro-inflammatory responses induced by Hia may contribute to the pathogenesis of invasive Hia disease. As the activation of macrophages and neutrophils did not depend on encapsulation or source of Hia isolation, the functional abilities of phagocytic cells unlikely represent a limiting factor in host defenses. The development of invasive versus noninvasive disease may depend on the functional abilities of the adaptive immune system.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".