Bibliographic record
Abstract
Infection with the filarial nematodes that cause diseases such as lymphatic filariasis and onchocerciasis represent major public health challenges. With millions of people at risk of infection, new strategies for treatment or prevention are urgently needed. More complete understanding of the host immune system's ability to control and eliminate the infection is an important step towards fighting these debilitating infectious diseases. Neutrophils are innate immune cells that are rapidly recruited to inflamed or infected tissues and while considered primarily anti-microbial, there is increasing recognition of their role in helminth infections. Filarial nematodes present a unique situation, as many species harbour the bacterial endosymbiont, Wolbachia. The unexpected involvement of neutrophils during filarial infections has been revealed both in human diseases and animal studies, with strong evidence for recruitment by Wolbachia. This present review will introduce the different human filarial diseases and discuss neutrophil involvement in both protective immune responses, but also in the exacerbation of pathology. Additionally, we will highlight the contributions of the murine model of filariasis, Litomosoides sigmodontis. While several studies have revealed the importance of neutrophils in these parasite infections, we will also draw attention to many questions that remain to be answered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".