Biomphalaria glabrata Granulin Increases Resistance to Schistosoma mansoni Infection in Several Biomphalaria Species and Induces the Production of Reactive Oxygen Species by Haemocytes
Bibliographic record
Abstract
Gastropod molluscs, which have co-evolved with parasitic digenean trematodes for millions of years, utilize circulating heamocytes as the primary method of containing and killing these invading parasites. In order to do so, they must generate suitable amounts of haemocytes that are properly armed to kill parasitic worms. One method by which they generate the haemocytes required to initiate the appropriate cell mediated immune response is via the production and post-translational processing of granulins. Granulins are an evolutionarily conserved family of growth factors present in the majority of eukaryotic life forms. In their pro-granulin form, they can elicit cellular replication and differentiation. The pro-granulins can be further processed by elastase to generate smaller granulin fragments that have been shown to functionally differ from the pro-granulin precursor. In this study, we demonstrate that in vivo addition of Biomphalaria glabrata pro-granulin (BgGRN) can reduce Schistosoma mansoni infection success in numerous Biomphalaria sp. when challenged with different S. mansoni strains. We also demonstrate that cleavage of BgGRN into individual granulin subunits by elastase results in the stimulation of haemocytes to produce reactive oxygen species.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".