Immune Cell Degranulation in Fungal Host Defence
Bibliographic record
Abstract
Humans have developed complex immune systems that defend against invading microbes, including fungal pathogens. Many highly specialized cells of the immune system share the ability to store antimicrobial compounds in membrane bound organelles that can be immediately deployed to eradicate or inhibit growth of invading pathogens. These membrane-bound organelles consist of secretory vesicles or granules, which move to the surface of the cell, where they fuse with the plasma membrane to release their contents in the process of degranulation. Lymphocytes, macrophages, neutrophils, mast cells, eosinophils, and basophils all degranulate in fungal host defence. While anti-microbial secretory vesicles are shared among different immune cell types, information about each cell type has emerged independently leading to an uncoordinated and confusing classification of granules and incomplete description of the mechanism by which they are deployed. While there are important differences, there are many similarities in granule morphology, granule content, stimulus for degranulation, granule trafficking, and release of granules against fungal pathogens. In this review, we describe the similarities and differences in an attempt to translate knowledge from one immune cell to another that may facilitate further studies in the context of fungal host defence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".