Signal Transduction in the Interactions of Fungal Pathogens and Mammalian Hosts
Bibliographic record
Abstract
This chapter highlights both the generalities and the unique features of signaling pathways controlling interactions between fungal pathogens and mammalian hosts. Of the common fungal pathogens of humans, Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus have been the most extensively investigated. Although many different signal transduction cascades mediate responses of pathogens to their hosts, the most extensively investigated networks involve cAMP, MAPK, two-component histidine kinase (HK), pH pathways, and Ca2+/calmodulin signaling. The pathways can mediate different environmental conditions, such as temperature, stress, and presence of serum, within the different pathogens and induce different responses, including changes in cell morphology and expression of particular virulence factors. TLR2 and TLR4 appear to be the most important for the recognition of fungal pathogens. Both TLR2 and TLR4 play a role in the recognition of A. fumigatus and C. albicans, while C. neoformans, through its polysaccharide capsule consisting of glucuronoxylomannan, appears to be recognized uniquely by TLR4. The developing tools of knockout mice, genome sequences, cultured cell lines, and DNA microarrays have a profound impact on one's ability to define the interaction between mammalian host cells and fungal pathogens. The tools to address such questions are here or soon to arrive, the questions themselves are exciting and answerable, and the potential payoffs for understanding the signaling pathways in fungal pathogens are profound.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".