Iron and fungal pathogenesis: a case study with Cryptococcus neoformans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The acquisition of iron from mammalian hosts is an important aspect of infection because microbes must compete with the host for this nutrient and iron perception often regulates virulence factor expression. For example, iron levels are known to influence the elaboration of two major virulence factors, the polysaccharide capsule and melanin, in the pathogenic fungus Cryptococcus neoformans. This pathogen, which causes meningoencephalitis in immunocompromised people, acquires iron through the use of secreted reductants, cell surface reductases, a permease/ferroxidase uptake system and siderophore transporters. In addition, a master regulator, Cir1, integrates iron sensing with the expression of virulence factors, with growth at 37 degrees C and with signalling pathways that also influence virulence. The challenge ahead is to develop mechanistic views of the iron acquisition functions and regulatory schemes that operate when C. neoformans is in host tissue. Achieving these goals may contribute to an understanding of the notable predilection of the fungus for the mammalian central nervous 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it