<i>Cryptococcus neoformans</i> Gene Expression during Experimental Cryptococcal Meningitis
Bibliographic record
Abstract
Cryptococcus neoformans, an encapsulated basidiomycete fungus of medical importance, is capable of crossing the blood-brain barrier and causing meningitis in both immunocompetent and immunocompromised individuals. To gain insight into the adaptation of the fungus to the host central nervous system (CNS), serial analysis of gene expression (SAGE) was used to characterize the gene expression profile of C. neoformans cells recovered from the CNS of infected rabbits. A SAGE library was constructed, and 49,048 tags were sequenced; 16,207 of these tags were found to represent unique sequences or tag families. Of the 304 most-abundant tags, 164 were assigned to a putative gene for subsequent functional grouping. The results (as determined according to the number of tags that identified genes encoding proteins required for these functions) indicated that the C. neoformans cells were actively engaged in protein synthesis, protein degradation, stress response, small-molecule transport, and signaling. In addition, a high level of energy requirement of the fungal cells was suggested by a large number of tags that matched putative genes for energy production. Taken together, these findings provide the first insight into the transcriptional adaptation of C. neoformans to the host environment and identify the set of fungal genes most highly expressed during cerebrospinal fluid infection.
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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".