Elucidating Drug Resistance in Human Fungal Pathogens
Bibliographic record
Abstract
Fungal pathogens cause life-threatening infections in immunocompetent and immunocompromised individuals. Millions of people die each year due to fungal infections, comparable to the mortality attributable to tuberculosis or malaria. The three most prevalent fungal pathogens are Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus. Fungi are eukaryotes like their human host, making it challenging to identify fungal-specific therapeutics. There is a limited repertoire of antifungals in clinical use, and drug resistance and host toxicity compromise the clinical utility. The three classes of antifungals for treatment of invasive infections are the polyenes, azoles and echinocandins. Understanding mechanisms of resistance to these antifungals has been accelerated by global and targeted approaches, which have revealed that antifungal drug resistance is a complex phenomenon involving multiple mechanisms. Development of novel strategies to block the emergence of drug resistance and render resistant pathogens responsive to antifungals will be critical to treating life-threatening fungal infections.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 0.002 |
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".