Progress and prospects for targeting Hsp90 to treat fungal infections
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
Fungal pathogens pose a major threat to human health worldwide. They infect billions of people each year, leading to at least 1·5 million deaths. Treatment of fungal infections is difficult due to the limited number of clinically useful antifungal drugs, and the emergence of drug resistance. A promising new strategy to enhance the efficacy of antifungal drugs and block the evolution of drug resistance is to target the molecular chaperone Hsp90. Pharmacological inhibitors of Hsp90 function that are in development as anticancer agents have potential to be repurposed as agents for combination antifungal therapy for some applications, such as biofilm infections. For systemic infections, however, effective combination therapy regimens may require Hsp90 inhibitors that can selectively target Hsp90 in the pathogen, or alternate strategies to compromise function of the Hsp90 chaperone machine. Selectively impairing Hsp90 function in the pathogen could in principle be achieved by targeting Hsp90 co-chaperones or regulators of Hsp90 function that are more divergent between pathogen and host than Hsp90. Antifungal combination therapies could also exploit downstream effectors of Hsp90 that are critical for fungal drug resistance and virulence. Here, we discuss the progress and prospects for establishing Hsp90 as an important therapeutic target for 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.
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) | 0.000 | 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 | 0.000 |
| 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