The pathogenicity of <i>Aspergillus fumigatus</i>, drug resistance, and nanoparticle delivery
Bibliographic record
Abstract
The genus Aspergillus includes fungal species that cause major health issues of significant economic importance. These microorganisms are also the culprit for production of carcinogenic aflatoxins in grain storages, contaminating crops, and economically straining the production process. Aspergillus fumigatus is a very important pathogenic species, being responsible for high human morbidity and mortality on a global basis. The prevalence of these infections in immunosuppressed individuals is on the rise, and physicians struggle with the diagnosis of these deadly pathogens. Several virulence determinants facilitate fungal invasion and evasion of the host immune response. Metabolic functions are also important for virulence and drug resistance, since they allow fungi to obtain nutrients for their own survival and growth. Following a positive diagnostic identification, mortality rates remain high due, in part, to emerging resistance to frequently used antifungal drugs. In this review, we discuss the role of the main virulence, drug target, and drug resistance determinants. We conclude with the review of new technologies being developed to treat aspergillosis. In particular, microsphere and nanoparticle delivery systems are discussed in the context of improving drug bioavailability. Aspergillus will likely continue to cause problematic infections in immunocompromised patients, so it is imperative to improve treatment options.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".