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Record W2920766005 · doi:10.1093/mmy/myy067

Beyond tissue concentrations: antifungal penetration at the site of infection

2018· review· en· W2920766005 on OpenAlex
Yanan Zhao, Brendan Prideaux, Shane R. Baistrocchi, Donald C. Sheppard, David S. Perlin

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Mycology · 2018
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsAntifungalAntifungal drugAntifungal drugsDrugPenetration (warfare)EchinocandinsMedicineBiologyPharmacologyIntensive care medicineMicrobiologyVoriconazole

Abstract

fetched live from OpenAlex

Despite advances in antifungal therapy, invasive fungal infections remain a significant cause of morbidity and mortality worldwide. One important factor contributing to the relative ineffectiveness of existing antifungal drugs is insufficient drug exposure at the site of infection. Despite the importance of this aspect of antifungal therapy, we generally lack a full appreciation of how antifungal drugs distribute, penetrate, and interact with their target organisms in different tissue subcompartments. A better understanding of drug distribution will be critical to guide appropriate use of currently available antifungal drugs, as well as to aid development of new agents. Herein we briefly review current perspectives of antifungal drug exposure at the site of infection and describe a new technique, matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, which has the potential to greatly expand our understanding of drug penetration.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.877
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.035
GPT teacher head0.381
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it