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Record W2141158025 · doi:10.1101/cshperspect.a019687

Host Cell Invasion by Medically Important Fungi

2014· review· en· W2141158025 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsEndocytosisBiologyCryptococcus neoformansCandida albicansCell biologyCellHost (biology)MicrobiologyIntracellular

Abstract

fetched live from OpenAlex

To infect the host and cause disease, many medically important fungi invade normally nonphagocytic host cells, such as endothelial cells and epithelial cells. Host cell invasion is a two-step process consisting of adherence followed by invasion. There are two general mechanisms of host cell invasion, induced endocytosis and active penetration. Furthermore, fungi can traverse epithelial or endothelial cell barriers either by proteolytic degradation of intercellular tight junctions or via a Trojan horse mechanism in which they are transported by leukocytes. Although these mechanisms of host cell invasion have been best studied using Candida albicans and Cryptococcus neoformans, it is probable that other invasive fungi also use one or more of these mechanisms to invade host cells. Identification of these invasion mechanisms holds promise to facilitate the development of new approaches to inhibit fungal invasion and thereby prevent disease.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.331
Teacher spread0.300 · 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