MétaCan
Menu
← Back to cohort
Record W4385766880 · doi:10.1101/2023.08.08.552515

North American Powassan virus encompasses diverse <i>in vitro</i> phenotypes

2023· preprint· en· W4385766880 on OpenAlexaboutno aff
Rebekah J. McMinn, Rose M. Langsjoen, Erica Normandin, Samuel D. Stampfer, Pardis C. Sabeti, Anne Piantadosi, Gregory D. Ebel

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPhenotypeVirologyPopulationFlavivirusGenetic diversityVirusGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

Abstract Powassan virus (POWV) is a tick-borne flavivirus which has resulted in increasing human cases over the past two decades. Despite high prevalence in ticks and evidence of broad distribution in North America, fewer than 50 human cases are detected annually with evidence of undetected asymptomatic infections. Experimental studies of the relationships between POWV genetic diversity and disease potential are currently lacking. In the present study, sixteen isolates originating from 13 locations in the United States and Canada were used to assess in vitro phenotypic diversity in human neuronal cells. Broad differences in replication and cytopathic ability were observed between isolates, even amongst those in the same sublineage. In vitro phenotype was not associated with geographic or temporal location and could not be associated with specific genotypes. These results support the observation that the North American POWV population may be highly genetically and phenotypically diverse. The degree to which in vitro phenotype reflects transmission and pathogenesis remains to be determined.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.242
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicMosquito-borne diseases and control→French-language works237,207→