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Record W2045408446 · doi:10.1371/journal.ppat.1004646

Does the Arthropod Microbiota Impact the Establishment of Vector-Borne Diseases in Mammalian Hosts?

2015· article· en· W2045408446 on OpenAlexafffund
Constance A. M. Finney, Shaden Kamhawi, James D. Wasmuth

Bibliographic record

VenuePLoS Pathogens · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect symbiosis and bacterial influences
Canadian institutionsUniversity of Calgary
FundersNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsBiologyVector (molecular biology)Host (biology)Immune systemTransmission (telecommunications)ImmunologyEcologyGeneticsRecombinant DNAComputer science

Abstract

fetched live from OpenAlex

The impact of the microbiota on the immune status of its host is a source of intense research and publicity. In comparison, the effect of arthropod microbiota on vector-borne infectious diseases has received little attention. A better understanding of the vector microbiota in relation to mammalian host immune responses is vital, as it can lead to strategies that affect transmission and improve vaccine design in a field of research where few vaccines exist and effective treatment is rare. Recent demonstrations of how microbiota decrease pathogen development in arthropods, and thus alter vector permissiveness to vector-borne diseases (VBDs), have led to renewed interest. However, hypotheses on the interactions between the arthropod-derived microbiota and the mammalian hosts have yet to be addressed. Advances in DNA sequencing technology, increased yield and falling costs, mean that these studies are now feasible for many microbiologists and entomologists. Here, we distill current knowledge and put forward key questions and experimental designs to shed light on this burgeoning research topic.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations51
Published2015
Admission routes2
Has abstractyes

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