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Record W3205534244 · doi:10.3791/63144

<em>Drosophila melanogaster</em> Larva Injection Protocol

2021· article· en· W3205534244 on OpenAlexaff
Ghada Tafesh‐Edwards, Eric Kenney, Ioannis Eleftherianos

Bibliographic record

VenueJournal of Visualized Experiments · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect symbiosis and bacterial influences
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsDrosophila melanogasterMelanogasterBiologyInnate immune systemModel organismImmunityDrosophila (subgenus)GenomeVirulenceGenetic modelGeneticsComputational biologyImmune systemGene

Abstract

fetched live from OpenAlex

The use of unconventional models to study innate immunity and pathogen virulence provides a valuable alternative to mammalian models, which can be costly and raise ethical issues. Unconventional models are notoriously cheap, easy to handle and culture, and do not take much space. They are genetically amenable and possess complete genome sequences, and their use presents no ethical considerations. The fruit fly Drosophila melanogaster, for instance, has provided great insights into a variety of behavior, development, metabolism, and immunity research. More specifically, D. melanogaster adult flies and larvae possess several innate defense reactions that are shared with vertebrate animals. The mechanisms regulating immune responses have been mostly revealed through genetic and molecular studies in the D. melanogaster model. Here a novel larval injection technique is provided, which will further promote investigations of innate immune processes in D. melanogaster larvae and explore the pathogenesis of a wide range of microbial infections.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0600.040

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 head0.355
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2021
Admission routes1
Has abstractyes

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