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Record W2904550080 · doi:10.3389/fmicb.2018.03084

A Fluorescence Based-Proliferation Assay for the Identification of Replicating Bacteria Within Host Cells

2018· article· en· W2904550080 on OpenAlexafffund
Ronald S. Flannagan, David E. Heinrichs

Bibliographic record

VenueFrontiers in Microbiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCystic Fibrosis Canada
KeywordsBacteriaBiologyPathogenIntracellular parasiteMicrobiologyPhagocyteHost (biology)Staphylococcus aureusPathogenic bacteriaGram-negative bacteriaIntracellularCell biologyEscherichia coliPhagocytosisGeneticsGene

Abstract

fetched live from OpenAlex

Understanding host pathogen interactions is paramount to the development of novel antimicrobials. An important facet of this pursuit is the accurate characterization of pathogen replication within infected host cells. Here we describe the use of a fluorescence-based proliferation assay to identify intracellular populations of replicating bacteria at the subcellular level. Using Staphylococcus aureus as a model Gram-positive bacterial pathogen and macrophages as a model host phagocyte, we demonstrate this assay can be used to reliably identify individual phagocytes that contain replicating bacteria. Furthermore, we demonstrate this assay is compatible with additional cellular probes that enable characterization of cellular compartments in which replicating bacteria reside. Finally, we demonstrate that this assay facilitates the investigation of both Gram-negative and Gram-positive bacteria within host cells.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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

Citations33
Published2018
Admission routes2
Has abstractyes

Explore more

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