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Record W1645565655 · doi:10.3791/53053

Immunofluorescence to Monitor the Cellular Uptake of Human Lactoferrin and its Associated Antiviral Activity Against the Hepatitis C Virus

2015· article· en· W1645565655 on OpenAlexafffund
Andréa Allaire, Frédéric Picard‐Jean, Martin Bisaillon

Bibliographic record

VenueJournal of Visualized Experiments · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsImmunofluorescenceAntibodyLactoferrinBiologyConfocal microscopyFluorescence microscopeVirusVirologyCell biologyImmunologyFluorescenceBiochemistry

Abstract

fetched live from OpenAlex

Immunofluorescence is a laboratory technique commonly used to study many aspects of biology. It is typically used to visualize the distribution and/or localization of a target molecule in cells and tissues. Immunofluorescence relies on the specificity of fluorescent-labelled antibodies against their corresponding antigens within a cell. Both direct and indirect immunofluorescence approaches can be used which rely on the use of antibodies linked with a fluorochrome. Direct immunofluorescence is less frequently used because it provides lower signal, involves higher cost and less flexibility. In contrast, indirect immunofluorescence is more commonly used because of its high sensitivity and provides an amplified signal since more than one secondary antibody can attach to each primary antibody. In this manuscript, both epifluorescence microscopy and confocal microscopy were used to monitor the internalization of human lactoferrin, an important component of the immune system, into hepatic cells. Moreover, we monitored the inhibitory potential of hLF on the intracellular replication of the Hepatitis C virus using immunofluorescence. Both the advantages and disadvantages associated with these approaches are discussed.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.410
Teacher spread0.365 · 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 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

Citations9
Published2015
Admission routes2
Has abstractyes

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