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Record W3122536534 · doi:10.1210/endocr/bqab014

Development of a Highly Sensitive ELISA for Measurement of FSH in Serum, Plasma, and Whole Blood in Mice

2021· article· en· W3122536534 on OpenAlexafffund
Luisina Ongaro, Carlos Agustín Isidro Alonso, Xiang Zhou, Emilie Brûlé, Yining Li, Gauthier Schang, Albert F. Parlow, Frederik J. Steyn, Daniel J. Bernard

Bibliographic record

VenueEndocrinology · 2021
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsFollicle-stimulating hormoneLuteinizing hormoneProlactinHormoneRadioimmunoassayEndocrinologyInternal medicineBiologyMedicine

Abstract

fetched live from OpenAlex

Follicle-stimulating hormone (FSH) regulates gonadal function and fertility. Measurement of FSH in bodily fluids and tissues is possible with radioimmunoassays and enzyme-linked immunosorbent assays (ELISAs). Recently, several novel assays were developed to measure pituitary hormones including growth hormone, prolactin, and luteinizing hormone in mice from small sample volumes. Here, we describe a novel and sensitive ELISA that enables the accurate measurement of FSH in serum, plasma, and whole blood from female and male mice. The assay can also be used to measure FSH in murine pituitary lysates and cell culture media. In summary, the new methodology described here will enable investigators to measure FSH from a variety of biological samples in mice accurately, at low cost, and in their own laboratories.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.027
GPT teacher head0.260
Teacher spread0.232 · 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
GenreMethods

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

Citations42
Published2021
Admission routes2
Has abstractyes

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