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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 OpenAlex
Luisina Ongaro, Carlos Agustín Isidro Alonso, Xiang Zhou, Emilie Brûlé, Yining Li, Gauthier Schang, Albert F. Parlow, Frederik J. Steyn, Daniel J. Bernard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
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 score0.229
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
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