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Record W3185085294 · doi:10.1101/2021.07.26.453772

NMR spectroscopy of a single mammalian embryo

2021· preprint· en· W3185085294 on OpenAlexafffund
Giulia Sivelli, Gaurasundar M. Conley, Carolina Herrera, Kathryn Marable, Kyle J. Rodriguez, H. Bollwein, Mateus José Sudano, Juergen Brügger, André J. Simpson, Giovanni Boero, Marco Grisi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaInnosuisse - Schweizerische Agentur für InnovationsförderungÉcole Polytechnique Fédérale de LausanneEuropean Commission
KeywordsEmbryoSensitivity (control systems)In vivoNuclear magnetic resonance spectroscopyChemistryBiological systemMaterials scienceNanotechnologyAnalytical Chemistry (journal)BiologyChromatographyCell biologyGeneticsStereochemistry

Abstract

fetched live from OpenAlex

Abstract The resolving power, chemical sensitivity and non-invasive nature of NMR has made it an established technique for in vivo studies of large organisms both for research and clinical applications. These features would clearly be beneficial at the nanoliter scale (nL), typical of early development of mammalian embryos, microtissues and organoids, the scale where the building blocks of complex organisms could be observed. However, the handling of such small samples (about 100 micrometers) and sensitivity issues have prevented the widespread adoption of NMR. Recently we have shown how these limitations can be overcome with ultra-compact single-chip probes. In this article we show that such probes have sufficient sensitivity to detect and resolve NMR signals from individual bovine pre-implantation embryos. In less than 1 hour these spherical samples of just 130-190 micrometers produce distinct spectral peaks, largely originating from lipids contained inside them. We further observe how the spectral features, namely the peak intensities, vary from one sample to another despite their optical and morphological similarities.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicReproductive Biology and Fertility→French-language works237,207→