NMR spectroscopy of a single mammalian early stage embryo
Bibliographic record
Abstract
The resolving power, chemical sensitivity and non-invasive nature of NMR have made it an established technique for in vivo studies of large organisms both for research and clinical applications. NMR would clearly be beneficial for analysis of entities at the microscopic scale of about 1 nL (the nanoliter scale), 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 µm) and sensitivity issues have prevented a widespread adoption of NMR. In this article we show how these limitations can be overcome to obtain NMR spectra of a mammalian embryo in its early stage. To achieve this we employ ultra-compact micro-chip technologies in combination with 3D-printed micro-structures. Such device is packaged for use as plug & play sensor and it shows sufficient sensitivity to resolve NMR signals from individual bovine pre-implantation embryos. The embryos in this study are obtained through In Vitro Fertilization (IVF) techniques, transported cryopreserved to the NMR laboratory, and measured shortly after thawing. In less than 1 h these spherical samples of just 130-190 µm produce distinct spectral peaks, largely originating from lipids contained inside them. We further observe how the spectra vary from one sample to another despite their optical and morphological similarities, suggesting that the method can further develop into a non-invasive embryo assay for selection prior to embryo transfer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".