Enhanced characterization of yeast hydrolysate combining acid digestion and <scp>1D‐1H NMR</scp> targeted profiling
Bibliographic record
Abstract
Abstract Yeast extract, or autolysate, is a required component for many cell‐culture media, but its exact constituents and benefits are unknown. Yeast extract contains a diverse assortment of metabolites, often present in complex forms (eg, polypeptides and polynucleotides). This study employs one‐dimensional proton nuclear magnetic resonance (1D‐1H NMR) spectroscopy to analyze free (ie, readily available) components present in commercially available yeast autolysate. The product is monitored while further subjected to acid hydrolysis, allowing for a more robust understanding of the exact components present, particularly those contained in complex forms. The amino acids and glucose compounds behaved as expected based on other acid hydrolysis studies, and were modelled similarly. Parameter estimation was in strong agreement with pre‐hydrolysis targeted 1D‐1H NMR profiling. This analysis was expanded to components not as thoroughly investigated and was especially applicable to nucleic compounds. Acid hydrolysis revealed that the yeast extract was approximately 5.4% nucleic material by weight, mostly composed of adenosine, and largely provided by RNA due to the presence of uracil and lack of thymidine. Choline‐containing compounds were also positively identified with an observable increase of free choline during hydrolysis. 1D‐1H NMR spectroscopy allowed for the simultaneous monitoring of a significant number of yeast‐extract metabolites, some of which were previously unreported. Utilizing 1D‐1H NMR spectroscopy in conjunction with acid hydrolysis led to a more complete view of the compounds present, and accounted for an additional 24% of the yeast‐extract mass.
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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) | 0.001 | 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".