Performance Assessment of a 125 Human Plasma Peptide Mixture Stored at Room Temperature for Multiple Reaction Monitoring–Mass Spectrometry
Bibliographic record
Abstract
Synthetic peptides are a critical requirement for the development and application of targeted mass spectrometry (MS)-based assays for the quantitation of proteins from biological matrices. Transporting synthetic peptides on dry ice from one laboratory to another is costly and often difficult because of country-specific import and export regulations. Therefore, in this study, we assessed the impact of leaving a lyophilized mixture consisting of 125 peptides at room temperature for up to 20 days, and we assessed the effect on the quantitative performance of multiple reaction monitoring-MS (MRM-MS) assays. The findings suggest that there are no significant differences in the MRM-MS results for the time points assessed in this study (up to 20 days). All the calibration curves and quality control (QC) samples met the acceptance criteria for precision and accuracy (raw data are available via the public MS data repository PanoramaWeb, identifier: /MRM Proteomics/2020_BAK125_RT). The number of endogenous proteins quantifiable across five plasma samples was consistently between 87 and 99 out of 125 for all time points. Moreover, the coefficients of variation (CVs) calculated for the majority of peptide concentrations across all samples and time points were <5%. In addition, a lyophilized peptide mixture was transported from Canada to Iceland without dry ice. The results showed that there was no significant difference in the quantitative performance, with the determined concentrations of most proteins in the samples falling within 30% between the analyses performed on the same three plasma samples in Iceland and those in Canada. Overall, a comparison of the results obtained in Canada and in Iceland indicated that the peptides were stable under the conditions tested and also indicated that shipping lyophilized peptide mixtures without dry ice, but in the presence of sufficient desiccant material, could be a feasible option in cases where transport difficulties may arise or dry-ice sublimation may occur.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".