Data set for mass spectrometric analysis of recombinant human serum albumin from various expression systems
Bibliographic record
Abstract
Human serum albumin (HSA) is a versatile and important protein for the pharmaceutical industry (Fanali et al., Mol. Aspects Med. 33(3) (2012) 209-290). Due to the potential transmission of pathogens from plasma sourced albumin, numerous expression systems have been developed to produce recombinant HSA (rHSA) (Chen et al., Biochim. Biophys. Acta (BBA)-Gen. Subj. 1830(12) (2013) 5515-5525; Kobayashi, Biologicals 34(1) (2006) 55-59). Based on our previous study showing increased glycation of rHSA expressed in Asian rice (Frahm et al., J. Phys. Chem. B 116(15) (2012) 4661-4670), both supplier-to-supplier and lot-to-lot variability of rHSAs from a number of expression systems were evaluated using reversed phase liquid chromatography linked with MS and MS/MS analyses. The data are associated with the research article 'Determination of Supplier-to-Supplier and Lot-to-Lot Variability in Glycation of Recombinant Human Serum Albumin Expressed in Oryza sativa' where further analysis of rHSA samples with additional biophysical methods can be found (Frahm et al., PLoS ONE 10(9) (2014) e109893). We determined that all rHSA samples expressed in rice showed elevated levels of arginine and lysine hexose glycation compared to rHSA expressed in yeast, suggesting that the extensive glycation of the recombinant proteins is a by-product of either the expression system or purification process and not a random occurrence.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.031 |
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".