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Record W4246560524 · doi:10.32920/ryerson.14645256.v1

Identification and Quantification of Proteins from Preparative Partition Chromatography and Peptides from Organic Extraction of Fetal Versus Adult Bovine Serum using Nano-Spray-LC-ESI-MS/MS

2021· preprint· en· W4246560524 on OpenAlexaff
Zhuo Zhen Chen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFetal bovine serumFetusTrypsinCell cultureBovine serum albuminGrowth factorCellChemistryProteomicsBiologyCell growthChromatographyBiochemistryReceptorPregnancy

Abstract

fetched live from OpenAlex

Blood proteins communicate with many different cells, tissue and organs; perform key functions in the immune system and may be of particular biological complexity. One of the most widely used blood products in the laboratory is fetal bovine serum for cell culture. There are ethical and practical concerns regarding the use of fetal serum from animals and alternative serum-free replacements have been attempted using platelet lysates. Previous biochemical experiments have shown that FBS apparently contained factors such as alpha-feto protein (AFP) and insulin-like growth factors that may support the indefinite cell growth and division of certain cell lines. It is presumed that a set of as yet undefined growth factors transform cells growth resulting in rapid proliferation. Cultured Raw cells 264.7 in adult bovine serum multiplied slowly and differentiated into elongated cells with a dendritic shape, which died after the first few generations. On the contrary, in fetal bovine serum, cultured cells multiplied rapidly and formed many smaller cells with a rounded shape through many cell passages. Three independent batches of fetal bovine serum were tested on Raw cells 264.7 macrophages to confirm that they supported cell growth in culture compared to three independent batches of adult bovine serum. The intact proteins of each serum sample were separated by partition chromatography into 16 fractions with an increasing step gradient of salts over quaternary amine resin (proteomics). The endogenous peptides were precipitated with 90% of acetonitrile and extracted into 10 fractions with a decreasing step gradient of acetonitrile in water (peptidomics). Trypsin digested intact proteins and endogenous peptides were then analyzed on a fresh C18 nano-HPLC column with random and independent sampling by LC-ESI-MS/MS. The fractionated mass spectra were identified with SEQUEST and X!TANDEM algorithms. Redundant use of MS/MS spectra were flirted out with the SQL Server system and the R statistical analysis system was used to perform Chi Square (X2) analysis of frequency counts and ANOVA of the log10 precursor intensity results. Alpha-feto protein, fetal albumin, insulin, insulin like growth factors, platelet derived growth factors and proteins associated with HRAS/AKT growth pathway at the level of ligand, receptors, receptor associated enzyme and nucleic acid binding proteins including transcription factors were observed to be specifically enriched in fetal serum.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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 routes1
Has abstractyes

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