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Record W4319019656 · doi:10.3390/membranes13020182

Membrane-Based Hybrid Method for Purifying PEGylated Proteins

2023· article· en· W4319019656 on OpenAlexafffund
Shing Fung Lam, Xiaojiao Shang, Raja Ghosh

Bibliographic record

VenueMembranes · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsPolyethylene glycolMicrofiltrationChromatographyMembranePEG ratioMicelleChemistryMaterials scienceLyotropicSalt (chemistry)Chemical engineeringPolymerOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

PEGylated proteins are usually purified using chromatographic methods, which are limited in terms of both speed and scalability. In this paper, we describe a microfiltration membrane-based hybrid method for purifying PEGylated proteins. Polyethylene glycol (or PEG) is a lower critical solution temperature polymer which undergoes phase transition in the presence of a lyotropic salt and forms micelle-like structures which are several microns in size. In the proposed hybrid method, the PEGylated proteins are first converted to their micellar form by the addition of a lyotropic salt (1.65 M ammonium sulfate). While the micelles are retained using a microfiltration membrane, soluble impurities such as the unmodified protein are washed out through the membrane. The PEGylated proteins thus retained by the membrane are recovered by solubilizing them by removing the lyotropic salt. Further, by precisely controlling the salt removal, the different PEGylated forms of the protein, i.e., mono-PEGylated and di-PEGylated forms, are fractionated from each other. Hybrid separation using two different types of microfiltration membrane devices, i.e., a stirred cell and a tangential flow filtration device, are examined in this paper. The membrane-based hybrid method for purifying PEGylated proteins is both fast and scalable.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.168
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.308
Teacher spread0.282 · 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 teacher head, 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
Published2023
Admission routes2
Has abstractyes

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