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Record W4384929401 · doi:10.3390/pr11072148

Membrane-Based Micro-Volume Dialysis Method for Rapid and High-Throughput Protein Crystallization

2023· article· en· W4384929401 on OpenAlexafffund
Raja Ghosh

Bibliographic record

VenueProcesses · 2023
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrystallizationProtein crystallizationDialysisMembraneVolume (thermodynamics)Materials scienceTetragonal crystal systemNanotechnologyNucleationChemical engineeringChromatographyChemistryCrystallographyCrystal structureBiochemistryOrganic chemistryEngineeringMedicine

Abstract

fetched live from OpenAlex

Protein crystallization techniques are very important in drug development. This paper discusses a membrane-based micro-volume dialysis method suitable for rapid and high-throughput protein crystallization. A droplet of protein solution was deposited on the membrane surface in a micro-volume dialysis device. Crystallizing agents could be added to the protein solution either directly or through the membrane. The crystallization process could easily be monitored in real time under a microscope. Tiny specks, indicative of forming crystals, were observed as early as 30 s from the start of the experiment, and these were clearly distinguishable as tetragonal lysozyme crystals within 2 min. This method is particularly suitable for carrying out screening and optimization experiments, and for studying crystallization kinetics. The easy and direct manner in which protein and crystallizing agents are introduced into the device makes this method amenable to miniaturization and automation. Additionally, this approach would potentially allow for rapid screening of the effects of drug molecules on the crystallization process and the nature of crystal formation.

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)0.000
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: none
Teacher disagreement score0.448
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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