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Record W2213269181 · doi:10.20381/ruor-4109

Capillary Electrophoresis - Mass Spectrometry for Bioanalysis

2015· dissertation· en· W2213269181 on OpenAlexfundno aff
Gleb G. Mironov

Bibliographic record

VenueuO Research (University of Ottawa) · 2015
Typedissertation
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsBioanalysisCapillary electrophoresisMass spectrometryChromatographyCapillary electrophoresis–mass spectrometryChemistryElectrospray ionization

Abstract

fetched live from OpenAlex

Bioanalysis is a subdivision of analytical chemistry and deals with biological analytes such as metabolites, proteins, nucleic acids, small molecules, virus particles and entire cells. The rationale of my thesis was to achieve two goals: (i) develop a set of ready to use methods (ii) which are capable providing exact concentrations of analytes as well as kinetic and thermodynamic parameters of their interactions. To investigate interactions between biomolecules special conditions are required which do not interfere with the course if biomolecule interactions. Establishing these conditions and optimization of separation and detection parameters can be tedious and can take longer than actual analysis of samples. I developed a variety of Capillary Electrophoresis – Mass Spectrometry (CE-MS) methods suitable for bionalalysis. CE-MS establishes a new paradigm that separation methods together with MS detection can be used as comprehensive kinetic tools. Most previous attempts to use chromatography and electrophoresis for studying nucleic acid interactions were restricted to assuming slow or no equilibrium between reactants. Kinetic CE (KCE) shows that non-zero kinetics and structural dynamics must be taken into account when separation happens. KCE-MS could be a valuable supplement to IM-MS due to the separation of ions in solution according to their size-to-charge ratio. These methods allowed to reveal new facts about biomolecules and added novel data to the bank of the mankind knowledge. For the best of my knowledge, kinetic parameters for TG2 and thrombin G-quadruplex folding were reported for the first time. I developed a homogeneous method to determine kon, koff and Kd of fast and weak noncovalent interactions between multiple unlabeled ligands (small molecule drugs) and an oligosaccharide (α- or β-cyclodextrin) simultaneously in one capillary microreactor. It has been shown for the first time that KCE can be used to separate and detect the slowly interconverting open and closed conformations of human TG2. It allowed the first direct measurement of the Kd value for calcium binding. Sixteen new substrates were discovered for three aminotransferases (AAT, BCAT, and DAAT). In addition, Viral qCE showed a feasibility to analyse both the count of intact viral particles and sample nucleic acid contamination.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0150.024

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 head0.023
GPT teacher head0.275
Teacher spread0.253 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

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