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Record W2314288396 · doi:10.1021/la403981t

Investigation of pH-Induced Protein Conformation Changes by Nanomechanical Deflection

2014· article· en· W2314288396 on OpenAlexaff
Garima Thakur, Keren Jiang, Dongkyu Lee, K. Prashanthi, Seonghwan Kim, Thomas Thundat

Bibliographic record

VenueLangmuir · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsChemistryDeprotonationProtonationBiosensorSurface plasmon resonanceMacromoleculeBiophysicsLigand (biochemistry)CrystallographyBiochemistryNanotechnologyMaterials scienceNanoparticleOrganic chemistry

Abstract

fetched live from OpenAlex

Broad-spectrum biosensing technologies examine sensor signals using biomarkers, such as proteins, DNA, antibodies, specific cells, and macromolecules, based on direct- or indirect-conformational changes. Here, we have investigated the pH-dependent conformational isomerization of human serum albumin (HSA) using microcantilevers as a sensing platform. Native and denatured proteins were immobilized on cantilever surfaces to understand the effect of pH on conformational changes of the protein with respect to the coupling ligand. Our results show that protonation and deprotonation of amino acid residues on proteins play a significant role in generating charge-induced cantilever deflection. Surface plasmon resonance (SPR) was employed as a complementary technique to validate the results.

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: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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