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Human haptoglobin phenotypes and concentration determination by nanogold-enhanced electrochemical impedance spectroscopy

2011· article· en· W2006306813 on OpenAlexfundno aff
Tsai-Mu Cheng, Tzu-Cheng Lee, Shin-Hua Tseng, Hsueh-Liang Chu, Ju‐Pin Pan, Chia‐Ching Chang

Bibliographic record

VenueNanotechnology · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
FundersNational Research Council CanadaUniversity of OttawaJohns Hopkins University
KeywordsHaptoglobinDielectric spectroscopyMaterials sciencePhenotypeAnalytical Chemistry (journal)HemoglobinBlood proteinsElectrochemistryCalibration curveBiophysicsChromatographyDetection limitGeneBiologyChemistryBiochemistryElectrodeImmunology

Abstract

fetched live from OpenAlex

Haptoglobin (Hp) is an acute phase protein that binds free hemoglobin (Hb), preventing Hb-induced oxidative damage in the vascular system. There are three phenotypes in human Hp, whose heterogeneous polymorphic structures and varying concentrations in plasma have been attributed to the cause of diseases and outcome of clinical treatments. Different phenotypes of Hp may be composed of the same subunits but different copy numbers, rendering their determination difficult by a single procedure. In this study, we have developed a simple, fast, reliable and sensitive method, using label-free nanogold-modified bioprobes coupled with self-development electrochemical impedance spectroscopy (EIS). By this method, probe surface charge transfer resistance is detected. The relative charge transfer resistance ratios for Hp 1-1, Hp 2-1 and Hp 2-2 were characterized. We were able to determine protein size difference within 3 nm, and the linear region of the calibration curve for Hp levels in the range of 90 pg ml(-1) and 90 µg ml(-1) (∼1 fM to 1 pM). We surmise that similar approaches can be used to investigate protein polymorphism and altered protein-protein interaction associated with diseases.

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 score0.004
Threshold uncertainty score0.727

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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