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Record W4281626559 · doi:10.1038/s41598-022-13259-1

Label-free multiplex electrochemical immunosensor for early diagnosis of lysosomal storage disorders

2022· article· en· W4281626559 on OpenAlexafffund
Haya Abdulkarim, Mohamed Siaj

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsKrabbe diseaseMultiplexGlutaraldehydeChemistryLysosomal storage diseaseDetection limitCysteamineEnzyme replacement therapyGlucocerebrosidaseMolecular biologyChromatographyBiochemistryMedicineEnzymeBiologyPathologyBioinformaticsDisease

Abstract

fetched live from OpenAlex

Pompe, Gaucher and Krabbe disease are lysosomal storage disorders (LSDs) which are a group of genetic diseases that causes the accumulation of lipids in tissues and cells. Pompe, Gaucher and Krabbe are characterized by the deficiency of acid α-glucosidase (GAA), β-Glucocerebrosidase (GBA) and galactocerebrosidase (GALC), and treatable if detected in their early stages. Here, we present the fabrication of an electrochemical immunosensor for the multiplexed quantification and simultaneous detection of GAA, GBA and GALC. The sensor was developed by electrodepositing gold nanoparticles (AuNPs) on an array of carbon electrodes, followed by the immobilization of GAA, GBA and GALC specific antibodies via functionalization with cysteamine and glutaraldehyde. The multiplexed immunosensor was able to successfully detect GAA, GBA and GALC at the femtomolar level with respective low detection limits of 0.12 pg/ml, 0.31 pg/ml and 0.18 pg/ml. The immunosensor showed good selectivity, sensitivity and good recovery when spiked in human serum, which confirms its possible applicability in point-of-care testing for the early diagnosis of LSDs.

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)0.002
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 score0.237
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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
Published2022
Admission routes2
Has abstractyes

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