MétaCan
Menu
Back to cohort
Record W4364321946 · doi:10.1109/tim.2023.3265117

A Low-Cost Handheld Reconfigurable Impedimetric Readout System for Diagnostics of Viral Infections

2023· article· en· W4364321946 on OpenAlexafffund
Hamed Osouli Tabrizi, Razieh Salahandish, Pezhman Jalali, Mahmood Khalghollah, Fatemeh Haghayegh, Amir Sanati‐Nezhad, Ebrahim Ghafar‐Zadeh

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2023
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of CalgaryYork University
FundersCanadian Institutes of Health Research
KeywordsBiosensorElectrical impedanceDynamic rangeSensitivity (control systems)Focused Impedance MeasurementComputer scienceElectronic engineeringMaterials scienceNanotechnologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a portable impedimetric biosensor for detecting infectious diseases such as SARS-CoV-2 Infections. A bio-ready sensing electrode functionalized with SARS-CoV-2 nucleocapsid antibody was employed to quantitatively convert the concentration of nucleocapsid protein (N-protein) into impedance changes. In this paper, we proposed a readout system with a dynamic input range of 200 Ω to 1 MΩ magnitude and 0 to 180° phase. The resolution of this device is 1% and 6.5° for measuring the magnitude and phase, respectively. Herein we demonstrate and discuss the proposed system’s functionality, sensitivity, and selectivity using the clinical swab samples. As per these results, this readout system is suitable for the detection of N-protein ranging up to 10,000 pg/mL with a resolution of 56 fg/mL. The proposed impedimetric sensing system can be adopted for the detection of infectious diseases in the future. This low-cost (<$80) device using off-the-shelf is a unique candidate for batch production purposes during urgent pandemic situations.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.453

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 head0.030
GPT teacher head0.237
Teacher spread0.207 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Instrumentation and MeasurementSame topicBiosensors and Analytical DetectionFrench-language works237,207