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Record W4210256143 · doi:10.3390/chemosensors10020044

Electrochemical Sensor for the Direct Determination of Warfarin in Blood

2022· article· en· W4210256143 on OpenAlexaff
Iman Saeedi, Soha Ahmadi, Michael Thompson, Payman Hashemi, Zahra Ramezani

Bibliographic record

VenueChemosensors · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDetection limitCalibration curvePoint of carePotentiometerWhole bloodMaterials scienceChromatographyMicrofluidicsIonTherapeutic drug monitoringCalibrationChemistryNanotechnologyDrugPharmacologySurgeryVoltageMedicine

Abstract

fetched live from OpenAlex

Detecting warfarin levels in the blood is of critical importance in anticoagulant therapy because it is imperative that the concentration of the drug is maintained within a specific range. In this paper, we present a proof-of-concept of a novel sensing device based on ion-selective electrode (ISE) technology for the direct detection of warfarin in blood samples without any sample pretreatment. We used tetradodecylammonium chloride (TDDA) as an ion-exchanger to fabricate an ion-selective membrane. The ISE we developed showed high sensitivity, with a limit of detection (LOD) of 1.25 × 10−7 M and 1.4 × 10−5 M for detecting warfarin in buffer and blood, respectively. The sensor also exhibited promising selectivity in identifying the presence of various ions including chloride and salicylate, the most abundant ions in blood with a calibration slope of 58.8 mV/dec. We envision combining the ISE with a microfluidic system and a simple potentiometer to produce a sensitive, selective, and portable point-of-care testing device for monitoring the level of warfarin in patients’ blood during treatment.

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 score0.069
Threshold uncertainty score0.480

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 head0.013
GPT teacher head0.235
Teacher spread0.223 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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