Evidence map›Paper›PMID 41361653›Full record

ArticleMikrochimica acta2025

Machine learning-integrated electrochemical sensing of ciprofloxacin for digital point-of-care therapeutic drug monitoring.

Chellakannu Rajkumar, Ngoc-Dau Mai, Suganthy Ramamoorthy, Urwa Shahid, Lionel Nkenyereye, Kwon Taek Lim, Wan-Young Chung, Tae Hwan Oh

Abstract read
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In one paragraph

Article in Mikrochimica acta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chellakannu RajkumarDepartment of Electronic Engineering, Pukyong National University, Busan, 48513, South Korea.
Ngoc-Dau MaiDepartment of Artificial Intelligence Convergence, Pukyong National University, Busan, 48513, South Korea.
Suganthy RamamoorthyIndustry 4.0 Convergence of Bionics Engineering, Pukyong National University, Busan, 48513, South Korea.
Urwa ShahidIndustry 4.0 Convergence of Bionics Engineering, Pukyong National University, Busan, 48513, South Korea.
Lionel NkenyereyeBK21; AI Convergence Education & Research Group, Pukyong National University, Busan, 48513, South Korea.
Kwon Taek LimIndustry of display semiconductor technology, Pukyong National University, Busan, 48513, South Korea.
Wan-Young ChungDepartment of Electronic Engineering, Pukyong National University, Busan, 48513, South Korea. wychung@pknu.ac.kr.
Tae Hwan OhSchool of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, 280 Daehak-Ro, Republic of Korea. taehwanoh@ynu.ac.kr.

Funding

National Research Foundation NRF-2019R1A2C1089139
6 · The paper itself

Abstract

Timely and precise therapeutic drug monitoring (TDM) is critical for managing pharmacokinetic variability and optimizing individualized therapy, particularly during public health crises such as the COVID-19 pandemic. Herein, we optimized integrated machine learning (ML) with an electrochemical sensor for accurate, non-invasive quantification of ciprofloxacin (CFX) drug in human urine for digital health-care settings. Synthesis of a dual molecularly imprinted polymer (duMIP) matrix is described via electropolymerization of chitosan (Chit) and poly(ortho-phenylenediamine) (poly(o-PD)) with CFX as a template, coated onto nitrogen-doped cerium oxide (CeO

Indexed as

CiprofloxacinDrug MonitoringElectrochemical TechniquesMachine LearningPoint-of-Care SystemsAnti-Bacterial AgentsCeriumChitosanHumansLimit of DetectionMolecularly Imprinted PolymersAnti-Bacterial Agentsceric oxideCeriumChitosanCiprofloxacinMolecularly Imprinted PolymersCerium oxideCOVID-19 pandemicImprinted polymerMachine learning algorithmsTherapeutic drug

Identifiers

PMID41361653

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.