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Real-time detection of carboplatin using a microfluidic system.

ABSTRACT: A microfluidic sensor system based on a carbon nanotube-epoxy composite electrode was fabricated to allow detection of the presence of the anti-cancer drug carboplatin in healthy tissue in real time during chemotherapy. Detection of carboplatin was carried out by observing the effects of the drug on the differential pulse voltammetry of free purine bases using a novel carbon nanotube-epoxy composite electrode. In free solution these electrodes performed better than glassy carbon electrodes for oxidation of the free purine bases AMP and GMP, and than DNA-modified carbon nanotube-epoxy composite sensors for detection of carboplatin. On-line carboplatin detection was performed using a computer-controlled microfluidic platform. The methodology for on-line carboplatin detection was optimised in terms of the analysis time and to allow repeated carboplatin measurement using the same electrode. Microdialysis sampling and our microfluidic platform were combined to give a proof-of-concept system for real-time carboplatin detection with a limit of detection of 0.014 ?M carboplatin in the sampled media. This paper is dedicated to Craig Lunte's pioneering work in analysis and microdialysis.

SUBMITTER: Phairatana T

PROVIDER: S-EPMC5123639 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Real-time detection of carboplatin using a microfluidic system.

Phairatana Tonghathai T Leong Chi Leng CL Gowers Sally A N SA Patel Bhavik Anil BA Boutelle Martyn G MG

The Analyst 20161001 22

A microfluidic sensor system based on a carbon nanotube-epoxy composite electrode was fabricated to allow detection of the presence of the anti-cancer drug carboplatin in healthy tissue in real time during chemotherapy. Detection of carboplatin was carried out by observing the effects of the drug on the differential pulse voltammetry of free purine bases using a novel carbon nanotube-epoxy composite electrode. In free solution these electrodes performed better than glassy carbon electrodes for o ...[more]

PMID: 27796386

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Project description:BackgroundLaryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.MethodsAll 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS.ResultsLPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications.ConclusionsLPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Project description:Infection diagnosis and antibiotic susceptibility testing (AST) are time-consuming and often laborious clinical practices. This paper presents a microwave-microfluidic biosensor for rapid, contactless and non-invasive device for testing the concentration and growth of Escherichia Coli (E. Coli) in medium solutions of different pH to increase the efficacy of clinical microbiology practices. The thin layer interface between the microfluidic channel and the microwave resonator significantly enhanced the detection sensitivity. The microfluidic chip, fabricated using standard soft lithography, was injected with bacterial samples and incorporated with a microwave microstrip ring resonator sensor with an operation frequency of 2.5?GHz and initial quality factor of 83 for detecting the concentration and growth of bacteria. The resonator had a coupling gap area on of 1.5?×?1.5?mm2 as of its sensitive region. The presence of different concentrations of bacteria in different pH solutions were detected via screening the changes in resonant amplitude and frequency responses of the microwave system. The sensor device demonstrated near immediate response to changes in the concentration of bacteria and maximum sensitivity of 3.4?MHz compared to a logarithm value of bacteria concentration. The minimum prepared optical transparency of bacteria was tested at an OD600 value of 0.003. The sensor's resonant frequency and amplitude parameters were utilized to monitor bacteria growth during a 500-minute time frame, which demonstrated a stable response with respect to detecting the bacterial proliferation. A highly linear response was demonstrated for detecting bacteria concentration at various pH values. The growth of bacteria analyzed over the resonator showed an exponential growth curve with respect to time and concurred with the lag-log-stationary-death model of cell growth. This biosensor is one step forward to automate the complex AST workflow of clinical microbiology laboratories for rapid and automated detection of bacteria as well as screening the bacteria proliferation in response to antibiotics.

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Real-time detection of carboplatin using a microfluidic system.

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Real-time detection of carboplatin using a microfluidic system.

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