PocketELISA: A low-cost portable ELISA reader based on image analysis over PDA platform for clinical diagnose in medical veterinary

Pérez, Mónica A.; Mera, Maria Isabel; Arias, J. R.; Arza, B.G.; Carleos, C.E.; Muiz, R.; Torre, Cristina de la; Cifuentes, D.; Cueli, J.C.; Baró, J.A.

doi:10.1109/isie.2008.4677127

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Several studies have previously investigated the use of compact digital cameras or cell phone cameras for biomedical imaging applications. Consumer-grade cameras have been used for wide-field fluorescence imaging [13] – [16] , pathology analysis [17] , and the on-board cameras of cell phones [20] – [24] and PDAs [25] have been used in clinical applications. Here we demonstrated a portable high-resolution fiber-optic fluorescence imaging system for in vivo cellular imaging.…”

Section: Discussionmentioning

confidence: 99%

A Fiber-Optic Fluorescence Microscope Using a Consumer-Grade Digital Camera for In Vivo Cellular Imaging

et al. 2010

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BackgroundEarly detection is an essential component of cancer management. Unfortunately, visual examination can often be unreliable, and many settings lack the financial capital and infrastructure to operate PET, CT, and MRI systems. Moreover, the infrastructure and expense associated with surgical biopsy and microscopy are a challenge to establishing cancer screening/early detection programs in low-resource settings. Improvements in performance and declining costs have led to the availability of optoelectronic components, which can be used to develop low-cost diagnostic imaging devices for use at the point-of-care. Here, we demonstrate a fiber-optic fluorescence microscope using a consumer-grade camera for in vivo cellular imaging.MethodsThe fiber-optic fluorescence microscope includes an LED light, an objective lens, a fiber-optic bundle, and a consumer-grade digital camera. The system was used to image an oral cancer cell line labeled with 0.01% proflavine. A human tissue specimen was imaged following surgical resection, enabling dysplastic and cancerous regions to be evaluated. The oral mucosa of a healthy human subject was imaged in vivo, following topical application of 0.01% proflavine.FindingsThe fiber-optic microscope resolved individual nuclei in all specimens and tissues imaged. This capability allowed qualitative and quantitative differences between normal and precancerous or cancerous tissues to be identified. The optical efficiency of the system permitted imaging of the human oral mucosa in real time.ConclusionOur results indicate this device as a useful tool to assist in the identification of early neoplastic changes in epithelial tissues. This portable, inexpensive unit may be particularly appropriate for use at the point-of-care in low-resource settings.

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Section: Discussionmentioning

confidence: 99%

A Fiber-Optic Fluorescence Microscope Using a Consumer-Grade Digital Camera for In Vivo Cellular Imaging

et al. 2010

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“…The colour value was then compared to the test sample's corresponding deltamethrin concentration, determined using HPLC-DAD [18]. A similar approach has been used in other colour reaction analyses [20].…”

Section: Analysis Of the Insecticide Content And Insecticidal Activitmentioning

confidence: 99%

A Colorimetric Test for the Evaluation of the Insecticide Content of LLINs Used on Bioko Island, Equatorial Guinea

Kaur

Allan

Eggelte

et al. 2020

Preprint

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BackgroundInsecticide-treated nets and indoor residual spraying of insecticides are used as the major vector control interventions in the fight against malaria. Measuring the actual amount of deposits of insecticides on bed nets and walls is essential for evaluating the quality and effectiveness of the intervention. A colorimetric “Test Kit” designed for use as a screening tool, able to detect the type II pyrethroids on fabrics and sprayed walls, was used for the first time to detect deltamethrin on long-lasting insecticidal nets (LLINs) deployed on Bioko Island, Equatorial Guinea.MethodsLLINs were analysed using the colorimetric Test Kit performed in situ, which leads to the formation of an orange-red solution whose depth of colour indicates the amount of type II pyrethroid on the net. The kit results were validated by measuring the amount of extracted insecticide using high-performance liquid chromatography (HPLC) with diode array detection (DAD).ResultsDeltamethrin concentration was determined for 130 LLINs by HPLC-DAD. The deltamethrin concentration of these nets exhibited a significant decrease with the age from 65 mg/m2 (<12 months of use) to 31 mg/m2 (>48 months; p<0.001). Overall, 18% of the nets being used in households had <15 mg/m2 of deltamethrin, thus falling into the failed category as assessed by the colorimetric Test Kit. This was supported by the bioassay determined bio-efficacy of the nets with ≥80% mortality following the World Health Organization (WHO) minimal effectiveness criteria. The Test Kit was field evaluated in situ and found to be rapid, accurate, and easy to use by people without laboratory training. The Test Kit was shown to have a reliable linear relationship between depth of colour produced and deltamethrin concentration (R2 = 0.9135).ConclusionResults of this study show that this colorimetric test was a reliable method to assess the insecticidal content of LLINs under operational conditions. The Test Kit provides immediate results and offers a rapid, inexpensive, field-friendly alternative to the complicated and costly methods as HPLC and WHO cone bioassays which also need specialist staff. Thus, enabling National Malaria Programs to gain access to effective and affordable monitoring tools for use in situ.

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“…However, a simple and rapid method for detection of disease using a regular hand‐held equipment such as a smartphone is yet to be operational. This concept of using a cellphone has also been implemented in several works where the advantages of a mobile, portable, and economical approach with high performance has been realised [14]. A prevailing research has suggested the use of an apparatus involving LED light to illuminate the ELISA and optical fibres to then sense the data to further feed into the smartphone [15].…”

Section: Introductionmentioning

confidence: 99%

Smartphone camera‐based analysis of ELISA using artificial neural network

Nath¹,

Sarcar²,

Chatterjee³

et al. 2018

IET Computer Vision

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The proposed method indicates an inexpensive, portable, and easily accessible method for the quantitative analysis of medical samples for the detection of disease in the enzyme-linked immunosorbent assay (ELISA). The procedure follows a point-of-care diagnostic model and attends to the several challenges in healthcare system in rural settings. The proposed technique will alleviate the inconveniences faced by the average citizen of a country with insufficient resources to implement an affordable healthcare administration for its entire population. A smartphone is used to procure images of an ELISA containing para-nitrophenol samples which is then fed into a machine learning algorithm, specifically artificial neural network. The introduction of two relatively new technologies in medical aid-the smartphone and machine learning not only reduces cost and time of detection, but also presents ample possibility for further development. The predictions result in highly accurate diagnostic labels. The same method can be used for blood samples for the prediction of presence of any disease, provided adequate training set has been deployed.

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PocketELISA: A low-cost portable ELISA reader based on image analysis over PDA platform for clinical diagnose in medical veterinary

Cited by 6 publications

References 2 publications

A Fiber-Optic Fluorescence Microscope Using a Consumer-Grade Digital Camera for In Vivo Cellular Imaging

A Fiber-Optic Fluorescence Microscope Using a Consumer-Grade Digital Camera for In Vivo Cellular Imaging

A Colorimetric Test for the Evaluation of the Insecticide Content of LLINs Used on Bioko Island, Equatorial Guinea

Smartphone camera‐based analysis of ELISA using artificial neural network

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