Smartphone-based clinical diagnostics

Chapter 48

Smartphone-based clinical diagnostics Shengwei Zhang1, Taleb Ba Tis2 and Qingshan Wei1, 3 1

Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, United States; 2Department of Materials

Science and Engineering, North Carolina State University, Raleigh, NC, United States; 3Emerging Plant Disease and Global Food Security Cluster, North Carolina State University, Raleigh, NC, United States

Introduction Most molecular detection methods are currently designed and developed for laboratory use, which show several drawbacks, such as complicated steps and procedures, and requirement of highly trained professionals. The concept of point-of-care (POC) testing or diagnostics was timely introduced to address these issues. In POC testing, samples are collected and analyzed at the bedside, or in the clinician’s office, and the results can be obtained in real time. Due to their cost-effectiveness, fast result turnaround, and small device footprint, POC diagnostic tools have shown great promise for predictive and personalized healthcare.

Potential social, economical, and public-health impact There is an urgent need for next-generation sensing and measurement tools, for accurate and reliable diagnosis of human diseases in resource-limited settings. In many parts of the world, especially developing countries in Africa, Asia, and Latin America, the access to advanced diagnostic technologies or laboratories is still quite limited. At the same time, many high-risk diseases, such as HIV infection, hepatitis, parasite, and viral infections, are still endemic in these regions. With limited medical professional presence, disease diagnostics could only be performed in a very inefficient way, for example, by visual checking of disease symptoms. As such, the development of portable, costeffective, and ready-to-use diagnostic devices, is of great importance in addressing the public health issues in developing countries and remote areas. Mobile phone users worldwide have reached 5 billion in 2017 [1]. Global smartphone users have increased from 1.57 billion in 2014 to 2.53 billion in 2018 [2]. The increase of smartphones is even faster in developing countries, where the ownership rate has increased from

21% in 2013 to 37% in 2015 [3]. Therefore, smartphones are becoming easily accessible and affordable tools, for most people around the world. Smartphones can be converted into compact, low-cost sensing and imaging tools, for applications in global health, in particular in the field and resource-limited settings. A number of review articles have summarized these applications, including sensing, nanoscale imaging, POC detection, and disease diagnostics [4e13].

Smartphone technologies Current smartphones work like a portable computer in a pocket. Equipped with built-in sensors, such as camera, GPS module, and accelerometer, smartphones can be used as data acquisition devices or wearable sensors. The cameras installed in smartphones have undergone massive technological advances. In the past decade, the total pixel count of smartphone image sensor chips doubles almost every 2 years, following a Moore’s Law trend [7]. High-performance image sensors have been applied to the newest smartphone models, producing much better image quality than before. As of July 2018, smartphone-based CMOS image sensors (e.g., Sony IMX 586), provide pixel counts as high as 48 megapixels, and pixel size as small as 0.8 mm [14]. As a result, the optical resolution of a smartphone microscope can be enhanced theoretically to the sub-micron level. These advances in imaging hardware, have made it possible to take high-quality images with smartphones, which is closely comparable with those obtained from highend CMOS/CCD cameras. In addition, powerful processors and adequate memory, have made it possible to process and store data directly on the phone. Almost every smartphone comes with wireless communication like Wi-Fi, cellular network, and Bluetooth. As such, smartphone-acquired data can also be easily transmitted to nearby computers or remote servers, for various telemedicine applications.

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Detection methods in smartphonebased devices Smartphone-based microscopy To prepare a smartphone-based microscope, it requires to integrate multiple different optical components with the smartphone camera. Bright-field imaging can be carried out on the smartphone by simply adding an external lens and light source. The external lens functions equivalently to the objective lens in a traditional microscope, and the smartphone camera is analogous to the CCD/CMOS camera, on the benchtop counterpart. The magnification of the smartphone microscope can be estimated from the expression below: M ¼ f1/f2, where f1 is the focal length of the built-in lens of the smartphone camera, and f2 is the focal length of the external lens. The external lens can be either a single-piece lens or a compact lens-module used on a smartphone or Raspberry Pi camera. Such lens modules are usually cheap, readily available, and provide better image quality than single-piece lenses. Cost-effective, battery-powered white LEDs can be used as light sources for bright-field imaging. A dilemma in bright-field imaging, is the trade-off between spatial resolution and field of view (FOV). Lenses with higher magnification can provide better resolution, but with reduced FOV. In a more balanced example, Switz et al. used a reversed smartphone camera lens as the external lens, and achieved resolution less than 5 mm over a FOV larger than 10 mm2 [15].

Fluorescence imaging In fluorescence imaging, light source and optical filters with specific wavelengths are required, to excite the fluorophores and collect emission signals. A compact laser diode module is often used as the light source, when a high signal to noise ratio (SNR) is needed, while color LEDs become a more economical option, in combination with excitation filters. Effective excitation filters could be band-pass or short-pass optical filters, to narrow the spectral bandwidth of LEDs. In smartphone fluorescence microscopy, a strong background due to autofluorescence or nonspecific scattering is often the main limitation of high image contrast. To circumvent that, optical configuration based on either side (i.e., waveguide coupling), or tilted illumination at a high angle, can be adapted [16]. Fluorescence imaging on a smartphone with large FOV has also been reported [16]. With these techniques, high-sensitivity imaging results were reported, including imaging of 100-nm diameter nanoparticles (Fig. 48.1A), single viruses, and single DNA molecules [17,18].

Diffraction-based computational microscopy Imaging methods based on computational imaging have also been applied in smartphone microscopy, especially for cellular imaging and detection. In smartphone-based holographic imaging (Fig. 48.1B), the conventional lens system is removed. An aperture (w100 mm) is placed in front of the light source, to generate a partially coherent light illumination. The spatially filtered light is then shed onto the sample, and creates holograms, due to the interference between the scattered light from the samples and uninterrupted background. The hologram is then captured by the image sensor of the smartphone. The geometry and morphology of the sample can be reconstructed from the holograms digitally. This method provides a simple way to perform microscopic imaging on the smartphone without using any optical lenses [19]. Similarly, smartphone-based digital diffraction detection (or “3D”) has been reported for cellular imaging. In this method, antibody-labeled microbeads are added to the sample, to label specific antigens on the cell surface. Diffraction patterns are created by the illumination through a pinhole between sample and light source. The diffraction image is captured by the smartphone camera and later processed on a cloud server, to reconstruct the image. Both amplitude and phase information can be reconstructed in the output, which allows the number and position of microbeads bound on the cells to be identified for differentiation of different cell types. [20].

Smartphone-based quantitative sensing With smartphone camera as the detector, optical information from samples can also be quantitatively analyzed on the phone. In the fluorometric analysis, sample concentration is correlated with fluorescence intensity, measured by the detector. In smartphone-based fluorometric sensors, the fluorescent intensity is proportional to the brightness of the image taken by the camera. Under the same acquisition conditions like exposure time, ISO, and F aperture, a relationship can be found between the pixel brightness and analyte concentration. The setup of the smartphone fluorometric sensor is similar to smartphone-based fluorescence microscopy, where a laser module, an emission filter, and an external lens (if needed) are used. A more cost-effective way in recent work is to use the flashlight on the phone as the light source (Fig. 48.1C), which was filtered by a colored adhesive tape as excitation filter. The fluorescent signals are then separated by a colored piece of glass, as the emission filter [21].

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FIGURE 48.1 Representative detection methods in smartphone-based diagnostics. (A) Smartphone-based fluorescence microscopy [17]. (B) Lens-free microscopy on a cellphone [19]. (C) Fluorometric measurement of electrolyte concentrations from sweat [21]. (D) Colorimetric measurement of pH from sweat [23]. Reproduced with permission from the references mentioned above. Copyright 2013 American Chemical Society, 2010 Royal Society of Chemistry, 2018 Royal Society of Chemistry and 2013 Royal Society of Chemistry.

Colorimetric sensing Colorimetric analysis on the phone is a little bit more complicated, as both color and brightness information is involved and analyzed. One approach to quantify the colors from different sample spots is to compare red, green, and blue (RGB) values, extracted from the cellphone images. Either the value of R/G/B component itself or the difference in these color components can quantitatively reflect the color difference between samples [22]. Some studies also converted RGB values into other color space (like hue, saturation, lightness/HSL), to better correlate changes of color with respect to analyte concentrations, such as pH level (Fig. 48.1D) [23]. Imaging setup of colorimetric smartphone device, is similar to those of smartphone-based bright-field microscopes. Typically, a white light based on either the phone flashlight or an external LED is used to illuminate the samples. Reflected or transmitted light is then collected by the lens system of the smartphone camera. External lenses can be used in case higher magnification is needed.

Electrical and electrochemical sensing methods Smartphone-connected electrochemical biosensors are used in the detection of ions and small molecules from biological samples. Wireless communication modules like WiFi, Bluetooth, and NFC modules, can be installed on the

electrochemical biosensor, and the data are transmitted to the smartphone for next-step analysis. Moreover, amperometric [24], potentiometric [24], and impedimetric [25] sensors are able to be connected with smartphone interfaces.

Clinical diagnostic applications of smartphone devices Ions and small molecules Colorimetric and fluorometric detection of small analytes has been demonstrated by smartphones. By choosing proper sensing dyes, a variety of ions and molecules can be measured on the smartphones. Table 48.1 shows representative examples of these detections. Colorimetric measurement of pH from sweat and saliva has been carried out on a smartphone [23]. A saliva sample was collected on a test strip, and the image was taken by the smartphone for colorimetric analysis. Hue values were extracted from RGB images, to better reflect changes of color with respect to pH level. Fluorometric analysis has also been applied to detect common ions, from tear and sweat. Several chemical probes, namely crown ethers, o-acetanisidide, and seminaphtorhodafluor, were immobilized in the detection region, to generate fluorescent signals. Concentrations of Naþ, Kþ, Ca2þ and Hþ from tear

496 PART | III Hospital, managed care and public health applications

TABLE 48.1 Smartphone-based detection of ions and small molecules. Analyte

Sample

Method of detection

Results

References

pH

Saliva

Colorimetric

Range: 5.0e9.0

[23]

þ

þ

Na Kþ Ca2þ pH

Tear

Fluorescence

Na : Sensitivity ¼ 2.7 mM Kþ: Sensitivity ¼ 1.4 mM Ca2þ: Sensitivity ¼ 0.02 m pH: Sensitivity ¼ 0.06 pH units

[26]

Cle Naþ Zn2þ

Sweat

Fluorometric

Cl: Dynamic range ¼ 5e100mM Naþ: Dynamic range ¼ 20e60mM Zn2þ: Dynamic range ¼ 1e20mM

[21]

Naþ

Saliva

Fluorometric

e

[27]

Glucose

Human serum

Enzymatic colorimetric

LOD ¼ 0.7 mM (buffer) LOD ¼ 0.3 mM (serum)

[28]

Glucose

Blood

Enzymatic colorimetric

Range ¼ 110 e586 mM

[29]

Lactate

Oral fluids

Enzymatic colorimetric

LOD (oral fluid) ¼ 0.5 mmol/L

[30]

Vitamin B12

Blood

AuNP-based colorimetric

Vitamin B12: Sensitivity ¼ 87% Specificity ¼ 100%

[31]

Vitamin D

Human serum

AuNP-based colorimetric

Vitamin D: Accuracy ¼ 15 nM Precision ¼ 10 nM

[32]

Cholesterol

Blood

Colorimetric

Range: 140mg/dl-400 mg/dL Within 1.8% accuracy

[33]

Pregnanediol glucuronide (PdG)

Urine

Colorimetric, ELISA

Accuracy ¼ 82.20%

[34]

Cortisol

Saliva

Chemiluminescence

LOD ¼ 0.3 ng/mL (saliva) LOD ¼ 0.1 ng/mL (buffer) Range: LOD ¼ 60 ng/mL

[35]

samples were measured on a smartphone, when integrated with a paper-based microfluidic system (Fig. 48.2A). With independent light source and filters, this device can detect four ions simultaneously. This system has the potential to be applied in the diagnosis of dry eye [26]. Electrolytes from sweat can provide information about nutritional health and physical performance. In the work by Sekine et al., sweat sample was collected and mixed with preloaded dyes, in a soft and conformal microfluidic device. Concentrations of Naþ, Cl, and Zn2þ were read out by using a smartphone imaging device [21]. Fluorometric detection of sodium ion was also conducted on a smartphone device, where excitation and emission lights were separated by transmission grating [27].

Blood glucose Glucose oxidase (GOx)- horseradish peroxidase (HRP) enzymatic colorimetric assay, is commonly used in the

detection of glucose. In this assay, glucose is oxidized and produces H2O2. Then a color substance is produced from two colorless precursors, via an enzymatic oxidation process mediated by HRP. Glucose detection has recently been carried out, in test solutions as well as human serum, on a paper-based device, with a smartphone readout [28]. Based on a similar detection method, the glucose level in blood was measured, on an enzyme-immobilized hydrophilic PET film by a smartphone [29].

Lactate, vitamins, and steroids As the product of anaerobic respiration, lactate can be detected in a similar way of enzymatic colorimetry. The functionalized paper was used as a substrate, to enhance reagents stability and homogeneity of color distribution. The L-lactate in the oral fluid was then analyzed on a portable smartphone reader [30]. Concentrations of Vitamin B12 [31] and Vitamin D (Fig. 48.2B) [32] have also been measured

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FIGURE 48.2 Detection of ions and small molecules on smartphone platforms. (A) Smartphone-based colorimetric analysis of electrolyte concentrations from the tear [26]. (B) Quantification of vitamin D level on a smartphone platform [32]. (C) Cholesterol testing on a smartphone [33]. Reproduced with permission from the references mentioned above. Copyright 2014 Royal Society of Chemistry and 2014 Royal Society of Chemistry.

on the smartphone, in combination with a gold nanoparticle (AuNP)-based colorimetric immunoassay. In these works, antibodies of the vitamins were conjugated on AuNPs. Competitive binding of antibody-modified AuNP was performed on the test line. The color intensities of these lines were then quantified through the colorimetric analysis. Several steroids, including cholesterol (Fig. 48.2C), and steroid hormones, including cortisol, have also been measured on the smartphone by colorimetric or fluorometric methods [33e35]. Representative cases of ions and small molecules detection for diagnostic applications on the smartphone are shown in Fig. 48.2.

Detection of proteins Traditionally, tests of proteins are performed with sandwich immunoassays, and the assay signals are quantified by benchtop readers. The smartphone-based sensing technologies provide an alternative approach in a much faster and cost-effective way. Table 48.2 summarizes representative results in the quantification of protein biomarkers by smartphones for disease diagnosis. Petryayeva et al. designed a fluorescence-based assay, to detect thrombin concentration in whole blood and serum. A paper-in-PDMS microfluidic chip was fabricated, containing thrombin-sensitive test spot and insensitive reference spot. Upon the exposure of test spot to the sample, quenched photoluminescence of quantum dot (QD630) was recovered. The intensity of photoluminescence was calibrated to quantify the concentration of thrombin [36]. Using inkjet printing technology, Joh et al. developed a

sensitive point-of-care “D4” immunoassay, which is capable of detecting multiple protein targets from a drop of blood. They tested their assay on a compact and cost-effective smartphone reader for leptin detection. The results were comparable with those obtained from a tabletop glass slide scanner [37]. Measurement of CRP concentration was similarly reported by using a colorimetric assay and smartphone readout [38].

Antibody biomarkers of infectious disease Enzyme-linked immunosorbent assay (ELISA) is the gold standard for antibody detection. A number of novel ELISA assays that operate on the smartphone devices have recently been reported. For example, researchers from Sia’s lab designed a smartphone dongle for the POC diagnosis of HIV and syphilis (Fig. 48.3A). In this device, a disposable cassette with microfluidic channels was preloaded with reagents. Blood sample flew through the channels, reacted with the gold-labeled antibodies, and subsequently was washed by the washing buffer. The silver enhancing reagents were then added, which amplified the signals of gold nanolabels by darkening the color and increasing optical density (OD) of test spots. Little power was required to run this device, since the flow of the sample and washing buffers, was driven by a mechanical vacuum pump at the time of assay. The audio jack on the smartphone was used for powering the electronics and data transmission. In the field test, the smartphone dongle met the need of current clinical requirements, with a sensitivity of 92%e100% and specificity of 79%e100% [39].

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TABLE 48.2 Examples of protein detection on smartphone platforms for disease diagnosis. Method of detection

Analyte

Sample

Results

References

Thrombin

Whole blood and serum

Fluorescent

Time  30 min LOD ¼ 18 NIH units/mL

[36]

Leptin

Serum

Fluorescent

LOD ¼ 0.71 ng/mL

[37]

C-reactive protein

e

Colorimetric

LOD ¼ 0.026 mg/mL

[38]

HIV antibody Syphilis antibody

Whole blood

Gold/silver amplified ELISA

HIV: sensitivity ¼ 100% specificity ¼ 87% Syphilis: sensitivity ¼ 92% specificity ¼ 92%

[39]

HIV-1-p17 hemagglutinin (HA)

Blood plasma

Bioluminescence

LOD ¼ 10 pM

[40]

Highly pathogenic H5N1

Throat swab samples

Fluorescent

Sensitivity ¼ 96.55% Specificity ¼ 98.55%

[41]

Influenza hemagglutinin (HA)

e

Digital diffraction detection

LOD ¼ 0.9 ng

[42]

Mumps IgG Measles IgG Herpes simplex virus (HSV) IgG

e

Colorimetric

Assay time ¼ 1 min Accuracy: Mumps IgG ¼ 99.6% Measles IgG ¼ 98.6% HSV-1 IgG ¼ 99.4% HSV-2 IgG ¼ 99.4%

[43]

HE4

Urine

Colorimetric

Sensitivity ¼ 89.5% Specificity ¼ 90% Assay time ¼ 5 h LOD ¼ 19.5 ng/mL Range ¼ 19.5 e1250 ng/mL

[44]

Prostate-specific antigen (PSA)

Whole blood

Colorimetric fluorescent

Colorimetric detection: Assay time ¼ 13 min LOD ¼ 0.9 ng/mL Fluorescence detection Assay time ¼ 22 min LOD ¼ 0.08 ng/mL

[45]

Antigen of brain natriuretic peptide (BNP), suppression of tumorigenicity 2 (ST2)

Serum

Fluorescent

LOD of BNP ¼ 5 pg/ mL LOD of ST2 ¼ 1 ng/ mL

[46]

In HIV detection, Arts and his colleagues developed an alternative method, based on bioluminescent sensor protein, and applied the system on the smartphone for the detection of HIV p17 antigen. This platform can be applied in the detection of hemagglutinin (HA), and dengue virus type I as well [40]. Other viral diseases, like avian influenza, have also been detected on smartphone platforms. Yeo et al.

reported a fluorescent ELISA platform on the smartphone to detect highly pathogenic H5N1 influenza virus [41]. Smartphone-based digital diffraction detection (or “D3”) assay (Fig. 48.3B) has also been applied in molecular diagnostics, including the detection of biomarkers of breast and cervical cancer [20]. By using the D3 assay, molecular diagnostic of avian influenza on the smartphone

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FIGURE 48.3 Protein detection on smartphone platforms. (A) A smartphone dongle for point-of-care diagnosis of HIV and syphilis [39]. (B) Digital diffraction imaging (D3 assay) on a smartphone for protein biomarker detection [20]. (C) Microplate reader on a smartphone for portable ELISA testing [43]. (D) Smartphone-based lateral flow strip reader for biomarker detection associated with heart failure [46]. Reproduced with permission from the references mentioned above. Copyright 2015 The American Association for the Advancement of Science and 2017 American Chemical Society.

has been demonstrated [42]. A smartphone-based 96-well plate reader (Fig. 48.3C), for the detection of characteristic antibodies for multiple viral infections, including mumps, measles, and HSV infection, has also been reported. This compact microtiter plate reader builds a bridge between benchtop ELISA techniques and portable diagnostics in field settings [43].

Oncological and nononcological diagnosis Human epididymis protein 4 (HE4) is a biomarker for ovarian cancer. A colorimetric sandwich ELISA assay with cellphone readout has been reported by Wang et al. in the detection of HE4 [44]. In another example, Barbosa et al. performed ELISA quantification of prostate-specific antigen (PSA) on the smartphone. Colorimetric and fluorometric tests were compared in the detection of PSA from a whole blood sample [45]. Biomarker detection of the noncancerous disease on the smartphone has also been reported recently. Brain natriuretic peptide (BNP), and suppression of tumorigenicity 2 (ST2), are two biomarkers used in the prognosis evaluation of heart failure. You et al. designed a fluorescent lateral flow assay (LFA) platform (Fig. 48.3D), which detects these biomarkers using upconversion fluorescent nanoparticles, and the results

were quantified by a smartphone reader. These two biomarkers can be detected at the same time with high sensitivity and specificity [46].

Detection of viral nucleic acids Polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) are two commonly used nucleic acid amplification methods. However, limited by complicated assay steps, and the size and cost of the instruments, these methods are seldom adopted in remote areas, or regions with limited healthcare infrastructure. Below is a list of recent examples of nucleic acid detection on smartphone platforms, for diagnosis of major viral diseases (Table 48.3). Compared with PCR, LAMP is an isothermal DNA amplification method, that is more commonly used on smartphone-based nucleic acid detection. Unlike PCR, which requires temperature cycling, LAMP works at a constant temperature, which significantly simplifies the device design. Combined with the reverse transcription step, LAMP can be applied to detect RNA as well. As LAMP is typically run at 60e65 C, a heat source is needed. The fluorescent signal from dyes can be easily detected with a smartphone camera.

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TABLE 48.3 Examples of nucleic acid detection on smartphone platforms. Analyte

Sample

Method of detection

Results

References

HIV-1 RNA

e

Digital reverse-transcription loopmediated isothermal amplification (RTLAMP)

Resolution: twofold change at 105 copies/mL

[47]

HIV-1 RNA

Whole blood

RT-LAMP

LOD: 3 viruses/60 nL droplet (670 viruses/mL) Resolution: 10-fold change at 6.7  104 mL1, 100-fold change at 670 mL

[48]

1

Hepatitis C RNA

e

Colorimetric, digital RT-LAMP

Upper limit of quantification (ULQ) ¼ 1.1e1.6  107 copies/mL

[49]

Herpes simplex virus type 2 (HSV-2) DNA

e

LAMP

Sensitivity: 100 copies/reaction

[53]

Hepatitis B viral DNA HIV viral RNA

Clinical

Isothermal amplification

LOD: 10e50 fmol (6  109e3  1010 copies)/10 mL Resolution: up to 40-fold change

[54]

Zika virus (ZIKV)

Urine Blood Saliva

RT-LAMP

LOD95 ¼ 2 PFU/mL LOD50 ¼ 4.9 PFU/mL

[55]

Human papillomavirus (HPV) DNA

e

Digital diffraction detection

LOD: w50 atto-mole

[20]

Kaposi’s sarcoma herpesvirus (KSHV) DNA

e

AuNP-colorimetric

LOD ¼ 500 pM

[57]

Fluorescent imaging

Sizing accuracy: <1 kbp for >10 kbp

[18]

Single DNA

Integration of microfluidic digital amplification assay with a smartphone camera detector has been reported recently [47e49]. In a digital analysis, the sample is injected into an array of discrete microchambers, so that each microchamber contains one or zero target molecules of interest. After the reaction, only these chambers containing analytes can produce signals, which are considered positive regardless of signal intensity. The percentage of positive microwells will be used to calculate analyte concentration, based on Poisson statistics. A digital microfluidic chip named "SlipChip" has recently been applied in biomedical detection, including PCR and immunoassay (Talis Biomedical Corporation, Menlo Park, CA, USA) [50e52]. More recently, the SlipChip was tested on smartphone platforms for LAMP amplification [48,49]. Selck et al. tested RT-LAMP on SlipChip to detect HIV-1 RNA (Fig. 48.4A). They demonstrated that digital analysis was more robust against temperature fluctuations and reaction time variations, compared with traditional analog analysis. Given the limited precision of temperature control and imaging quality of smartphone LAMP platform, this research provided a method to run LAMP reactions with improved robustness, under low hardware requirements [48]. Colorimetric RT-LAMP has also been reported in the

detection of hepatitis C virus (HCV) RNA. In this work, a rotational SlipChip device was designed and built as the substrate. Positive and negative reactions generated blue and purple colorimetric signals, respectively, which were captured by the unmodified cellphone camera directly. From the cellphone images, quantification of RNA concentration was achieved, after image processing and digital counting [49]. Damhorst et al. developed an integrated assay with a microfluidic blood lysis module and a microfluidic chip for real-time, quantitative measurement of amplified DNA from reverse transcription. They applied this system in the detection of HIV from blood samples [47]. Similar real-time analysis was also conducted inside a Thermos cup, which holds the heat source, a microfluidic chip, and optical components (Fig. 48.4B) [53]. Quantum dot barcode has also been applied in the detection of RNA of HIV and hepatitis B virus (HBV) on the smartphone (Fig. 48.4C). In this study, different capturing DNA oligonucleotides were conjugated to the barcode, as biorecognition elements for target DNA. In the presence of target DNA, red-fluorescence labeled detection DNA, will bind with target DNA to form a sandwich structure. Five different barcodes loaded with different capture DNAs,

Smartphone-based clinical diagnostics Chapter | 48

were immobilized on the glass slide with microwells. Three were used for DNA detection, and two for the positive and negative control, respectively. The first smartphone image was taken to identify the positions of all five color barcodes. By using different filters for barcodes, five more images were then taken, and used for determining the presence of target DNA. Multiplexed detection of HIV, HBV, and HCV was achieved with this smartphonesupported biosensor [54]. In another example of multiplexed detection of Zika and Chikungunya viruses (Fig. 48.4D), quenching of unincorporated amplification signal reporters (QUASR) technique, was used to produce signals. A Bluetooth microcontroller was used to control LED excitation light and the isothermal hot plate. This platform can handle blood, urine, or saliva sample directly, without lysis procedure [55]. Energy consumption is an important issue in field applications of nucleic acid amplification and detection, especially when a heat plate is needed. Jiang et al. designed a field-deployable PCR system, powered by a solar cell. They also tested the system in the detection of Kaposi’s sarcoma herpesvirus (KSHV) [56]. D3 assay has been applied in the detection of human papillomavirus, from cervical specimen [20]. In another example, Mancuso and his colleagues designed a cellphone accessory, to measure the concentration of KSHV from biosamples, using AuNP-based colorimetric method [57].

501

Imaging and length measurement of fluorescently labeled single DNA molecules has also been achieved on a smartphone, using a lightweight, cost-effective imaging attachment. This fluorescent microscopy device provides a new approach for DNA-associated disease diagnostics, and DNA-protein interaction study, by direct visualization of single DNA strands on a portable platform [18].

Detection and imaging of bacteria and microbes Bacteria and parasites are pathogens for many infectious diseases. In the developing world, tuberculosis, malaria, and filarial parasite diseases remain to be major public health issues. Optical microscopy is an effective way in the detection of pathogen and diagnosis of these diseases. A portable, easy-to-use imaging platform would be helpful in the disease control and evaluation of treatment in remote areas, given the limited healthcare infrastructure and trained personnel present. Both smartphone-based microscopic and analytical methods have been adopted in the detection of pathogenic bacteria and parasite. Table 48.4 summarizes recent examples of clinical-related detection and identification of pathogenic bacteria and parasites on smartphonebased devices. Detection and imaging of bacteria on mobile phone devices have been explored in the last few years. As early

FIGURE 48.4 Nucleic acid detection on smartphones. (A). Digital LAMP assay with smartphone readout [48]. (B) SmartCup for portable nucleic acid amplification and detection [53]. (C) Multiplexed detection of infectious disease DNA on a smartphone with quantum dot barcode [54]. (D) Virus RNA detection on a smartphone platform [55]. Reproduced with permission from the references mentioned above. Copyright 2013 American Chemical Society, 2016 Elsevier and 2015 American Chemical Society.

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TABLE 48.4 Examples of pathogenic bacteria and parasites detection on smartphone platforms. Bacteria/Parasite

Sample

Method of detection

Results

References

Plasmodium falciparum Mycobacterium tuberculosis

Blood cell, sputum

Bright-field imaging Fluorescent imaging

Resolution ¼ 1.2 mm

[9]

Bacillus anthracis

e

Bright-field imaging

Range: 50e5000 spores in 3e5 h.

[58]

Escherichia coli

Blood serum

AuNP-base colorimetric

LOD ¼ 8 CFU/mL

[59]

M. tuberculosis

e

AuNP-based colorimetric

Time ¼ 65 min LOD ¼ 10 ug/mL

[60]

E. coli Neisseria gonorrhoeae

Urine

Immunoagglutination assay

LOD ¼ 10 CFU/mL for both bacteria

[61]

Loa loa, filarial parasites

Blood

Bright-field video

100% sensitivity 94% specificity

[62]

P. falciparum

Blood

Immunoassay

LOD ¼ 20.6 par./mL

[63]

Soil-transmitted helminth infection

Urine

Bright-field imaging

e

[64e66]

Schistosoma haematobium eggs Schistosoma mansoni eggs

Stool and urine

Bright-field microscopy

S. mansoni: Sensitivity ¼ 50% specificity ¼ 99.5% S. haematobium: Sensitivity ¼ 35.6% Specificity ¼ 100%

[79]

Strongyle eggs

Feces

Fluorescent imaging

LOD ¼ 50 EPG (egg per gram of feces)

[67]

Giardia lamblia cysts

Water

Fluorescence

LOD ¼ w12 cysts/10 mL

[68]

as 2009, bright-field and fluorescent microscopy of bacteria and parasite-infected blood cells have been demonstrated on a Nokia N73 phone equipped with a microscope eyepiece and an objective [9]. Smartphone-based microscopy was combined with microfluidic incubation device to identify spores of B. anthracis. Spores were allowed for germination in the incubation chamber. Bacterial filaments were then trapped in a fine filter for optical imaging during aspiration [58]. Paper-based microfluidic assays have been applied in bacterial detection as well. In the cellulose paperbased assay, signals were produced when the nanoparticles aggregated in the existence of target bacteria, causing a change in the color. With this assay, E. coli was detected and differentiated from S. aureus, another common bacterial pathogen [59]. Similarly, An AuNP-based paper platform for the detection of tuberculosis pathogen was also applied in the detection of M. tuberculosis on the smartphone [60]. Cho et al. detected E. coli and Neisseria gonorrhoeae by bright-field imaging of an immuneagglutination assay. After filtered by the microfluidic paper device, target bacterial antigen in the urine sample induced coagulation of antibody-conjugated particles, which can be captured by the smartphone camera [61]. Detection of parasites was also carried out on smartphones. By capturing videos of blood sample flow in a thin

imaging chamber, filarial parasite Loa loa was identified on a smartphone microscope (Fig. 48.5A), namely CellScope. Differential images between each recorded video frame and a time-averaged frame were calculated, and parasites were identified in those differential images. A microcontroller was used to control the sample flow and illumination. The results were finally displayed directly in an iOS app [62]. In another example, with an unmodified mobile phone and a commercially available immunoassay kit, malarial parasites were detected on the smartphone. This simple procedure can be easily standardized, allowing rapid training and easy implementation in resource-limited areas [63]. Smartphone devices for the diagnosis of soil-transmitted helminth diseases have been made available. In these studies, brightfield imaging was carried out on smartphones to observe Schistosoma haematobium after simple processing of stool or urine samples [64e66]. Parasite eggs from fecal samples could be counted on a smartphone after labeled with fluorescent dyes (Fig. 48.5B). Due to the different size of eggs and resulted different fluorescent intensities, the eggs of two parasites, strongyle and ascarid, can be discriminated on the cellphone, based on the intensity threshold set in the mobile phone app [67]. Detection of water-borne pathogens, especially parasites, is an important way of controlling the spread of some parasite diseases. A smartphone fluorescence

Smartphone-based clinical diagnostics Chapter | 48

503

FIGURE 48.5 Parasite and microbe detection on smartphone platforms. (A) Quantification of blood-borne filarial Parasites on a smartphone microscope [62]. (B) Counting of fluorescently labeled parasite eggs on the smartphone [67]. (C) Detection and quantification of Giardia lamblia cysts on a smartphone fluorescent microscope [68]. Reproduced with permission from the references mentioned above. Copyright 2015 American Association for the Advancement of Science, 2016 Australian Society for Parasitology and 2014 Royal Society of Chemistry.

microscopy was used to detect fluorescently labeled Giardia lamblia cysts from water samples (Fig. 48.5C). In this application, the images were sent by the mobile phone to the server and analyzed with a machine-learning algorithm to obtain accurate counts of cysts [68].

Detection and imaging of human cells Imaging, counting, and analysis of human cells can provide rich information in disease diagnosis. Labeling of specific antigens on the cells can also provide molecular information like protein expression, which is important in identification, risk management, and treatment of disease. A few examples of cell imaging, counting, and analysis on the smartphone are summarized in Table 48.5. Smartphone microscopes have shown great potential in cancer diagnosis via single-cell imaging or counting. The smartphone-based D3 (digital diffraction detection) platform has been demonstrated for cellular imaging of immunolabeled cancer cells [20]. Another imaging modality, autofluorescence (AF) imaging, was applied in

the detection of basal cell carcinoma. From the difference of AF intensity, malignant tissue can be identified from normal healthy tissue. This simple method can be applied in the primary evaluation of suspicious skin tissue [69]. Recently, a fluorescent imaging cytometry platform installed on a smartphone was reported for counting and magnetic separation of cancer cells. Stained breast cancer cells were magnetically levitated, imaged, and counted. By using different staining procedure and filters, different cells can be distinguished. Combination of magnetic levitation and fluorescence imaging also allowed spatial separation and imaging of different cells due to their difference in density, and therefore, levitation height [70]. Imaging and quantitative analysis of blood cells provides valuable information on the morphology and population of blood cells, which is essential in the diagnosis of diseases, such as malaria, sickle cell anemia, and HIV infection. Proof-of-concept work was reported recently, where white blood cells in a microfluidic chamber were counted on a smartphone-based fluorescent imaging cytometer. In this work, a fluorescence imaging device was

504 PART | III Hospital, managed care and public health applications

TABLE 48.5 Representative cases on cellular detection using mobile phone detectors. Cell

Sample

Method of detection

Results

References

Breast cancer cell Cervical cancer cell

e

D3 (digital diffraction analysis) platform

e

[20]

Basal cell carcinoma

e

Autofluorescence imaging

e

[69]

Cancer cell imaging

e

Fluorescence imaging

LOD ¼ 680 cells/mL Counting accuracy: R2 ¼ 0.95e0.96

[70]

Blood cell counting

Blood

Fluorescence imaging Bright-field imaging Colorimetric analysis

WBC: R2 ¼ 0.98, 2.9  104 cells/mL bias RBS: R2 ¼ 0.98, 230 cells/mL bias Hb: R2 ¼ 0.92 CC, 0.036 g/dL bias

[72]

Sickle cell detection

Blood

Bright-field imaging, magnetic levitation

Statistically significant difference between the control group and sickle cell anemia sample.

[74]

CD4þ T cell count

Blood

Bright-field imaging

Accuracy: 93.3% (threshold ¼ 200 cells/mL), 96.6% (threshold ¼ 500 cells/mL)

[75]

CD4þ T cell count

Blood

Colorimetric ELISA

Accuracy: 97% (threshold ¼ 350 cells/mL)

[76]

Sperm

Semen

Bright-field video

Sensitivity ¼ 87.5% specificity ¼ 90.9%

[77]

Sperm

Semen

Bright-field video

Sperm concentration criterion: sensitivity ¼ 95.83% specificity ¼ 97.10% accuracy ¼ 96.77% Sperm motility criterion: sensitivity ¼ 95.83% specificity ¼ 98.04% accuracy ¼ 97.31%

[78]

built on a smartphone using an LED as a light source and a plastic color film as an inexpensive emission filter. Blood cell sample labeled with fluorescent dyes was continuously injected into a microfluidic chamber using a syringe pump. The microfluidic chip was placed in front of the smartphone imaging system. Video clips were taken to record cell flow through the chamber. The JPEG images were then extracted for processing and cell counting [71]. In another smartphone device, blood cells were analyzed by an imaging cytometry platform (Fig. 48.6A) which is capable of the quantification of white blood cell (WBC) counts, red blood cell (RBC) counts, and hemoglobin (Hb) concentrations [72]. Sickle cell disease or sickle cell anemia remains to be a major public health issue in Africa. As a hematologic disease, this disease can be diagnosed directly by a blood test. A smartphone was used in the imaging and identification of sickle cells from blood smear samples [73]. In a different example, by using magnetic levitation, sickle cell anemia sample can be distinguished from normal RBC samples with a smartphone. A difference in the density between sickle cells and normal RBCs resulted in the different height distribution pattern of sickle cell anemia blood sample and normal

blood sample under magnetic levitation. This effect can be further magnified by induced dehydration of RBC. As a result, sickle cell anemia genotype (SS) can be distinguished from normal blood cell with a statistically significant difference [74]. The number of a specific kind of WBC called CD4þ T cells gives important information in the diagnosis of HIV infection. The CD4þ T cell count, or CD4 test, is traditionally carried out on a benchtop flow cytometer. As an alternative approach, CD4 cells were imaged and counted on a smartphone platform (Fig. 48.6B). To do that, antibodies were conjugated onto the chip surface to capture CD4þ cells. The CD4þ cell concentration can then be used to identify HIV-positive and negative samples using the threshold values suggested by WHO [75]. Wang et al. developed a colorimetric ELISA system for CD4þ cell counts on the smartphone. Magnetic beads conjugated with anti-CD4 antibody were used for capturing CD4þ cells. Under the actuation of a magnet, the cells labeled with beads were moved between chambers for washing, secondary antibody targetting, and color development. Both mobile and desktop apps were developed to quantify CD4þ cell counts by measuring the color change of the assay [76].

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505

FIGURE 48.6 Cell imaging and analysis on smartphone platforms. (A) A cost-effective blood analysis platform on a smartphone for WBC, RBC, and Hb quantification [72]. (B) CD4þ cell counting on a smartphone device [75]. (C) Smartphone device for POC semen analysis [78]. Reproduced with permission from the references mentioned above. Copyright 2013 Royal Society of Chemistry, 2017 Royal Society of Chemistry and 2017 American Association for the Advancement of Science.

Smartphones have also been applied in the analysis of semen and diagnosis of male infertility. A simple ball lensbased smartphone microscope was used in the semen analysis. By analyzing the videos, the numbers and motilities of spermatozoa were calculated on three smartphones with different models for comparison [77]. Similarly, in the work by Kanakasabapathy et al., bright-field videos of spermatozoa were taken under the magnification of two aspheric lenses. Semen samples were drawn into the counting chamber by a manual vacuum pump. From the smartphone-captured videos, the concentration and motility of sperm can be calculated by an Android application on the phone (Fig. 48.6C). Untrained users can then test semen quality at home, conveniently with smartphones [78].

Summary and outlook The cost-effectiveness and portability of smartphone devices have enabled various diagnostic applications in the resource-limited or field settings. In the context of global health challenges, simple, easy-to-use, and low-cost smartphone devices have found increased applications in

both developed and developing countries for personalized health monitoring and disease diagnosis. Currently, adding optical attachments onto smartphones is still the mainstream approach of designing smartphonebased detection and diagnosis systems. Highly customizable and compact optical attachments provide the opportunity of using a single smartphone in multiple scenarios to perform a series of detection by switching the attachments. In the context of sample preparation and liquid handling, microfluidic devices have been extensively combined with smartphones. With the integration of microfluidic devices, the sample volume can be reduced to microliters, and the sample processing procedures can be significantly simplified. Power-free and vacuum-driven sample handling systems on smartphone diagnostic tools have made another step toward real field use. Subsequent data processing after mobile sensing and imaging can be realized either directly on the phone or a cloud server. Remote data sharing can be easily available between patients and clinicians with connected mobile phone devices. In the meanwhile, parallel processing of high-volume data can help clinicians, and

506 PART | III Hospital, managed care and public health applications

other healthcare specialists make more accurate decisions on diagnostics and therapeutics. Based on the current achievements in smartphone-based detection and diagnostics, several areas can be envisioned in the future research and development. To move forward toward clinical applications, more closely collaborations between engineers and clinicians will be expected, which allow to better identify the need and validate the POC systems under more rigorous conditions. Multiplexed, high-throughput analysis of multiple biomarkers is another field in smartphone detection diagnostics. Novel designs of assays are needed to achieve this goal, including efforts on the design of high-throughput microfluidic systems for parallel sample preparation and assay reactions. More robust and user-friendly imaging and sensing system would be needed for field applications in those areas with few trained professionals. Big-data produced by the massive smartphone devices can better assist clinicians and policy makers in analyzing and tracking emerging public health issues, such as the outbreak of new infectious diseases. Finally, machine learning and artificial intelligence is a promising area in data processing of smartphone images for faster and more accurate result analysis. All these efforts will be made toward designing more powerful and precise smartphone diagnostic tools for POC and field applications.

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