Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform

A computer vision enhanced smart phone platform for microfluidic urine glucometry

Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

Development of a Quantitative Digital Urinalysis Tool for Detection of Nitrite, Protein, Creatinine, and pH

This paper presents a cost-effective, quantitative, point-of-care solution for urinalysis screening, specifically targeting nitrite, protein, creatinine, and pH in urine samples. Detecting nitrite is crucial for the early identification of urinary tract infections (UTIs), while regularly measuring urinary protein-to-creatinine (UPC) ratios aids in managing kidney health. To address these needs, we developed a portable, transmission-based colorimeter using readily available components, controllable via a smartphone application through Bluetooth. Multiple colorimetric detection strategies for each analyte were identified and tested for sensitivity, specificity, and stability in a salt buffer, artificial urine, and human urine. The colorimeter successfully detected all analytes within their clinically relevant ranges: nitrite (6.25-200 µM), protein (2-1024 mg/dL), creatinine (2-1024 mg/dL), and pH (5.0-8.0). The introduction of quantitative protein and creatinine detection, and a calculated urinary protein-to-creatinine (UPC) ratio at the point-of-care, represents a significant advancement, allowing patients with proteinuria to monitor their condition without frequent lab visits. Furthermore, the colorimeter provides versatile data storage options, facilitating local storage on mobile devices or in the cloud. The paper further details the setup of the colorimeter's secure connection to a cloud-based environment, and the visualization of time-series analyte measurements in a web-based dashboard.

Portable dual-channel blood enzyme analyzer for point-of-care liver function detection

Because the liver is an important metabolic center in the human body, the reliability and timeliness of chronic liver disease diagnosis are particularly important. Alanine aminotransferase and aspartate transaminase are the two most important liver function indicators, and their test results are crucial in the diagnosis of liver diseases. However, the simultaneous detection of these two indicators is currently restricted by the need for expensive equipment and complicated detection processes. This study proposes a portable dual-channel blood enzyme analyzer (BEA) for point-of-care-testing. The device uses photometric reflectance to quantify the enzyme concentration by evaluating the reflected light intensity. The BEA also precisely controls and maintains the temperature at 37 °C ± 0.1 °C in the dual-channel assay. We assessed the responses of this system within a clinically relevant range by testing blood samples from a local hospital. The test verified that BEA for ALT and AST achieved a detection limit of 3.5 U L-1 and 4 U L-1, detection range of 4-350 U L-1 and 4-250 U L-1, coefficients of variation (CV) that were both less than 10%, and a linear correlation coefficient of 0.9827 and 0.9714 compared with a high-precision clinical biochemistry analyzer (Roche Cobas C702), respectively. We realized remote data analysis and storage through connection with smartphones, which can be applied to remote diagnostics and preventative personal disease management. Therefore, BEA has broad application prospects in the future internet of medical things.

Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test

Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.

Artificial Intelligence Applications in Clinical Chemistry

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

Development of a portable toolkit to diagnose coral thermal stress

Coral bleaching, precipitated by the expulsion of the algal symbionts that provide colonies with fixed carbon is a global threat to reef survival. To protect corals from anthropogenic stress, portable tools are needed to detect and diagnose stress syndromes and assess population health prior to extensive bleaching. Here, medical grade Urinalysis strips, used to detect an array of disease markers in humans, were tested on the lab stressed Hawaiian coral species, Montipora capitata (stress resistant) and Pocillopora acuta (stress sensitive), as well as samples from nature that also included Porites compressa. Of the 10 diagnostic reagent tests on these strips, two appear most applicable to corals: ketone and leukocytes. The test strip results from M. capitata were explored using existing transcriptomic data from the same samples and provided evidence of the stress syndromes detected by the strips. We designed a 3D printed smartphone holder and image processing software for field analysis of test strips (TestStripDX) and devised a simple strategy to generate color scores for corals (reflecting extent of bleaching) using a smartphone camera (CoralDX). Our approaches provide field deployable methods, that can be improved in the future (e.g., coral-specific stress test strips) to assess reef health using inexpensive tools and freely available software.

A free customizable tool for easy integration of microfluidics and smartphones

The integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.

Toward a Quantitative Colorimeter for Point-of-Care Nitrite Detection

This paper reports on a low-cost, quantitative, point-of-care solution for the early detection of nitrite, a common biomarker for urinary tract infections (UTIs). In a healthy individual, nitrite is not found in the urine. However, a subject with a suspected UTI will produce nitrite in their urine since the bacteria present will convert nitrate into nitrite. Traditionally, nitrite is monitored by urinary dipsticks that are either read by eye or using a reflectance spectrophotometer. Both methods provide a semiquantitative positive or negative result at best. In this paper, we described a novel, affordable, portable transmission-based colorimeter for the quantitative measurement of nitrite. A unique permutation of the Griess reaction was optimized for the clinical detection of nitrite in urine and is reported. By using nitrite spiked in a salt buffer, artificial, and human urine samples, the performance of the colorimeter was evaluated against dipsticks read using two commercial dipstick analyzers, Urisys 1100 (Roche Diagnostics) and Clinitek Status+ (Siemens Medical Solutions). The colorimeter was able to detect the clinically relevant range of nitrite from 0.78 to 200 μM in a salt buffer. The detection limit in artificial urine was determined as 1.6 μM, which is ∼16× more sensitive than commercial dipstick reflectance analyzers, enabling the possibility for earlier detection of urinary infections. The colorimeter is assembled using off-the-shelf components (<$80) and controlled by a smartphone application via low-energy bluetooth. It has a built-in color correction algorithm and is designed to enable for a turbidity correction in samples containing bacteria or other cellular debris as well. The mobile application can display the nitrite concentration for a single sample or display the results over a period of time. Tracking urinalysis results longitudinally can help identify trends such as increases in nitrite concentrations over an individual's baseline and identify possible infections earlier. While the detection of nitrite was showcased here, this portable analyzer can be expanded to other colorimetric-based chemistries to detect a panel of biomarkers, which can improve the overall sensitivity and specificity of the desired assay.

Cyclic swelling enabled, electrically conductive 3D porous structures for microfluidic urinalysis devices

Urinalysis is a simple and non-invasive approach for the diagnosis and monitoring of organ health and also is often used as a facile technique in assessment of substance abuse. However, quantitative urinalysis is predominantly limited to clinical laboratories. Here, we present an electrical sensing based, reusable, cellular microfluidic device that offers a fast urinalysis through quantitative reading of the electrical signals. The spatial soft porous scaffolds decorated with electrically conductive multiwalled carbon nanotubes that are capable of physically interacting with biomarkers in urine are developed through a cyclic swelling/absorption process of soft materials and are utilized to manufacture the cellular microfluidic device. The sensing capability, sensitivity and reusability (via sunlight exposure) of the device to monitor red blood cells, Escherichia coli, and albumin are systemically demonstrated by programming mechanical deformation of porous scaffolds. Ex vivo experiments in disease mouse models confirm the diagnosis robustness of the device in comparable results with existing biochemical tests. The full integration of electrically conductive nanomaterials into soft scaffolds provides a foundation for devising bioelectronic devices with mechanically programmable microfluidic features in a low-cost manner, with broad applications for rapid disease diagnoses through body fluid.

Toilet-based continuous health monitoring using urine

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

Applications, challenges, and needs for employing synthetic biology beyond the lab

Synthetic biology holds great promise for addressing global needs. However, most current developments are not immediately translatable to 'outside-the-lab' scenarios that differ from controlled laboratory settings. Challenges include enabling long-term storage stability as well as operating in resource-limited and off-the-grid scenarios using autonomous function. Here we analyze recent advances in developing synthetic biological platforms for outside-the-lab scenarios with a focus on three major application spaces: bioproduction, biosensing, and closed-loop therapeutic and probiotic delivery. Across the Perspective, we highlight recent advances, areas for further development, possibilities for future applications, and the needs for innovation at the interface of other disciplines.

Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays

Purpose

The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.

Design/methodology/approach

The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.

Findings

This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.

Originality/value

To the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Urine biopsy technologies: Cancer and beyond

Since the discovery of circulating tumor cells in 1869, technological advances in the study of biomarkers from liquid biopsy have made it possible to diagnose disease in a less invasive way. Although blood-based liquid biopsy has been used extensively for the detection of solid tumors and immune diseases, the potential of urine-based liquid biopsy has not been fully explored. Advancements in technologies for the harvesting and analysis of biomarkers are providing new opportunities for the characterization of other disease types. Liquid biopsy markers such as exfoliated bladder cancer cells, cell-free DNA (cfDNA), and exosomes have the potential to change the nature of disease management and care, as they allow a cost-effective and convenient mode of patient monitoring throughout treatment. In this review, we addressed the advancement of research in the field of disease detection for the key liquid biopsy markers such as cancer cells, cfDNA, and exosomes, with an emphasis on urine-based liquid biopsy. First, we highlighted key technologies that were widely available and used extensively for clinical urine sample analysis. Next, we presented recent technological developments in cell and genetic research, with implications for the detection of other types of diseases, besides cancer. We then concluded with some discussions on these areas, emphasizing the role of microfluidics and artificial intelligence in advancing point-of-care applications. We believe that the benefits of urine biopsy provide diagnostic development potential, which will pave opportunities for new ways to guide treatment selections and facilitate precision disease therapies.

Integrated Digital Microfluidic Platform for Colorimetric Sensing of Nitrite

In this paper, a palm-size digital microfluidic (DMF) platform integrated with colorimetric analysis was developed for quantifying the concentration of nitrite. To realize the on-chip repeatable colorimetric analysis, a novel printed circuit board (PCB)-based DMF chip was designed with an embedded aperture on the actuator electrode, forming a vertical light path for online measurement of the droplets. The capabilities of the DMF platform enable automatic manipulation of microliter-level droplets to implement Griess assay without the use of external systems such as syringe, pump, or valve, which provides the benefits including high flexibility, portability, miniature size, and low cost. Results indicated the characteristics of good linearity (R 2 = 0.9974), the ignorable crosstalk for reusability, and the limit of detection (LOD) of nitrite as low as 5 μg/L. Furthermore, the presented platform was successfully applied to determine nitrite levels in food products with reliable results and satisfactory recoveries. This integrated DMF platform can be a promising new tool for a wide range of applications involving step-by-step solution mixing and optical detection in environmental monitoring, food safety analysis, and point-of-care testing.

Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays

Urinalysis is a commonly utilized laboratory test, and analysis of urine has been studied and used since ancient times. Urine contains a wide array of metabolites that can provide information regarding the current physiologic state of the body and clinical manifestations of disease. In this review, we discuss the mechanics of the dry chemistry component of the urine dipstick such as the reaction principles underlying various assays and potential effects of collection and storage on results. Additionally, we discuss the benefits and limitations of the urine dipstick as it pertains to its use as a low-cost tool in point-of-care settings and the reasoning for a lack of its use as a broad screening tool.

Lab on a Chip for the Colorimetric Determination of Nitrite in Processed Meat Products in the Jordanian Market

Nitrite and Nitrate have been used extensively as additives in various meat products to enhance flavor, color, and to preserve the meat from the bacterial growth. High concentrations of nitrite can threat human health since several studies in the literature claim that nitrite is associated with cancer incidences, leukemia, and brain tumors. Therefore, it is vital to measure the nitrite concentrations in processed meat products. In this study, an in-lab miniaturized photometric detection system is fabricated to inspect the nitrite concentration in processed meat products in Jordan. The analytical performance of nitrite detection is evaluated based on three key statistical parameters; linearity, limit of detection, and limit of quantitation. Respectively, for the fabricated system, the three values are found to be equal to 0.995, 1.24 × 10-2 ppm, and 4.12 × 10-2 ppm. Adherence to Beer's law is found over the investigated range from 2.63 ppm to 96.0 ppm. The developed system is utilized for photometric detection of nitrite in processed meat products available in the Jordanian market like pastrami, salami, and corned beef. In all of the analyzed samples, the nitrite content is found to be lower than 150 ppm, which represents the maximum allowable nitrite limit.

Progress in Automated Urinalysis

New technological advances have paved the way for significant progress in automated urinalysis. Quantitative reading of urinary test strips using reflectometry has become possible, while complementary metal oxide semiconductor (CMOS) technology has enhanced analytical sensitivity and shown promise in microalbuminuria testing. Microscopy-based urine particle analysis has greatly progressed over the past decades, enabling high throughput in clinical laboratories. Urinary flow cytometry is an alternative for automated microscopy, and more thorough analysis of flow cytometric data has enabled rapid differentiation of urinary microorganisms. Integration of dilution parameters (e.g., creatinine, specific gravity, and conductivity) in urine test strip readers and urine particle flow cytometers enables correction for urinary dilution, which improves result interpretation. Automated urinalysis can be used for urinary tract screening and for diagnosing and monitoring a broad variety of nephrological and urological conditions; newer applications show promising results for early detection of urothelial cancer. Concomitantly, the introduction of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has enabled fast identification of urinary pathogens. Automation and workflow simplification have led to mechanical integration of test strip readers and particle analysis in urinalysis. As the information obtained by urinalysis is complex, the introduction of expert systems may further reduce analytical errors and improve the quality of sediment and test strip analysis. With the introduction of laboratory-on-a-chip approaches and the use of microfluidics, new affordable applications for quantitative urinalysis and readout on cell phones may become available. In this review, we present the main recent developments in automated urinalysis and future perspectives.

Color Space Transformation-Based Smartphone Algorithm for Colorimetric Urinalysis

Urine strips are widely applied for rapid analysis of various indexes of urine for clinical examinations. The tests mainly rely on the application of a urine analyzer, which suffers several drawbacks and cannot meet the requirements of point-of-care testing (POCT). The integration of a smartphone with a biosensor has recently attracted great attention. We herein propose a human vision-based smartphone algorithm for colorimetric analysis of various urine indexes. A CIEDE2000 formula in CIELab color space is applied for the evaluation of color difference, which may greatly improve the analytical performances of urine strips. The proposed algorithm also possesses merits such as good accuracy, quantitative analysis, and limited calculation task, which is suitable for the application with smartphone platform. Experimental results demonstrate that the proposed method shows excellent reliability compared with the urine analyzer and some other algorithms. In addition, human real samples are successfully analyzed with excellent accuracy. Therefore, this work provides a convenient colorimetric tool for POCT urine analysis.

Low-power, low-cost urinalysis system with integrated dipstick evaluation and microscopic analysis

We introduce a coupled dipstick and microscopy device for analyzing urine samples. The device is capable of accurately assessing urine dipstick results while simultaneously imaging the microscopic contents within the sample. We introduce a long working distance, cellphone-based microscope in combination with an oblique illumination scheme to accurately visualize and quantify particles within the urine sample. To facilitate accurate quantification, we couple the imaging set-up with a power-free filtration system. The proposed device is reusable, low-cost, and requires very little power. We show that results obtained with the proposed device and custom-built app are consistent with those obtained with the standard clinical protocol, suggesting the potential clinical utility of the device.

New and developing diagnostic technologies for urinary tract infections

Timely and accurate identification and determination of the antimicrobial susceptibility of uropathogens is central to the management of UTIs. Urine dipsticks are fast and amenable to point-of-care testing, but do not have adequate diagnostic accuracy or provide microbiological diagnosis. Urine culture with antimicrobial susceptibility testing takes 2-3 days and requires a clinical laboratory. The common use of empirical antibiotics has contributed to the rise of multidrug-resistant organisms, reducing treatment options and increasing costs. In addition to improved antimicrobial stewardship and the development of new antimicrobials, novel diagnostics are needed for timely microbial identification and determination of antimicrobial susceptibilities. New diagnostic platforms, including nucleic acid tests and mass spectrometry, have been approved for clinical use and have improved the speed and accuracy of pathogen identification from primary cultures. Optimization for direct urine testing would reduce the time to diagnosis, yet these technologies do not provide comprehensive information on antimicrobial susceptibility. Emerging technologies including biosensors, microfluidics, and other integrated platforms could improve UTI diagnosis via direct pathogen detection from urine samples, rapid antimicrobial susceptibility testing, and point-of-care testing. Successful development and implementation of these technologies has the potential to usher in an era of precision medicine to improve patient care and public health.

Toward practical application of paper-based microfluidics for medical diagnostics: state-of-the-art and challenges

Microfluidic paper-based analytical devices (μPADs) have emerged as a promising diagnostic platform a decade ago. In contrast to highly active academic developments, their entry into real-life applications is still very limited. This discrepancy is attributed to the gap between research developments and their practical utility, particularly in the aspects of operational simplicity, long-term stability of devices, and associated equipment. On the basis of these backgrounds, this review attempts to: 1) identify the reasons for success of paper-based devices already in the market, 2) describe the current status and remaining issues of μPADs in terms of operational complexity, signal interpretation approaches, and storage stability, and 3) discuss the possibility of mass production based on established manufacturing technologies. Finally, the state-of-the-art in commercialisation of μPADs is discussed, and the "upgrades" required from a laboratory-based prototype to an end user device are demonstrated on a specific example.