Multifunctional hand-held sensor using electronic components embedded in smartphones for quick PCR screening

Smartphone-Based Hand Function Assessment: Systematic Review

BACKGROUND

Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment.

OBJECTIVE

This review aims to evaluate existing studies on hand function evaluation using smartphones.

METHODS

An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions.

RESULTS

In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation.

CONCLUSIONS

This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.

A smartphone-based centrifugal mHealth platform implementing hollow daisy-shaped quick response chip for hematocrit measurement

Due to the ever-increasing challenge of emerging and reemerging infections on global health, the development of POCT tools has been propelled. However, conventional point-of-care testing methods suffer from several limitations, including cumbersome operation, long detection times, and low accuracy, which hamper their widespread application. Compared to traditional disease diagnostic equipment, mobile health platforms offer several advantages, including portability, ease of operation, and automated analysis of detection results through recognition algorithms. Consequently, they hold great promise for the future. Here, we developed a smartphone-based centrifugal mHealth platform implementing daisy-shaped quick response chip for hematocrit measurement. The centrifugal microfluidic chip is combined with a smartphone through a back-clip-on mobile phone adapter whose control circuit is designed with low power consumption to enable the platform to operate without requiring a high-power source that is inconvenient to carry, thereby achieving the goal of portability. Concurrently, we designed a quick response chip featuring a unique hollow daisy structure that is in line with the properties of hematocrit detection. The distinctive configuration of the chip enables adequate centrifugal force to be supplied for hematocrit detection. Additionally, our customized quick response code recognition algorithm is able to recognize this chip, facilitating non-experts in performing hematocrit intelligent recognition with their smartphones.

Sensitive Methods to Detect Single-Stranded Nucleic Acids of Food Pathogens Based on Cell-Free Protein Synthesis and Retroreflection Signal Detection

Cell-free protein synthesis (CFPS) has recently gained considerable attention as a new platform for developing methods to detect various molecules, ranging from small chemicals to biological macromolecules. Retroreflection has been used as an alternative signal to develop analytical methods because it can be detected by using a simple instrument comprising a white light source and a camera. Here, we report a novel reporter protein that couples the capability of CFPS and the simplicity of retroreflection signal detection. The design of the reporter was based on two pairs of protein-peptide interactions, SpyCatcher003-SpyTag003 and MDM2-PMI(N8A). MDM2-MDM2-SpyCatcher003 was decided as the reporter protein, and the two peptides, SpyTag003 and PMI(N8A), were immobilized on the surfaces of retroreflective Janus particles and microfluidic chips, respectively. The developed retroreflection signal detection system was combined with a previously reported CFPS reaction that can transduce the presence of a single-stranded nucleic acid into protein synthesis. The resulting methods were applied to detect 16S rRNAs of several foodborne pathogens. Concentration-dependent relationships were observed over a range of 10° fM to 102 pM, with the limits of detection being single-digit femtomolar concentrations. Considering the designability of the CFPS system for other targets, the retroreflection signal detection method will enable the development of novel methods to detect various molecules.

Coupling hCG-based protease sensors with a commercial pregnancy test strip for simple analyses of protease activities

Proteases play an essential role in many cellular processes, and consequently, abnormalities in their activities are related to various diseases. Methods have been developed to measure the activity of these enzymes, but most involve sophisticated instruments or complicated procedures, which hampers the development of a point-of-care test (POCT). Here, we propose a strategy for developing simple and sensitive methods to analyze protease activity using commercial pregnancy test strips that detect human chorionic gonadotropin (hCG). hCG was engineered to have site-specific conjugated biotin and a peptide sequence, which can be cleaved by a target protease, between hCG and biotin. hCG protein was immobilized on streptavidin-coated beads, resulting in a protease sensor. The hCG-immobilized beads were too large to flow through the membrane of the hCG test strip and yielded only one band in the control line. When the peptide linker was hydrolyzed by the target protease, hCG was released from the beads, and the signal appeared in both the control and test lines. Three protease sensors for matrix metalloproteinase-2, caspase-3, and thrombin were constructed by replacing the protease-cleavable peptide linker. The combination of the protease sensors and a commercial pregnancy strip enabled the specific detection of each protease in the picomolar range, with a 30-min incubation of the hCG-immobilized beads and samples. The modular design of the protease sensor and simple assay procedure will facilitate the development of POCTs for various protease disease markers.

Rapid Visible Detection of African Swine Fever Virus Using Hybridization Chain Reaction-Sensitized Magnetic Nanoclusters and Affinity Chromatography

African swine fever virus (ASFV) is a severe and persistent threat to the global swine industry. As there are no vaccines against ASFV, there is an immense need to develop easy-to-use, cost-effective, and rapid point-of-care (POC) diagnostic platforms to detect and prevent ASFV outbreaks. Here, a novel POC diagnostic system based on affinity column chromatography for the optical detection of ASFV is presented. This system employs an on-particle hairpin chain reaction to sensitize magnetic nanoclusters with long DNA strands in a target-selective manner, which is subsequently fed into a column chromatography device to produce quantitatively readable and colorimetric signals. The detection approach does not require expensive analytical apparatus or immobile instrumentation. The system can detect five genes constituting the ASFV whole genome with a detection limit of ≈19.8 pm in swine serum within 30 min at laboratory room temperature. With an additional pre-amplification step using polymerase chain reaction (PCR), the assay is successfully applied to detect the presence of ASFV in 30 suspected swine samples with 100% sensitivity and specificity, similar to quantitative PCR. Thus, this simple, inexpensive, portable, robust, and customizable platform for the early detection of ASFV can facilitate the timely surveillance and implementation of control measures.

Digital microfluidic platform assembled into a home-made studio for sample preparation and colorimetric sensing of S-nitrosocysteine

Digital microfluidics (DMF) is a versatile lab-on-a-chip platform that allows integration with several types of sensors and detection techniques, including colorimetric sensors. Here, we propose, for the first time, the integration of DMF chips into a mini studio containing a 3D-printed holder with previously fixed UV-LEDs to promote sample degradation on the chip surface before a complete analytical procedure involving reagent mixture, colorimetric reaction, and detection through a webcam integrated on the equipment. As a proof-of-concept, the feasibility of the integrated system was successfully through the indirect analysis of S-nitrosocysteine (CySNO) in biological samples. For this purpose, UV-LEDs were explored to perform the photolytic cleavage of CySNO, thus generating nitrite and subproducts directly on DMF chip. Nitrite was then colorimetrically detected based on a modified Griess reaction, in which reagents were prepared through a programable movement of droplets on DMF devices. The assembling and the experimental parameters were optimized, and the proposed integration exhibited a satisfactory correlation with the results acquired using a desktop scanner. Under the optimal experimental conditions, the obtained CySNO degradation to nitrite was 96%. Considering the analytical parameters, the proposed approach revealed linear behavior in the CySNO concentration range between 12.5 and 400 μmol L^-1 and a limit of detection equal to 2.8 μmol L^-1. Synthetic serum and human plasma samples were successfully analyzed, and the achieved results did not statistically differ from the data recorded by spectrophotometry at the confidence level of 95%, thus indicating the huge potential of the integration between DMF and mini studio to promote complete analysis of lowmolecular weight compounds.

Retroreflection-based sandwich type affinity sensing of isothermal gene amplification products for foodborne pathogen detection

Loop-mediated isothermal amplification (LAMP) is an outstanding method for molecular diagnostics, as the rapid, specific, and sensitive amplification of target genes is possible. However, it is necessary to measure fluorescence in the quantitative analysis of LAMP products, so a sophisticated optical setup is required. This study tried to develop a novel sensing method that can quantify target analytes with simple equipment, such as nonspectroscopic white light and a CMOS camera. To achieve this, a retroreflective Janus particle (RJP) as a probe and specially designed loop primers, fluorescein isothiocyanate (FITC)- and biotin-modified loop primers, were introduced into the LAMP system. By performing LAMP in the presence of designed primers, double-stranded amplicons possessing FITC and biotin labels at each end are generated in proportion to the quantity of the target pathogen. Using the anti-FITC antibody-modified sensing surface and streptavidin-conjugated RJP probes, the amplicons can be captured in sandwich-configuration and detected under nonspectroscopic conditions composed of white light and a camera. To confirm the feasibility of the sensing system, the invA gene of Salmonella was selected as the target. It was possible to quantitatively analyze the Salmonella concentration from 0 to 10⁶ colony-forming units, sufficiently covering the required detection range. In addition, quantitative analyses of pathogens in contaminated food sources, including milk and chicken meat, were successfully conducted with a limit of detection of 10 CFU.

A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings

The SARS-Coronavirus-2 (SARS-CoV-2) infectious disease, COVID-19, has spread rapidly, resulting in a global pandemic with significant mortality. The combination of early diagnosis via rapid screening, contact tracing, social distancing and quarantine has helped to control the pandemic. The absence of real time response and diagnosis is a crucial technology shortfall and is a key reason why current contact tracing methods are inadequate to control spread. In contrast, current information technology combined with a new generation of near-real time tests offers consumer-engaged smartphone-based "lab-in-a-phone" internet-of-things (IoT) connected devices that provide increased pandemic monitoring. This review brings together key aspects required to create an entire global diagnostic ecosystem. Cross-disciplinary understanding and integration of both mechanisms and technologies for effective detection, incidence mapping and disease containment in near real-time is summarized. Available measures to monitor and/or sterilize surfaces, next-generation laboratory and smartphone-based diagnostic approaches can be brought together and networked for instant global monitoring that informs Public Health policy. Cloud-based analysis enabling real-time mapping will enable future pandemic control, drive the suppression and elimination of disease spread, saving millions of lives globally. A new paradigm is introduced - scaled and multiple diagnostics for mapping and spreading of a pandemic rather than traditional accumulation of individual measurements. This can do away with the need for ultra-precise and ultra-accurate analysis by taking mass measurements that can relax tolerances and build resilience through networked analytics and informatics, the basis for novel swarm diagnostics. These include addressing ethical standards, local, national and international collaborative engagement, multidisciplinary and analytical measurements and standards, and data handling and storage protocols.

Review: Trends in point-of-care diagnosis for Escherichia coli O157:H7 in food and water

Escherichia coli O157:H7, a Shiga-producing E. coli is a major pathogenic E. coli strain which since the early 1980s has become a crucial food and water-borne pathogen. Several management strategies can be applied to control the spread of infection; however early diagnosis represents the optimum preventive strategy to minimize the infection. Therefore, it is crucial to detect this pathogen in a fast and efficient manner in order to reduce the morbidity and mortality. Currently used gold standard tests rely on culture and pre-enrichment of E. coli O157:H7 from the contaminated source; they are time consuming and laborious. Molecular methods such as polymerase chain reaction are sensitive; however, they require expensive instrumentation. Therefore, there is a requirement for Accurate, Sensitive, Specific, User friendly, Rapid, Equipment free and Deliverable (ASSURED) detection methods for use in the laboratory and in the field. Emerging technologies such as isothermal amplification methods, biosensors, surface enhanced Raman Spectroscopy, paper-based diagnostics and smartphone-based digital methods are recognized as new approaches in the field of E. coli O157:H7 diagnostics and are discussed in this review. Mobile PCR and CRISPR-Cas diagnostic platforms have been identified as new tools in E. coli O157:H7 POC diagnostics with the potential for implementation by industry. This review describes advances and progress in the field of E. coli O157:H7 diagnosis in the context of food and water industry. The focus is on emerging high throughput point-of-care (POC) E. coli O157:H7 diagnostics and the requirement for the transformation to service routine diagnostics in the food and water industry.

Recent Applications of Magnetic Nanoparticles in Food Analysis

Nanotechnology has become a topic of interest due to the outstanding advantages that the use of nanomaterials offers in many fields. Among them, magnetic nanoparticles (m-NPs) have been one of the most widely applied in recent years. In addition to the unique features of nanomaterials in general, which exclusively appear at nanoscale, these present magnetic or paramagnetic properties that result of great interest in many applications. In particular, in the area of food analysis, the use of these nanomaterials has undergone a considerable increase since they can be easily separated from the matrix in sorbent-based extractions, providing a considerable simplification of the procedures. This allows reducing cost and giving fast responses, which is essential in the food trade to guarantee consumer safety. These materials can also be easily tunable, providing higher selectivity. Moreover, their particular electrical, thermal and optical characteristics allow enhancing sensor signals, increasing the sensitivity of the approaches based on this type of device. The aim of this review article is to summarise the most remarkable applications of m-NPs in food analysis in the last five years (2016–2020) showing a general view of the use of such materials in the field.

Smartphone-based optical assays in the food safety field

Smartphone based devices (SBDs) have the potential to revolutionize food safety control by empowering citizens to perform screening tests. To achieve this, it is of paramount importance to understand current research efforts and identify key technology gaps. Therefore, a systematic review of optical SBDs in the food safety sector was performed. An overview of reviewed SBDs is given focusing on performance characteristics as well as image analysis procedures. The state-of-the-art on commercially available SBDs is also provided. This analysis revealed several important technology gaps, the most prominent of which are: (i) the need to reach a consensus regarding optimal image analysis, (ii) the need to assess the effect of measurement variation caused by using different smartphones and (iii) the need to standardize validation procedures to obtain robust data. Addressing these issues will drive the development of SBDs and potentially unlock their massive potential for citizen-based food control.

•

Optical smartphone based sensors in the food safety field are systematically reviewed.

•

Recommendations on image analysis optimization are given.

•

The analytical performance of smartphone based sensors is discussed.

•

Available commercial devises are critically compared.

Naked-eye based point-of-care detection of E.coli O157: H7 by a signal-amplified microfluidic aptasensor

Fast and sensitive detection of E.coli O157: H7 is significantly essential for clinical management as well as for transmission prevention during disease outbreaks. Though many types of detection strategies have been implemented for measuring E.coli O157: H7, most of them still rely on complex instruments or tedious/laborious setups, which restrict their applications in resource-limited scenarios. Herein, we introduce an eye-based microfluidic aptasensor (EA-Sensor) for fast detection of E.coli O157: H7 without the assist of any instruments. We demonstrate the perfect coupling of aptamer sensing, hybridization chain reaction (HCR)-amplification and a distance-based visualized readout to quantitatively determine the pathogen concentration. We first used gel-electrophoresis assay to evaluate the system and the results proved that E.coli O157: H7 was well recognized by the aptamer and HCR could increase the signal by about 100 folds. In addition, the Aptamer specificity and signal-amplification ability were verified on the EA-Sensor for sensing E.coli O157: H7 by naked eyes. Furthermore, we demonstrated that E.coli O157: H7 in milk could be accurately and conveniently measured with good performance. With the benefits of operation integration and strategy integration, our EA-Sensor shows advantages of high specificity, easy operation, efficient amplification and visualized readout, which offers a favorable point-of-care tool for E.coli O157: H7 or other pathogen detection in resource-constrained settings.

A smartphone-based biomedical sensory system

Disease diagnostics, food safety monitoring and environmental quality monitoring are the key means to safeguard human health. However, conventional detection devices for health care are costly, bulky and complex, restricting their applications in resource-limited areas of the world. With the rapid development of biosensors and the popularization of smartphones, smartphone-based sensing systems have emerged as novel detection devices that combine the sensitivity of biosensors and diverse functions of smartphones to provide a rapid, low-cost and convenient detection method. In these systems, a smartphone is used as a microscope to observe and count cells, as a camera to record fluorescence images, as an analytical platform to analyze experimental data, and as an effective tool to connect detection devices and online doctors. These systems are widely used for cell analysis, biochemical analysis, immunoassays, and molecular diagnosis, which are applied in the fields of disease diagnostics, food safety monitoring and environmental quality monitoring. Therefore, we discuss four types of smartphone-based sensing systems in this review paper, specifically in terms of the structure, performance and efficiency of these systems. Finally, we give some suggestions for improvement and future prospective trends.

A sensitive electrochemical strategy via multiple amplification reactions for the detection of E. coli O157: H7

The sensitive and efficient strategy remains a central challenge for early diagnosis of pathogenic bacteria. Herein, an ultrasensitive electrochemical biosensor was proposed based on the multiple amplification strategy via the 3D DNA walker, rolling circle amplification (RCA) and hybridization chain reaction (HCR) for the accurate detection of Escherichiacoli O157:H7 (E. coli O157:H7). Firstly, the target sequence extracted from E. coli O157:H7 was transformed and amplified by the DNA walker firstly. Subsequently, a large number of transformed nucleic acid sequences were amplified by the RCA reaction. And then, the progress of HCR was triggered by every fragment in RCA products to form a long double-stranded DNA sequence to immobilize electrochemical indicators, generating a significantly enhanced electrochemical signal. As expected, a high sensitivity with a detection limit of 7 CFU/mL was achieved based on the proposed multiple amplification strategy, which is superior to most current methods for E. coli O157: H7 assay. The multiple amplification strategy could be readily expanded for the detection of various pathogenic bacteria, providing a new approach for early diagnosis of pathogenic microorganisms or other diseases.