Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

Artificial intelligence enhanced microfluidic system for multiplexed point-of-care-testing of biological thiols

Cysteamine (CA) serves as a cystine-depleting agent employed in the management of cystinosis and a range of other medical conditions. Monitoring blood CA levels at the point of care is imperative due to the risk of toxicity associated with elevated CA dosages. An additional significant challenge is presented by the intricate composition of human plasma and the presence of various interfering biological thiols, which possess similar structures or properties. Here, this work proposes an AI-enhanced Lab-on-a-disc system, also termed AI-LOAD, for multiplexed point-of-care testing of cysteamine. The AI-LOAD system incorporates an online whole blood separation mechanism alongside a naked-eye colorimetric detection module, facilitating the rapid and precise visual identification of cysteamine. Remarkably, the system necessitates only 40 μL of whole blood to analyze eight samples within 3-min, achieving a limit of detection as low as 10 μM, which is lower than the physiological toxic concentration of 0.1 mM. By leveraging diverse colorimetric responses generated through interactions between gold nanoparticles of varying sizes and different biological thiols, combined with artificial intelligence methodologies, the system successfully accomplished specific recognition of various biological thiols with 100 % accuracy. The proposed AI-LOAD will drive advancements in centrifugal microfluidics for point-of-care testing, thereby holding potential for broader applications in future biomedical research and in vitro diagnosis.

Advances in nanoparticle-enhanced paper sensor for detecting toxic metals in water

Toxic metals in water pose a serious threat to public health and the environment, especially in regions with limited access to advanced laboratory infrastructure. Traditional methods for detecting toxic metals, such as atomic absorption spectrometry (AAS) and inductively coupled plasma mass spectrometry (ICP-MS), are accurate but expensive, complex, and unsuitable for on-site use. In contrast, paper-based analytical devices (PADs) offer a low-cost, and user-friendly alternative, especially in resource-limited areas. Recent advancements have significantly improved PAD performance, especially through the integration of nanoparticles and the use of enhanced colorimetric and electrochemical detection methods. These improvements have enabled faster, more sensitive detection while maintaining simplicity and field readiness. This review explores key advancements in PAD technology from 2015 to 2025 including advances in sensitivity, nanoparticle functionalization, and smartphone-based readouts. Unlike previous reviews, this study presents a comparative analysis of PAD detection mechanisms, evaluates commercialization and regulatory challenges, and explores emerging trends such as smartphone integration and microextraction techniques. By addressing these aspects, this review highlights key advancements and optimization strategies to enhance the stability, selectivity, and practical implementation of PADs for water quality monitoring.

Fast response cathodic electrochemiluminescence sensor based on closed bipolar electrode for point-of-care blood glucose testing

Glucose detection is vital for managing diabetes, monitoring metabolic disorders, and developing advanced biosensors. Electrochemical methods are widely used for glucose detection due to their sensitivity, portability, and low cost. However, these methods also have several limitations, such as interference from non-specific molecules, fouling of electrodes, and enzyme stability. Herein, to avoid external interference, we report a fast response cathodic electrochemiluminescence (ECL) glucose biosensor using a closed bipolar electrode (BPE) system with two separate cells (reporting cell and sensing cell). In this platform, Tris(2,2'-bipyridyl) ruthenium and K2S2O8 were used as the luminophore and co-reagent, respectively, to generate the cathodic ECL in the reporting cell, and a commercial test strip modified with GOx (glucose oxidase) and mediator served as the BPE anode to detect glucose in the sensing cell. The developed technique was able to determine glucose with a good correlation in the quantification of glucose in human serum samples with a fast response under a low potential, which avoided side reactions and was comparable to the commercial blood glucose meter. In addition, the sensing mechanism and working principle have been thoroughly studied, with the detailed discussion of the effect of oxygen and acetonitrile in influencing the ECL generation. Using this platform, glucose in the buffer was successfully quantified up to 18 mM, achieving a limit of detection of 3.8 mM and a linear concentration range between 4 and 12 mM. This electrochemical technique offers a simple and cost-effective strategy for point-of-care blood glucose testing without external interference, thereby opening up emerging opportunities in a broad range of sensing applications.

Field Detection of Multiple Infectious Diseases with Naked Eye Using Plasmonic-Enhanced Fluorescent Nanoparticles

Accurately diagnosing infectious diseases in a resource-limited setting is a major challenge. Plasmonic materials, via localized surface plasmon resonance (LSPR), have greatly enhanced fluorescence signal and detection sensitivity. However, traditional plasmonic-enhanced fluorescence methods largely rely on near-infrared or visible-light fluorophores with small Stokes shift, limiting naked-eye visibility without filters. In this study, we developed a novel plasmonic silver film (pSilverF) to enhance visible-light fluorescence with large Stokes shift, allowing for improved biomarker detection sensitivity under naked-eye observation. Integrated with bright fluorescent nanoparticles, we designed a multiplexed assay for detecting Hepatitis C Virus (HCV), Hepatitis B Virus (HBV), and Human Immunodeficiency Virus (HIV) antibodies, achieving detection sensitivities down to 0.0032, 0.023, and 0.168 NCU/mL, respectively. In a cohort of 68 clinical samples, our method achieved 100% sensitivity and specificity for HIV and HCV detection and 96% sensitivity and 100% specificity for HBV detection. Notably, the results can be visualized by the naked eye and directly captured by a standard mobile phone camera without any modification for signal analysis using RGB image splitting. This platform demonstrated potential for field detection of multiple infectious diseases with simple settings, providing a useful tool for disease control in communities and areas with limited medical resources.

Machine learning in point-of-care testing: innovations, challenges, and opportunities

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

Microfluidic chip systems for color-based antimicrobial susceptibility test a review

Clinical bacteria pose a significant public health threat, underscoring the need for reliable and rapid diagnostic methods for early disease detection, which can facilitate patient recovery. Current diagnostic methods for rapid pathogen detection often take hours to days and require numerous reagents and lengthy protocols. Microfluidic chip system offers a promising solution for clinical microbiology detection by reducing detection time with minimal setup and providing a point-of-care solution for patients. These systems are also easier to handle and, with advancements in technology, offer more conclusive observations. This review focuses on recent developments in microfluidic chip-based systems that use colored fluorescent and non-fluorescent dyes for phenotypic tests in clinical pathogen detection. Recent advancements in non-conventional observation methods, such as smartphones and software combined with microscopy, are paving the way for microfluidic systems to revolutionize point-of-care devices. Significant challenges for these systems include antimicrobial susceptibility testing protocols, which depend on color formation, observation methods, and reducing detection time. In the future, working with live cultures remains a major hurdle in developing efficient and accurate microfluidic diagnostic systems for antimicrobial susceptibility testing.

Mechanically regulated microcarriers with stem cell loading for skin photoaging therapy

Long-term exposure to ultraviolet radiation compromises skin structural integrity and results in disruption of normal physiological functions. Stem cells have gained attention in anti-photoaging, while controlling the tissue mechanical microenvironment of cell delivery sites is crucial for regulating cell fate and achieving optimal therapeutic performances. Here, we introduce a mechanically regulated human recombinant collagen (RHC) microcarrier generated through microfluidics, which is capable of modulating stem cell differentiation to treat photoaged skin. By controlling the cross-linking parameters, the mechanical properties of microcarriers could precisely tuned to optimize the stem cell differentiation. The microcarriers are surface functionalized with fibronectin (Fn)-platelet derived growth factor-BB (PDGF-BB) to facilitate adipose derived mesenchymal stem cells (Ad-MSCs) loading. In in vivo experiments, subcutaneous injection of stem cell loaded RHC microcarriers significantly reduced skin wrinkles after ultraviolet-injury, effectively promoted collagen synthesis, and increased vascular density. These encouraging results indicate that the present mechanically regulated microcarriers have great potential to deliver stem cells and regulate their differentiation for anti-photoaging treatments.

Advancements of paper-based microfluidics and organ-on-a-chip models in cosmetics hazards

Cosmetics have been used in society for centuries for beautification and personal hygiene maintenance. Modern cosmetics include various makeup, hair, and skincare products that range from moisturizers and shampoos to lipsticks and foundations and have become a quintessential part of our daily grooming activities. However, dangerous adulterants are added during the production of these cosmetics, which range from heavy metals to microbial contaminants. These adulterants not only reduce the quality and efficacy of cosmetic products but also pose a significant risk to human health. Detecting the presence of adulterants in cosmetics is crucial for regulating substandard cosmetic products in the industry. The conventional methods to detect such adulterants and quality testing are expensive and take a lot of effort, particularly when involving advanced analytical detection and clinical trials. Recently, efficient methods such as microfluidic methods have emerged to detect adulterants rapidly. In this review, we mainly focus on various adulterants present in cosmetics and their detection using paper-based microfluidic devices. In addition, this review also sheds light on the organ-on-a-chip model with the goal of developing a human tissue model for cosmetic testing. Combined, these approaches provide an efficient, inexpensive, and sustainable approach for quality testing in the cosmetics industry.

Weight Differences-Based Multi-level Signal Profiling for Homogeneous and Ultrasensitive Intelligent Bioassays

Current high-sensitivity immunoassay protocols often involve complex signal generation designs or rely on sophisticated signal-loading and readout devices, making it challenging to strike a balance between sensitivity and ease of use. In this study, we propose a homogeneous-based intelligent analysis strategy called Mata, which uses weight analysis to quantify basic immune signals through signal subunits. We perform nanomagnetic labeling of target capture events on micrometer-scale polystyrene subunits, enabling magnetically regulated kinetic signal expression. Signal subunits are classified through the multi-level signal classifier in synergy with the developed signal weight analysis and deep learning recognition models. Subsequently, the basic immune signals are quantified to achieve ultra-high sensitivity. Mata achieves a detection of 0.61 pg/mL in 20 min for interleukin-6 detection, demonstrating sensitivity comparable to conventional digital immunoassays and over 22-fold that of chemiluminescence immunoassay and reducing detection time by more than 70%. The entire process relies on a homogeneous reaction and can be performed using standard bright-field optical imaging. This intelligent analysis strategy balances high sensitivity and convenient operation and has few hardware requirements, presenting a promising high-sensitivity analysis solution with wide accessibility.

Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network

Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence. Due to the advantages in computing speed, integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm. Programmable photonic chips are vital for achieving practical applications of photonic computing. Herein, a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing. Through designing the ultrafast laser pulses, the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase, resulting in a large contrast in refractive index and extinction coefficient. As a consequence, the light transmission of waveguides can be switched between write and erase states. To determine the phase change time, the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, and the time-resolved transient reflectivity is measured. Based on the integrated photonic chip, photonic convolutional neural networks are built to implement machine learning algorithm, and images recognition task is achieved. This work paves a route for fabricating programmable photonic chips by designed ultrafast laser, which will facilitate the application of photonic computing in artificial intelligence.

Transformative laboratory medicine enabled by microfluidic automation and artificial intelligence

Laboratory medicine provides pivotal medical information through analyses of body fluids and tissues, and thus, it is essential for diagnosis of diseases as well as monitoring of disease progression. Despite its universal importance, the field is currently suffering from the limited workforce and analytical capabilities due to the increasing pressure from expanding global population and unexpected rise of noncommunicable diseases. The emerging technologies of microfluidic automation and artificial intelligence (AI) has led to the development of advanced diagnostic platforms, positioning themselves as adaptable solutions to enable highly efficient and accessible laboratory medicine. In this review, we will provide a comprehensive review of microfluidic automation, focusing on the microstructure design and automation principles, along with its intended functionalities for diagnostic purposes. Subsequently, we exemplify the integration of AI with microfluidics and illustrating how their combination benefits for the applications and what the challenges are in this rapidly evolving field. Finally, the review offers a balanced perspective on the microfluidics and AI, discussing their promising role in advancing laboratory medicine.

Recent advances in centrifugal microfluidics for point-of-care testing

Point-of-care testing (POCT) holds significant importance in the field of infectious disease prevention and control, as well as personalized precision medicine. The emerging microfluidics, capable of minimal reagent consumption, integration, and a high degree of automation, play a pivotal role in POCT. Centrifugal microfluidics, also termed lab-on-a-disc (LOAD), is a significant subfield of microfluidics that integrates crucial analytical steps onto a single chip, thereby optimizing the process and enabling high-throughput, automated analysis. By utilizing rotational mechanics to precisely control fluid dynamics without external pressure sources, centrifugal microfluidics facilitates swift operations ideal for urgent medical and field settings. This review provides a comprehensive overview of the latest advancements in centrifugal microfluidics for POCT, covering both theoretical principles and practical applications. We begin by introducing the fundamental operational principles, fluidic control mechanisms, and signal output detection methods. Subsequently, we delve into the typical applications of centrifugal microfluidic platforms in immunoassays, nucleic acid testing, antimicrobial susceptibility testing, and other tests. We also discuss the strengths and potential limitations of centrifugal microfluidic platforms, underscoring their transformative impact on traditional conventional procedures and their significant role in diagnostic practices.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Frog tongue-inspired wettable microfibers for particles capture

Fibers have been of great significance in our daily lives, especially in the industrial production of masks. Research in this area has been focused on developing microfibers with superior functions to enhance the filtration performances of the masks. Herein, inspired by the frog's predation mechanism using its tongues to swiftly grab flying insects, we propose novel porous wettable microfibers from microfluidics to efficiently capture particles in the air for filtration. Upon pre-dispersing LP emulsions into polyurethane (PU), porous microfibers dispersed with oil droplets could be continuously spun from a co-flow microfluidic device based on the quick phase inversion of PU. To design an optimal system with frog-tongue-like interfacial adhesion properties, the wettability performances of the porous microfibers are investigated under full, partial, and no oil coverage conditions. When implemented in a mask, the 3D patterned networks based on the frog-tongue-inspired microfibers have been proven with remarkable particle capture performances while maintaining good air permeability. Based on these features, we believe that frog-tongue-inspired microfibers and their derived masks are of practical significance in multiple applications.

Kirkendall Effect-Mediated Transformation of ZIF-67 to NiCo-LDH Nanocages as Oxidase Mimics for Multicolor Point-of-Care Testing of β-Galactosidase Activity and Escherichia coli

Early and portable detection of pathogenic bacteria is crucial for ensuring food safety, monitoring product quality, and tracing the sources of bacterial infections. Moving beyond traditional plate-culture counting methods, the analysis of active bacterial components offers a rapid means of quantifying bacteria. Here, metal-organic framework (MOF)-derived NiCo-layered double hydroxide nanosheets (LDHs), synthesized via the Kirkendall effect, were employed as highly effective oxidase mimics to generate reactive oxygen species (ROS). These ROS quickly etched gold nanobipyramids (Au NBPs), producing a vivid multicolormetric response. Experimental results and theoretical calculations indicated that the exceptional oxidase-like activity of NiCo-LDHs stemmed from the presence of bimetallic active sites and oxygen vacancies modulating the local electronic structure of LDHs. Additionally, β-galactosidase (β-Gal), a biomarker of Escherichia coli, reacted with p-aminophenyl-β-d-galactopyranoside (PAPG) to form p-aminophenol (PAP), a reducing agent which consumes ROS, thereby inhibiting the etching of Au NBPs. Furthermore, a three-dimensional (3D)-printed point-of-care testing (POCT) shell was designed as a portable device to visually detect β-Gal and E. coli in conjugation with smartphones. This study not only provides a novel approach to the rational design of nanozymes but also establishes a vivid and portably visual biosensing platform for detecting β-Gal activity and pathogenic bacteria.

Artificial Intelligence-Powered Materials Science

The advancement of materials has played a pivotal role in the advancement of human civilization, and the emergence of artificial intelligence (AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy, environment, and biomedical concerns in a sustainable manner. The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly, energy-efficient, and conducive to human well-being. This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications. We anticipate that AI technology will be extensively utilized in material research and development, thereby expediting the growth and implementation of novel materials. AI will serve as a catalyst for materials innovation, and in turn, advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science. Through the synergistic collaboration between AI and materials science, we stand to realize a future propelled by advanced AI-powered materials.

Magnetofluidic-Assisted Portable Automated Microfluidic Devices for Protein Detection

To facilitate on-site detection by nonspecialists, there is a demand for the development of portable "sample-to-answer" devices capable of executing all procedures in an automated or easy-to-operate manner. Here, we developed an automated detection device that integrated a magnetofluidic manipulation system and a signal acquisition system. Both systems were controllable via a smartphone. In the device, the mixing of solutions and magnetic beads in the static chamber was enhanced by steel bead agitation, which improved the reaction efficiency. We demonstrate the performance of the device using myoglobin detection as an example. During the detection process, the plasma was separated from the whole blood sample using a homemade mini-centrifuge, and subsequently, the plasma, magnetic beads, and reagents were added to a magnetofluidic chip with multiple chambers. After the chip was loaded, the device was initiated with a smartphone App via Bluetooth. Then, the magnetic beads were shuttled through different chambers of the chip and multiple steps were completed automatically: first, the targets were separated and enriched using antibody-modified magnetic beads, followed by washing, binding with aptamer-functionalized G-quadruplex, signal amplifying (optional), and chromogenic reaction. Finally, the images of colored solutions were captured and processed by a smartphone to obtain the concentrations of myoglobin. The detection limits depended on the mode of signal conversion, which were 0.1 or 2.7 nM (with or without signal amplifying). With its simple operation, compact design, low cost, and ease of scalability, this automated detection device holds potential applications in human health, food safety, environmental monitoring, etc.

Quantitative investigation of a 3D bubble trapper in a high shear stress microfluidic chip using computational fluid dynamics and L*A*B* color space

Microfluidic chips often face challenges related to the formation and accumulation of air bubbles, which can hinder their performance. This study investigated a bubble trapping mechanism integrated into microfluidic chip to address this issue. Microfluidic chip design includes a high shear stress section of fluid flow that can generate up to 2.7 Pa and two strategically placed bubble traps. Commercially available magnets are used for fabrication, effectively reducing production costs. The trapping efficiency is assessed through video recordings with a phone camera and analysis of captured air volumes by injecting dye at flow rates of 50, 100, and 150 µL/min. This assessment uses L*A*B* color space with analysis of the perceptual color difference ∆E and computational fluid dynamics (CFD) simulations. The results demonstrate successful application of the bubble trap mechanism for lab-on-chip bubble detection, effectively preventing bubbles from entering microchannels and mitigating potential damage. Furthermore, the correlation between the L*A*B* color space and volume fraction from CFD simulations allows accurate assessment of trap performance. Therefore, this observation leads to the hypothesis that ∆E could be used to estimate the air volume inside the bubble trap. Future research will validate the bubble trap performance in cell cultures and develop efficient methods for long-term air bubble removal.

Rapid parallel blood typing on centrifugal microfluidic platform by microcolumn gel immunoassay

Microcolumn gel immunoassay (MGIA) has the ability to meet the requirements of clinical diagnosis due to its reliable sensitivity and accuracy. However, traditional MGIA exhibits limitations including inadequate portability, low throughput, and extended analysis time. To address these challenges, we combined MGIA with microfluidic technology, demonstrating a centrifugal microfluidic-based microcolumn gel immunoassay (μMGIA) platform for blood typing of clinical samples. Experimental results indicate that the μMGIA platform can simultaneously detect six blood group antigens in five clinical blood samples within 2 min. Notably, it offers comprehensive detection of ABO blood group antigens and Rh blood group antigens with 100 % accuracy, outperforming the traditional slide method. The integration of microfluidic technology with MGIA circumvents the constraints of traditional methods, providing a new avenue for blood typing and immunoanalysis of clinical samples.

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and illness outbreaks. Here, an unsupervised learning-assisted and surface acoustic wave-interdigital transducer-driven nano-lens holography biosensing platform is developed for the ultrasensitive and amplification-free detection of viable bacteria. The monitoring device integrated with the nano-lens effect can achieve the holographic imaging of polystyrene microsphere probes in an ultra-wide field of view (∽28.28 mm2), with a sensitivity limit of as low as 99 nm. A lightweight unsupervised learning hologram processing algorithm considerably reduces training time and computing hardware requirements, without requiring datasets with manual labels. By combining phage-mediated viable bacterial DNA extraction and enhanced CRISPR-Cas12a systems, this strategy successfully achieves the ultrasensitive detection of viable Salmonella in various real samples, demonstrating enhanced accuracy validated with the qPCR benchmark method. This approach has a low cost (∽$500) and is rapid (∽1 h) and highly sensitive (∽38 CFU mL-1), allowing for the amplification-free detection of viable bacteria and emerging as a powerful tool for food safety inspection and clinical diagnosis.

Phase Transition of Wax Enabling CRISPR Diagnostics for Automatic At-Home Testing of Multiple Sexually Transmitted Infection Pathogens

Sexually transmitted infections (STIs) significantly impact women's reproductive health. Rapid, sensitive, and affordable detection of these pathogens is essential, especially for home-based self-testing, which is crucial for individuals who prioritize privacy or live in areas with limited access to healthcare services. Herein, an automated diagnostic system called Wax-CRISPR has been designed specifically for at-home testing of multiple STIs. This system employs a unique strategy by using the solid-to-liquid phase transition of wax to sequentially isolate and mix recombinase polymerase amplification (RPA) and CRISPR assays in a microfluidic chip. By incorporating a home-built controlling system, Wax-CRISPR achieves true one-pot multiplexed detection. The system can simultaneously detect six common critical gynecological pathogens (CT, MG, UU, NG, HPV 16, and HPV 18) within 30 min, with a detection limit reaching 10-18 M. Clinical evaluation demonstrates that the system achieves a sensitivity of 96.8% and a specificity of 97.3% across 100 clinical samples. Importantly, eight randomly recruited untrained operators performe a double-blinded test and successfully identified the STI targets in 33 clinical samples. This wax-transition-based one-pot CRISPR assay offers advantages such as low-cost, high-stability, and user-friendliness, making it a useful platform for at-home or field-based testing of multiple pathogen infections.

Machine-Learning Mental-Fatigue-Measuring μm-Thick Elastic Epidermal Electronics (MMMEEE)

Electrophysiological (EP) signals are key biomarkers for monitoring mental fatigue (MF) and general health, but state-of-the-art wearable EP-based MF monitoring systems are bulky and require user-specific, labeled data. Ultrathin epidermal electrodes with high performance are ideal for constructing imperceptive EP sensing systems; however, the lack of a simple and scalable fabrication delays their application in MF recognition. Here, we report a facile, scalable printing-welding-transferring strategy (PWT) for printing μm-thickness micropatterned silver nanowires (AgNWs)/sticky polydimethylsiloxane, welding the AgNWs via plasmonic effect, and transferring the electrode to skin as tattoos. The PWT provides electrodes with conformability, comfort, and stability for EP sensing. Leveraging the facile and scalable PWT, we develop plug-and-play wireless multimodal epidermal electronics integrated with an unsupervised transfer learning (UTL) scheme for MF recognition across various users. The UTL adaptively minimizes the intersubject difference and achieves high accuracy, without demand of expensive computation and labels from target users.

Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation

BACKGROUND

Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.

OBJECTIVE

The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips?

METHODS

To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement.

RESULTS

In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F6, 99=6144.45, P<.001; Bottleneck: F6, 99=3475.78, P<.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e-k×Height) and a bottleneck design could effectively preserve video quality (Straight: R2=0.95, k=4.33; Bottleneck: R2=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R2=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R2=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth.

CONCLUSIONS

In conclusion, we demonstrated the importance of the flow velocity in a microfluidic system, and we proposed SmartFlow, a low-cost system for computer vision-based cellular analysis. The proposed system could count the cells and estimate cell concentrations in the samples.

AI-empowered visualization of nucleic acid testing

AIMS

The visualization of nucleic acid testing (NAT) results plays a critical role in diagnosing and monitoring infectious and genetic diseases. The review aims to review the current status of AI-based NAT result visualization. It systematically introduces commonly used AI-based methods and techniques for NAT, emphasizing the importance of result visualization for accessible, clear, and rapid interpretation. This highlights the importance of developing a NAT visualization platform that is user-friendly and efficient, setting a clear direction for future advancements in making nucleic acid testing more accessible and effective for everyday applications.

METHOD

This review explores both the commonly used NAT methods and AI-based techniques for NAT result visualization. The focus then shifts to AI-based methodologies, such as color detection and result interpretation through AI algorithms. The article presents the advantages and disadvantages of these techniques, while also comparing the performance of various NAT platforms in different experimental contexts. Furthermore, it explores the role of AI in enhancing the accuracy, speed, and user accessibility of NAT results, highlighting visualization technologies adapted from other fields of experimentation.

SIGNIFICANCE

This review offers valuable insights for researchers and everyday users, aiming to develop effective visualization platforms for NAT, ultimately enhancing disease diagnosis and monitoring.

A press-actuated slidable microfluidic colorimetric biosensor for Salmonella detection utilizing nickel mesh sheet and MIL-88@Pd/Pt nanoparticles

A press-actuated slidable microfluidic colorimetric biosensor was designed for rapid, sensitive and multi-channel detection of Salmonella. The nickel mesh sheet (NMS) modified with capture antibodies was employed for capturing target bacteria, and metal organic frameworks decorated with palladium (Pd) and platinum (Pt) nanoparticles (MIL-88@Pd/Pt NPs) modified with detection antibodies were used for amplifying colorimetric signals. The capture efficiency of the immune NMS reached 83 %, and the detection limit of this colorimetric biosensor was 35 CFU/mL in 20 min. The average recoveries for Salmonella in spiked chicken meats ranged from 92.2 % to 102.5 % with a variation from 3.7 % to 7.2 %. The press-actuated slidable microfluidic chip was elaboratively developed with multiple functions, including mixing, separation, washing, catalysis and detection.

A microfluidic chip with integrated plasma separation for sample-to-answer detection of multiple chronic disease biomarkers in whole blood

Point-of-care testing of multiple chronic disease biomarkers is crucial for timely intervention and management of chronic diseases. Here, a "sample-to-answer" microfluidic chip was developed for simultaneous detection of multiple chronic disease biomarkers in whole blood by integrating a plasma separation module. The whole detection process is very convenient, i.e., just add whole blood and get the results. The chip successfully achieved the simultaneous detection of total cholesterol, triglycerides, uric acid, and glucose in undiluted whole blood within 21 min, including 6 min for plasma separation and 15 min for enzymatic chromogenic reactions. Moreover, the sensitivity levels of on-chip detection of chronic disease biomarkers can also meet clinically relevant thresholds. The chip is easy to use and has significant potential to improve home self-management of chronic diseases and enhance healthcare outcomes.

An electrochemical point-of-care testing device for specific diagnosis of the albinism biomarker based on paradigm shift designs

L-tyrosine is a recognized biomarker of albinism, whose endogenous level in human bodies is directly linked to melanin synthesis while no attention has been paid to its specific diagnosis. To this end, we have developed an electrochemical point-of-care testing device based on a molecularly imprinted gel prepared by a universal paradigm shift design to achieve the enhanced specific recognition of the L-tyrosine. Interestingly, this theoretically optimized molecularly imprinted gel validates the recognition pattern of L-tyrosine and optimizes the structure of the polymer itself with the aid of computational chemistry. Besides, modified extended-layer MXene and Au nanoclusters have significantly improved the sensing activity. As a result, the linear diagnostic range of this electrochemical point-of-care testing device for L-tyrosine is 0.1-100 μM in actual human fluids, which fully covers the L-tyrosine levels of healthy individuals and people with albinism. The diagnosis is completed in 90 s and then the results are transmitted by Bluetooth low energy to the smart mobile terminal. Therefore, we are convinced that this electrochemical point-of-care testing device is a promising tool in the future smart medical system.

Emerging Trends in Integrated Digital Microfluidic Platforms for Next-Generation Immunoassays

Technologies based on digital microfluidics (DMF) have made significant advancements in the automated manipulation of microscale liquids and complex multistep processes. Due to their numerous benefits, such as automation, speed, cost-effectiveness, and minimal sample volume requirements, these systems are particularly well suited for immunoassays. In this review, an overview is provided of diverse DMF manipulation platforms and their applications in immunological analysis. Initially, droplet-driven DMF platforms based on electrowetting on dielectric (EWOD), magnetic manipulation, surface acoustic wave (SAW), and other related technologies are briefly introduced. The preparation of DMF is then described, including material selection, fabrication techniques and droplet generation. Subsequently, a comprehensive account of advancements in the integration of DMF with various immunoassay techniques is offered, encompassing colorimetric, direct chemiluminescence, enzymatic chemiluminescence, electrosensory, and other immunoassays. Ultimately, the potential challenges and future perspectives in this burgeoning field are delved into.

Smartphone-driven centrifugal microfluidics for diagnostics in resource limited settings

The broad availability of smartphones has provided new opportunities to develop less expensive, portable, and integrated point-of-care (POC) platforms. Here, a platform that consists of three main components is introduced: a portable housing, a centrifugal microfluidic disc, and a mobile phone. The mobile phone supplies the electrical power and serves as an analysing system. The low-cost housing made from cardboard serves as a platform to conduct tests. The electrical energy stored in mobile phones was demonstrated to be adequate for spinning a centrifugal disc up to 3000 revolutions per minute (RPM), a rotation speed suitable for majority of centrifugal microfluidics-based assays. For controlling the rotational speed, a combination of magnetic and acoustic tachometry using embedded sensors of the mobile phone was used. Experimentally, the smartphone-based tachometry was proven to be comparable with a standard laser-based tachometer. As a proof of concept, two applications were demonstrated using the portable platform: a colorimetric sandwich immunoassay to detect interleukin-2 (IL-2) having a limit of detection (LOD) of 65.17 ng/mL and a fully automated measurement of hematocrit level integrating blood-plasma separation, imaging, and image analysis that takes less than 5 mins to complete. The low-cost platform weighing less than 150 g and operated by a mobile phone has the potential to meet the REASSURED criteria for advanced diagnostics in resource limited settings.

Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging

Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument - irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.

Artificial intelligence performance in testing microfluidics for point-of-care

Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications

The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

Fully Integrated and High-Throughput Microfluidic System for Multiplexed Point-Of-Care Testing

For every epidemic outbreak, the prevention and treatments in resource-limited areas are always out of reach. Critical to this is that high accuracy, stability, and more comprehensive analytical techniques always rely on expensive and bulky instruments and large laboratories. Here, a fully integrated and high-throughput microfluidic system is proposed for ultra-multiple point-of-care immunoassay, termed Dac system. Specifically, the Dac system only requires a handheld portable device to automatically recycle repetitive multi-step reactions including on-demand liquid releasing, dispensing, metering, collecting, oscillatory mixing, and discharging. The Dac system performs high-precision enzyme-linked immunosorbent assays for up to 17 samples or targets simultaneously on a single chip. Furthermore, reagent consumption is only 2% compared to conventional ELISA, and microbubble-accelerated reactions shorten the assay time by more than half. As a proof of concept, the multiplexed detections are achieved by detecting at least four infection targets for two samples simultaneously on a singular chip. Furthermore, the barcode-based multi-target results can rapidly distinguish between five similar cases, allowing for accurate therapeutic interventions. Compared to bulky clinical instruments, the accuracy of clinical inflammation classification is 92.38% (n = 105), with a quantitative correlation coefficient of R2 = 0.9838, while the clinical specificity is 100% and the sensitivity is 98.93%.

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Effective Laboratory Diagnosis of Parasitic Infections of the Gastrointestinal Tract: Where, When, How, and What Should We Look For?

(1) Introduction: Gastrointestinal parasites (GIPs) are one of the most common causes of disease in the world. Clinical diagnosis of most parasitic diseases is difficult because they do not produce characteristic symptoms. (2) Methods: The PubMed, Science Direct, and Wiley Online Library medical databases were reviewed using the following phrases: "parasitic infections and diagnostics", "intestinal parasites", "gastrointestinal parasites", "parasitic infections and diagnostics", and their combinations. (3) Results and Conclusions: Correct diagnosis of GIP involves determining the presence of a parasite and establishing a relationship between parasite invasion and disease symptoms. The diagnostic process should consider the possibility of the coexistence of infection with several parasites at the same time. In such a situation, diagnostics should be planned with consideration of their frequency in each population and the local epidemiological situation. The importance of the proper interpretation of laboratory test results, based on good knowledge of the biology of the parasite, should be emphasized. The presence of the parasite may not be causally related to the disease symptoms. Due to wide access to laboratories, patients often decide to perform tests themselves without clinical justification. Research is carried out using various methods which are often unreliable. This review briefly covers current laboratory methods for diagnosing the most common gastrointestinal parasitic diseases in Europe. In particular, we provide useful information on the following aspects: (i) what to look for and where to look for it (suitability of feces, blood, duodenal contents, material taken from endoscopy or biopsy, tissue samples, and locations for searching for eggs, cysts, parasites, parasite genetic material, and characteristics of immune responses indicating parasitic infections); (ii) when material should be collected for diagnosis and/or to check the effectiveness of treatment; (iii) how-that is, by what methods-laboratory diagnostics should be carried out. Here, the advantages and disadvantages of direct and indirect methods of detecting parasites will be discussed. False-positive or false-negative results are a problem facing many tests. Available tests have different sensitivities and specificities. Therefore, especially in doubtful situations, tests for the presence of the pathogen should be performed using various available methods. It is important that the methods used make it possible to distinguish an active infection from a past infection. Finally, we present laboratory "case reports", in which we will discuss the diagnostic procedure that allows for the successful identification of parasites. Additionally, we briefly present the possibilities of using artificial intelligence to improve the effectiveness of diagnosing parasitic diseases.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

A Biomimetic Optical Cardiac Fibrosis-on-a-Chip for High-Throughput Anti-Fibrotic Drug Screening

Cardiac fibrosis has emerged as the primary cause of morbidity, disability, and even mortality in numerous nations. In light of the advancements in precision medicine strategies, substantial attention has been directed toward the development of a practical and precise drug screening platform customized for individual patients. In this study, we introduce a biomimetic cardiac fibrosis-on-a-chip incorporating structural color hydrogels (SCHs) to enable optical high-throughput drug screening. By cocultivating a substantial proportion of cardiac fibroblasts (CFBs) with cardiomyocytes on the SCH, this biomimetic fibrotic microtissue successfully replicates the structural components and biomechanical properties associated with cardiac fibrosis. More importantly, the structural color shift observed in the SCH can be indicative of cardiac contraction and relaxation, making it a valuable tool for evaluating fibrosis progression. By incorporating such fibrotic microtissue into a microfluidic gradient chip, we develop a biomimetic optical cardiac fibrosis-on-a-chip platform that accurately and efficiently screens potential anti-fibrotic drugs. These characteristics suggest that this microphysiological platform possesses the capability to establish a preclinical framework for screening cardiac drugs, and may even contribute to the advancement of precision medicine.

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput, limited integration, and complex reagent preparation. Here, a system, microfluidic multiplate-based ultrahigh throughput analysis of SARS-CoV-2 variants of concern using CRISPR/Cas12a and nonextraction RT-LAMP (mutaSCAN), is proposed for rapid detection of SARS-CoV-2 and its variants with limited resource requirements. With the aid of the self-developed reagents and deep-learning enabled prototype device, our mutaSCAN system can detect SARS-CoV-2 in mock swab samples below 30 min as low as 250 copies/mL with the throughput up to 96 per round. Clinical specimens were tested with this system, the accuracy for routine and mutation testing (22 wildtype samples, 26 mutational samples) was 98% and 100%, respectively. No false-positive results were found for negative (n = 24) samples.

AI-powered microscopy image analysis for parasitology: integrating human expertise

Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.

Self-reporting electroswitchable colorimetric platform for smart ammonium recovery from wastewater

Recovery of ammonium from wastewater represents a sustainable strategy within the context of global resource depletion, environmental pollution and carbon neutralization. The present study developed an advanced self-reporting electroswitchable colorimetric platform (SECP) to realize smart ammonium recovery based on the electrically stimulated transformation of Prussian blue/Prussian white (PB/PW) redox couple. The key to SECP was the selectivity of ammonium adsorption, sensitivity of desorption to electric signals and visualability of color change during switchable adsorption/desorption transformation. The results demonstrated the electrochemical intercalation-induced selective adsorption of NH4+ (selectivity coefficient of 3-19 versus other cations) and deintercalation-induced desorption on the PB-film electrode. At applied voltage of 1.2 V for 20 min, the negatively charged PB-film electrode achieved the maximum adsorption capacity of 3.2 mmol g-1. Reversing voltage to -0.2 V for 20 min resulted in desorption efficiency as high as 99%, indicating high adsorption/desorption reversibility and cyclic stability. The Fe(III)/Fe(II) redox dynamics were responsible for PB/PW transformation during reversible intercalation/deintercalation of NH4+. Based on the blue/transparence color change of PB/PW, the quantitative relationship was established between amounts of NH4+ adsorbed and extracted RGB values by multiple linear regression (R2 = 0.986, RMSE = 0.095). Then, the SECP was created upon the unique capability of real-time monitoring and feedback of color change of electrode to realize the automatic control of NH4+ adsorption/desorption. During five cycles of tests, the adsorption process consistently peaked at an average value of 3.15±0.04 mmol g-1, while desorption reliably approached the near-zero average of 0.06±0.04 mmol g-1. The average time of duration was 19.6±1.67 min for adsorption and 18.8±1.10 min for desorption, respectively. With electroswitchability, selectivity and self-reporting functionalities, the SECP represents a paradigm shift in smart ammonium recovery from wastewater, making wastewater treatment and resource recovery more efficient, more intelligent and more sustainable.

A smartphone serves as a data logger for a fully automated lab-constructed microfluidic system

Fluorescence is an innovative technique that has captivated scholars in recent years due to its superior sensitivity and selectivity. The development of microfluidic components has added to its appeal, particularly given the technology ability to control fluid using very small quantities (microliter range) and achieve high liquid throughput. We have combined these two technologies to develop a lab-constructed simple system for measuring fluorescence, notable for the following features:•The device constructed entirely in our lab and programmed for measuring the fluorescence of liquids using microfluidic technology, delivered excellent results. The regression coefficient R² (0.9995) was obtained five points between 0.001-0.01µg .ml-1. Moreover, the reproducibility standard deviation (%) of 0.008 µg .ml-1 fluorescein dye remained at zero, for ten repeated experiments.•The device was full automated using a smartphone as a data logger, and lab-constructed programs.•The results were satisfactory with a detection limit of 1 × 10-4 µg.ml-1. This proposed system can measure over 200 samples per hour making it highly efficient and eco-friendly due to the reduced use of reagents and lower waste production. The fully automated system can effectively be used to determine fluorescein dye concentrations. Another application (micro pump view) manages all actions required in this microfluidic system, such as operating the two lab-constructed peristaltic pumps.

A Highly Sensitive Dual-Drive Microfluidic Device for Multiplexed Detection of Respiratory Virus Antigens

Conventional microfluidic systems that rely on capillary force have a fixed structure and limited sensitivity, which cannot meet the demands of clinical applications. Herein, we propose a dual-drive microfluidic device for sensitive and flexible detection of multiple pathogenic microorganisms antigens/antibodies. The device comprises a portable microfluidic analyzer and a dual-drive microfluidic chip. Along with capillary force, a second active driving force is provided by a removable self-driving valve in the waste chamber. The interval between these two driving forces can be adjusted to control the reaction time in the microchannel, optimizing the formation of antigen-antibody complexes and enhancing sensitivity. Moreover, the material used in the self-driving valve can be changed to adjust the active force strength needed for different tests. The device offers quantitative analysis for respiratory syncytial virus antigen and SARS-CoV-2 antigen using a 35 μL sample, delivering results within 5 min. The detection limits of the system were 1.121 ng/mL and 0.447 ng/mL for respiratory syncytial virus recombinant fusion protein and SARS-CoV-2 recombinant nucleoprotein, respectively. Although the dual-drive microfluidic device has been used for immunoassay for respiratory syncytial virus and SARS-CoV-2 in this study, it can be easily adapted to other immunoassay applications by changing the critical reagents.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting

BACKGROUND

Mastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform.

RESULTS

The central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices.

SIGNIFICANCE

In summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

Advances in Nucleic Acid Assays for Infectious Disease: The Role of Microfluidic Technology

Within the fields of infectious disease diagnostics, microfluidic-based integrated technology systems have become a vital technology in enhancing the rapidity, accuracy, and portability of pathogen detection. These systems synergize microfluidic techniques with advanced molecular biology methods, including reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR), have been successfully used to identify a diverse array of pathogens, including COVID-19, Ebola, Zika, and dengue fever. This review outlines the advances in pathogen detection, attributing them to the integration of microfluidic technology with traditional molecular biology methods and smartphone- and paper-based diagnostic assays. The cutting-edge diagnostic technologies are of critical importance for disease prevention and epidemic surveillance. Looking ahead, research is expected to focus on increasing detection sensitivity, streamlining testing processes, reducing costs, and enhancing the capability for remote data sharing. These improvements aim to achieve broader coverage and quicker response mechanisms, thereby constructing a more robust defense for global public health security.

Nucleic Acid Plate Culture: Label-Free and Naked-Eye-Based Digital Loop-Mediated Isothermal Amplification in Hydrogel with Machine Learning

Digital nucleic acid amplification enables the absolute quantification of single molecules. However, due to the ultrasmall reaction volume in the digital system (i.e., short light path), most digital systems are limited to fluorescence signals, while label-free and naked-eye readout remain challenging. In this work, we report a digital nucleic acid plate culture method for label-free, ultrasimple, and naked-eye nucleic acid analysis. As simple as the bacteria culture, the nanoconfined digital loop-mediated isothermal amplification was performed by using polyacrylamide (PAM) hydrogel as the amplification matrix. The nanoconfinement of PAM hydrogel with an ionic polymer chain can remarkably accelerate the amplification of target nucleic acids and the growth of inorganic byproducts, namely, magnesium pyrophosphate particles (MPPs). Compared to that in aqueous solutions, MPPs trapped in the hydrogel with enhanced light scattering characteristics are clearly visible to the naked eye, forming white "colony" spots that can be simply counted in a label-free and instrument-free manner. The MPPs can also be photographed by a smartphone and automatically counted by a machine-learning algorithm to realize the absolute quantification of antibiotic-resistant pathogens in diverse real samples.

Quantitative, high-sensitivity measurement of liquid analytes using a smartphone compass

Smartphone ubiquity has led to rapid developments in portable diagnostics. While successful, such platforms are predominantly optics-based, using the smartphone camera as the sensing interface. By contrast, magnetics-based modalities exploiting the smartphone compass (magnetometer) remain unexplored, despite inherent advantages in optically opaque, scattering or auto-fluorescing samples. Here we report smartphone analyte sensing utilizing the built-in magnetometer for signal transduction via analyte-responsive magnetic-hydrogel composites. As these hydrogels dilate in response to targeted stimuli, they displace attached magnetic material relative to the phone's magnetometer. Using a bilayer hydrogel geometry to amplify this motion allows for sensitive, optics-free, quantitative liquid-based analyte measurements that require neither any electronics nor power beyond that contained within the smartphone itself. We demonstrate this concept with glucose-specific and pH-responsive hydrogels, including glucose detection down to single-digit micromolar concentrations with potential for extension to nanomolar sensitivities. The platform is adaptable to numerous measurands, opening a path towards portable, inexpensive sensing of multiple analytes or biomarkers of interest.

Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs)

In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

Virus detection light diffraction fingerprints for biological applications

The transmission of viral diseases is highly unstable and highly contagious. As the carrier of virus transmission, cell is an important factor to explore the mechanism of virus transmission and disease. However, there is still a lack of effective means to continuously monitor the process of viral infection in cells, and there is no rapid, high-throughput method to assess the status of viral infection. On the basis of the virus light diffraction fingerprint of cells, we applied the gray co-occurrence matrix, set the two parameters effectively to distinguish the virus status and infection time of cells, and visualized the virus infection process of cells in high throughput. We provide an efficient and nondestructive testing method for the selection of excellent livestock and poultry breeds at the cellular level. Meanwhile, our work provides detection methods for the recessive transmission of human-to-human, animal-to-animal, and zoonotic diseases and to inhibit and block their further development.

Detection of Parasites in the Field: The Ever-Innovating CRISPR/Cas12a

The rapid and accurate identification of parasites is crucial for prompt therapeutic intervention in parasitosis and effective epidemiological surveillance. For accurate and effective clinical diagnosis, it is imperative to develop a nucleic-acid-based diagnostic tool that combines the sensitivity and specificity of nucleic acid amplification tests (NAATs) with the speed, cost-effectiveness, and convenience of isothermal amplification methods. A new nucleic acid detection method, utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) nuclease, holds promise in point-of-care testing (POCT). CRISPR/Cas12a is presently employed for the detection of Plasmodium falciparum, Toxoplasma gondii, Schistosoma haematobium, and other parasites in blood, urine, or feces. Compared to traditional assays, the CRISPR assay has demonstrated notable advantages, including comparable sensitivity and specificity, simple observation of reaction results, easy and stable transportation conditions, and low equipment dependence. However, a common issue arises as both amplification and cis-cleavage compete in one-pot assays, leading to an extended reaction time. The use of suboptimal crRNA, light-activated crRNA, and spatial separation can potentially weaken or entirely eliminate the competition between amplification and cis-cleavage. This could lead to enhanced sensitivity and reduced reaction times in one-pot assays. Nevertheless, higher costs and complex pre-test genome extraction have hindered the popularization of CRISPR/Cas12a in POCT.