Natural plants have been attracting increasing attention in biomedical research due to their numerous benefits. Plant exosome-derived vesicles, some of the plant's components, are small nanoscale vesicles secreted by plant cells. These vesicles are rich in bioactive substances and play significant roles in intercellular communication, information transfer, and maintaining homeostasis in organisms. They also hold promise for treating diseases, and their vesicular structures make them suitable carriers for drug delivery, with large-scale production feasible. Therefore, this paper aims to provide an overview of nanovesicles from different plant sources and their extraction methods. We also outline the biological activities of nanovesicles, including their anti-inflammatory, anti-viral, and anti-tumor properties, and systematically introduce their applications in drug delivery. These applications include transdermal delivery, targeted drug delivery, gene delivery, and their potential use in the modern food industry. This review provides new ideas and methods for future research on plant exosomes, including their empowerment by artificial intelligence and gene editing, as well as their potential application in the biomedicine, food, and agriculture industries.

Plant exosome nanovesicles (PENs): green delivery platforms.

Nature-inspired micropatterns

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Recent advances in point-of-care testing of COVID-19.

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. 

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

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. 

Artificial Intelligence−Powered Electrochemical Sensor: Recent Advances, Challenges, and Prospects

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms. 

/pdf/smartphone-based-platforms-implementing-microfluidic-2grh62qi.pdf

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

As data science and instrumentation become key practices in common careers ranging from medicine to agriscience, chemistry as a core introductory course must introduce such topics to students early and at an accessible level. Advanced data acquisition and data science generally require expensive precision instrumentation and massive computation, often out-of-reach even for upper-level undergraduate laboratory courses. At the same time, a new generation of affordable do-it-yourself instruments presents an opportunity for incorporation of curricula focused on instrument design and computation into freshman-level courses. We present a new lab for integration into existing courses that starts with hands-on spectrometer building, moves to data collection, and finally introduces an advanced data science technique, singular value decomposition, at an appropriate level with minimal computing requirements. The hardware and software used are modular and inexpensive. The lab was tested in three community college general chemistry sections over two semesters. Previously, students taking these courses did not typically see advanced quantitative chemistry curricula before deciding whether to pursue a bachelor’s degree. This lab allowed students to practice data collection and organization skills, use prewritten Jupyter notebooks that perform advanced data analysis, and gain presentation skills. A multiwave assessment completed by students highlights both successes and difficulties associated with incorporating multiple advanced topics involving instrument design, data collection, and analysis techniques in a single lab. 

An Instrument Assembly and Data Science Lab for Early Undergraduate Education

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. 

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

Music-Box: Controlling microfluidic flow with customized melodies for point-of-care testing.

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

An Instrument Assembly and Data Science Lab for Early Undergraduate Education

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

Music-Box: Controlling microfluidic flow with customized melodies for point-of-care testing.