Tumor cells may be eliminated by increasing their temperature. This is achieved via photothermal therapy (PTT) by penetrating the tumor tissue with near-infrared light and converting light energy into heat using photothermal agents. Copper sulfide nanoparticles (CuS NPs) are commonly used as PTAs in PTT. In this review, we aimed to discuss the synergism between tumor PTT with CuS NPs and other therapies such as chemotherapy, radiotherapy, dynamic therapies (photodynamic, chemodynamic, and sonodynamic therapy), immunotherapy, gene therapy, gas therapy, and magnetic hyperthermia. Furthermore, we summarized the results obtained with a combination of two treatments and at least two therapies, with PTT as one of the included therapies. Finally, we summarized the benefits and drawbacks of various therapeutic options and state of the art CuS-based PTT and provided future directions for such therapies. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/EXP.20220161

In vivo synergistic tumor therapies based on copper sulfide photothermal therapeutic nanoplatforms

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

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

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

In vivo synergistic tumor therapies based on copper sulfide photothermal therapeutic nanoplatforms