Current smartphone-assisted point-of-care cancer detection: Towards supporting personalized cancer monitoring

The novel nanozyme-based electrochemical-driven electrochromic visual biosensor based on PEDOT:PSS/RGO conductive film for rapid detection of nitrite in food samples

An efficient and facile nitrite (NO2-) detection system was developed using Fe3O4@Au-Cu/MOF, which was manufactured through self-assembly as the nanozyme, and a PEDOT:PSS/RGO thin film produced by chemical synthesis as the counter electrode, in conjunction with smartphone-based colorimetry. The Fe3O4@Au-Cu/MOF nanozyme exhibits remarkable catalytic efficiency and can significantly enhance the conversion of NO2-. PEDOT:PSS/RGO films exhibit outstanding electron transport and electrochromic properties. The color of PEDOT:PSS/RGO films can be modified by applying voltage and the electronic current generated by NO2- within the reaction system. The colorimetric assessment of film color alteration using a smartphone, supplemented by electrochemical validation. Under ideal conditions, the sensor detected NO2- within a linear range of 0.01 to 100 mmol/L and exhibited a detection limit of 3.37 μmol/L. This method demonstrated no significant difference compared to the results obtained using the electrochemical method and was effectively employed for the detection of NO2- in real samples.

Colorimetric detection of glucose in food using gold nanoparticles as nanoenzymes combined with a portable smartphone-assisted microfluidic paper-based analysis device

Glucose is an important source of energy for the human body, but excessive intake will destroy the body's metabolic balance and increase health risks. In this paper, a smartphone glucose colorimetric detection system was developed in a paper-based microfluidic analytical device (µPAD) using green synthetic gold nanoparticles (AuNPs) as probes for accurate, rapid and efficient quantitative analysis of glucose in food. The AuNPs, acting as mimetic enzymes, were capable of catalyzing the breakdown of H2O2 generated by the interaction of glucose oxidase (GOx) with glucose to OH, which subsequently oxidized the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), resulting in a typical green reaction. This reaction was integrated into a µPAD and the color change on the paper chip was colorimetrically analyzed using a smartphone, with UV-Vis verification. Under optimal conditions, the sensor exhibited a linear range for detecting glucose from 0.050 to 5.250 mmol/L and a limit of detection (LOD) of 7.615 µmol/L. The developed approach showed no significant deviation from UV-Vis determination and has good selectivity, as well as proved to be effective for detecting glucose in real samples. This platform for glucose detection combines green synthesized AuNPs, μPAD and a smartphone app, offering features such as intelligence, portability, speed, low cost, high sensitivity and high selectivity. And it holds great potential for broad applications in the field of food analysis.

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.

Tailoring Design of Microneedles for Drug Delivery and Biosensing

Microneedles (MNs) are emerging as versatile tools for both therapeutic drug delivery and diagnostic monitoring. Unlike hypodermic needles, MNs achieve these applications with minimal or no pain and customizable designs, making them suitable for personalized medicine. Understanding the key design parameters and the challenges during contact with biofluids is crucial to optimizing their use across applications. This review summarizes the current fabrication techniques and design considerations tailored to meet the distinct requirements for drug delivery and biosensing applications. We further underscore the current state of theranostic MNs that integrate drug delivery and biosensing and propose future directions for advancing MNs toward clinical use.

Integration of Immunosyringe Sensors with Bar-Chart Chips for Prick-and-Read Testing of Prostate Specific Antigen

Pressure-based signal transduction is of promise in developing microfluidic immunoassays such as volumetric bar-chart chips (V-chips), but new working principles are required to further simplify the methods in point-of-care testing (POCT). Herein, we developed immunosyringe sensors and integrated them with bar-chart chips for simple prick-and-read testing of prostate specific antigen (PSA) as a model target. Disposable syringes served as the host for the construction of the sandwich-type immuno-recognition system. Platinum nanoparticles (Pt NPs) as the peroxidase-mimicking detection probe catalyzed the decomposition of H2O2 to produce O2 in the syringe cylinders, enabling the pressure-driven automatic injection of liquids from the syringes. The immuno-recognition event in the syringes was thereby converted into the quantitative autoinjection behavior of the syringes, namely, immunosyringe sensors. By simply pricking the sensors to bar-chart chips, we visually and quantitatively read the immunoassay signals as the bar-chart injection distance of liquids from the syringes in channels of the chips. The immunoassay showed a limit of detection (LOD) of 0.41 ng/mL in PSA detection with satisfactory accuracy in testing clinical serum samples. Owing to the integration with the immunosyringe sensors, this method, in comparison with conventional V-chips, works in a simpler prick-and-read manner without complex chip configurations and specialized chip operations (e.g., on-chip loading of microvolume reagents and sealing treatments). Therefore, the immunoassay shows great potential in POCT applications.

A New Caffeine Detection Method Using a Highly Multiplexed Smartphone-Based Spectrometer

Smartphones equipped with highly integrated sensors are increasingly being recognized as powerful tools for rapid on-site testing. Here, we propose a low-cost, portable, and highly multiplexed smartphone-based spectrometer capable of collecting three types of spectra-transmission, reflection, and fluorescence-by simply replacing the optical fiber attached to the housing. Spectral analysis is performed directly on the smartphone using a custom-developed app. Furthermore, we introduce a high signal-to-noise ratio (SNR) caffeine detection scheme that leverages aspirin and salicylic acid as fluorescent probes, allowing for the rapid and straightforward detection of caffeine in various samples. The fluorescence quenching of the probes was found to be linearly related to the caffeine concentration (0-200 μM), and the recoveries of the commercially available caffeine-containing samples were in the range of 98.0333-105.6000%, with a limit of detection (LOD) of 2.58 μM. The reliability and stability of the on-site assay using the smartphone spectrometer were verified. More importantly, this spectrometer demonstrates great potential as a versatile device for use outside of laboratory settings by enabling different operating modes tailored to various scenarios.

Deep learning for early diagnosis of oral cancer via smartphone and DSLR image analysis: a systematic review

INTRODUCTION

Diagnosing oral cancer is crucial in healthcare, with technological advancements enhancing early detection and outcomes. This review examines the impact of handheld AI-based tools, focusing on Convolutional Neural Networks (CNNs) and their advanced architectures in oral cancer diagnosis.

METHODS

A comprehensive search across PubMed, Scopus, Google Scholar, and Web of Science identified papers on deep learning (DL) in oral cancer diagnosis using digital images. The review, registered with PROSPERO, employed PRISMA and QUADAS-2 for search and risk assessment, with data analyzed through bubble and bar charts.

RESULTS

Twenty-five papers were reviewed, highlighting classification, segmentation, and object detection as key areas. Despite challenges like limited annotated datasets and data imbalance, models such as DenseNet121, VGG19, and EfficientNet-B0 excelled in binary classification, while EfficientNet-B4, Inception-V4, and Faster R-CNN were effective for multiclass classification and object detection. Models achieved up to 100% precision, 99% specificity, and 97.5% accuracy, showcasing AI's potential to improve diagnostic accuracy. Combining datasets and leveraging transfer learning enhances detection, particularly in resource-limited settings.

CONCLUSION

Handheld AI tools are transforming oral cancer diagnosis, with ethical considerations guiding their integration into healthcare systems. DL offers explainability, builds trust in AI-driven diagnoses, and facilitates telemedicine integration.

An updated review on the development of a nanomaterial-based field-effect transistor-type biosensors to detect exosomes for cancer diagnosis

Cancer, a life-threatening genetic disease caused by abnormalities in normal cell growth regulatory functions, poses a significant challenge that current medical technologies cannot fully overcome. The current desired breakthrough is to diagnose cancer as early as possible and increase survival rates through treatments tailored to the prognosis and appropriate follow-up. From a perspective that reflects this contemporary paradigm of cancer diagnostics, exosomes are emerging as promising biomarkers. Exosomes, serving as mobile biological information repositories of cancer cells, have been known to create a microtumor environment in surrounding cells, and significant insight into the clinical significance of cancer diagnosis targeting them has been reported. Therefore, there are growing interests in constructing a system that enables continuous screening with a focus on patient-friendly and flexible diagnosis, aiming to improve cancer screening rates through exosome detection. This review focuses on a proposed exosome-embedded biological information-detecting platform employing a field-effect transistor (FET)-based biosensor that leverages portability, cost-effectiveness, and rapidity to minimize the stages of sacrifice attributable to cancer. The FET-applied biosensing technique, stemming from variations in an electric field, is considered an early detection system, offering high sensitivity and a prompt response frequency for the qualitative and quantitative analysis of biomolecules. Hence, an in-depth discussion was conducted on the understanding of various exosome-based cancer biomarkers and the clinical significance of recent studies on FET-based biosensors applying them.

Biominerals and Bioinspired Materials in Biosensing: Recent Advancements and Applications

Inspired by nature's remarkable ability to form intricate minerals, researchers have unlocked transformative strategies for creating next-generation biosensors with exceptional sensitivity, selectivity, and biocompatibility. By mimicking how organisms orchestrate mineral growth, biomimetic and bioinspired materials are significantly impacting biosensor design. Engineered bioinspired materials offer distinct advantages over their natural counterparts, boasting superior tunability, precise controllability, and the ability to integrate specific functionalities for enhanced sensing capabilities. This remarkable versatility enables the construction of various biosensing platforms, including optical sensors, electrochemical sensors, magnetic biosensors, and nucleic acid detection platforms, for diverse applications. Additionally, bioinspired materials facilitate the development of smartphone-assisted biosensing platforms, offering user-friendly and portable diagnostic tools for point-of-care applications. This review comprehensively explores the utilization of naturally occurring and engineered biominerals and materials for diverse biosensing applications. We highlight the fabrication and design strategies that tailor their functionalities to address specific biosensing needs. This in-depth exploration underscores the transformative potential of biominerals and materials in revolutionizing biosensing, paving the way for advancements in healthcare, environmental monitoring, and other critical fields.