Tumor metastasis presents a formidable challenge in cancer treatment, necessitating effective tools for anti-cancer drug development. Conventional 2D cell culture methods, while considered the \"gold standard\" for invasive studies, exhibit limitations in representing cancer hallmarks and phenotypes. This study proposes an innovative approach that combines the advantages of 3D tumor spheroid culture with impedance-based biosensing technologies to establish a high-throughput 3D cell invasion assay for anti-metastasis drug screening through multicellular tumor spheroids. In addition, the xCELLigence device is employed to monitor the time-dependent kinetics of cell behavior, including attachment and invasion out of the 3D matrix. Moreover, an iron chelator (deferoxamine) is employed to monitor the inhibition of epithelial-mesenchymal transition in 3D spheroids across different tumor cell types. The above results indicate that our integrated 3D cell invasion assay with impedance-based sensing could be a promising tool for enhancing the quality of the drug development pipeline by providing a robust platform for predicting the efficacy and safety of anti-metastatic drugs before advancing into preclinical or clinical trials.

Cell culture is a fundamental tool for cell-based assays in biological and preclinical research. The measurements of cell culture, including cell count, viability, and metabolic activity, can reflect the conditions of cells under culture conditions. The conventional cell culture and detection methods have problems such as high consumption of reagents and samples, inability to monitor cell status in real time, and difficulty in spatiotemporally adjusting the cell microenvironment. A cell impedance sensor measures changes in the electrical impedance of cells through alternating current, enabling real-time monitoring of impedance changes caused by cell activities such as attachment, growth, proliferation, and migration. Microfluidic chips are praised for reducing complex biological processes, integrating multiple analysis modes, and achieving high automation in detection. Integrating microfluidic chips with cell impedance sensors greatly improves the capability and efficiency of cell-related analysis. This review outlines the application of microfluidic chip-based impedance sensors in 2D and 3D cell systems and summarizes the research progress in application of such sensors in research on cell growth, proliferation, viability, metabolic activity, and drug screening. Finally, this review prospects the future development trends and possible challenges, providing ideas for the development of microfluidic chips integrated with electrical impedance sensors in drug screening.

Cerebrospinal fluid (CSF) is a body fluid that can be used for the diagnosis of various diseases. However, CSF collection requires an invasive and painful procedure called a lumbar puncture (LP). This procedure is applied to any patient with a known risk of central nervous system (CNS) damage or neurodegenerative disease, regardless of their age range. Hence, this can be a very painful procedure, especially in infants and elderly patients. On the other hand, the detection of disease biomarkers in CSF makes diagnoses as accurate as possible. This review aims to explore novel electrochemical biosensing platforms that have impacted biomedical science. Biosensors have emerged as techniques to accelerate the detection of known biomarkers in body fluids such as CSF. Biosensors can be designed and modified in various ways and shapes according to their ultimate applications to detect and quantify biomarkers of interest. This process can also significantly influence the detection and diagnosis of CSF. Hence, it is important to understand the role of this technology in the rapidly progressing field of biomedical science.

Microbial foodborne pathogens present significant challenges to public health and the food industry, requiring rapid and accurate detection methods to prevent infections and ensure food safety. Conventional single biosensing techniques often exhibit limitations in terms of sensitivity, specificity, and rapidity. In response, there has been a growing interest in multimodal biosensing approaches that combine multiple sensing techniques to enhance the efficacy, accuracy, and precision in detecting these pathogens. This review investigates the current state of multimodal biosensing technologies and their potential applications within the food industry. Various multimodal biosensing platforms, such as opto-electrochemical, optical nanomaterial, multiple nanomaterial-based systems, hybrid biosensing microfluidics, and microfabrication techniques are discussed. The review provides an in-depth analysis of the advantages, challenges, and future prospects of multimodal biosensing for foodborne pathogens, emphasizing its transformative potential for food safety and public health. This comprehensive analysis aims to contribute to the development of innovative strategies for combating foodborne infections and ensuring the reliability of the global food supply chain.

RNA nanotechnology harnesses the unique chemical and structural properties of RNA to build nanoassemblies and supramolecular structures with dynamic and functional capabilities. This review focuses on design and assembly approaches to building RNA structures, the RNA chemical modifications used to enhance stability and functionality, and modern-day applications in therapeutics, biosensing, and bioimaging.

The intracellular developmental processes in plants, particularly concerning lignin polymer formation and biomass production are regulated by microRNAs (miRNAs). MiRNAs including miR397b are important for developing efficient and cost-effective biofuels. However, traditional methods of monitoring miRNA expression, like PCR, are time-consuming, require sample extraction, and lack spatial and temporal resolution, especially in real-world conditions. We present a novel approach using plasmonics nanosensing to monitor miRNA activity within living plant cells without sample extraction. Plasmonic biosensors using surface-enhanced Raman scattering (SERS) detection offer high sensitivity and precise molecular information. We used the Inverse Molecular Sentinel (iMS) biosensor on unique silver-coated gold nanorods (AuNR@Ag) with a high-aspect ratio to penetrate plant cell walls for detecting miR397b within intact living plant cells. MiR397b overexpression has shown promise in reducing lignin content. Thus, monitoring miR397b is essential for cost-effective biofuel generation. This study demonstrates the infiltration of nanorod iMS biosensors and detection of non-native miRNA 397b within plant cells for the first time. The investigation successfully demonstrates the localization of nanorod iMS biosensors through TEM and XRF-based elemental mapping for miRNA detection within plant cells of Nicotiana benthamiana. The study integrates shifted-excitation Raman difference spectroscopy (SERDS) to decrease background interference and enhance target signal extraction. In vivo SERDS testing confirms the dynamic detection of miR397b in Arabidopsis thaliana leaves after infiltration with iMS nanorods and miR397b target. This proof-of-concept study is an important stepping stone towards spatially resolved, intracellular miRNA mapping to monitor biomarkers and biological pathways for developing efficient renewable biofuel sources.

With their regulated Boolean logic operations in vitro and in vivo, DNA logic circuits have shown great promise for target recognition and disease diagnosis. However, significant obstacles must be overcome to improve their operational efficiency and broaden their range of applications. In this study, we propose an Exo III-powered closed-loop DNA circuit (ECDC) architecture that integrates four highly efficient AND logic gates. The ECDC utilizes Exo III as the sole enzyme-activated actuator, simplifying the circuit design and ensuring optimal performance. Moreover, the use of Exo III enables a self-feedback (autocatalytic) mechanism in the dynamic switching between AND logic gates within this circulating logic circuit. After validating the signal flow and examining the impact of each AND logic gate on the regulation of the circuit, we demonstrate the intelligent determination of miR-21 using the carefully designed ECDC architecture in vitro. The proposed ECDC exhibits a linear detection range for miR-21 from 0 to 300 nM, with a limit of detection (LOD) of approximately 0.01 nM, surpassing most reported methods. It also shows excellent selectivity for miR-21 detection and holds potential for identifying and imaging live cancer cells. This study presents a practical and efficient strategy for monitoring various nucleic acid-based biomarkers in vitro and in vivo through specific sequence modifications, offering significant potential for early cancer diagnosis, bioanalysis, and prognostic clinical applications.

Comprehending digital content written in natural language online is vital for many aspects of life, including learning, professional tasks, and decision-making. However, facing comprehension difficulties can have negative consequences for learning outcomes, critical thinking skills, decision-making, error rate, and productivity. This paper introduces an innovative approach to predict comprehension difficulties at the local content level (e.g., paragraphs). Using affordable wearable devices, we acquire physiological responses non-intrusively from the autonomous nervous system, specifically pulse rate variability, and electrodermal activity. Additionally, we integrate data from a cost-effective eye-tracker. Our machine learning algorithms identify \'hotspots\' within the content and regions corresponding to a high cognitive load. These hotspots represent real-time predictors of comprehension difficulties. By integrating physiological data with contextual information (such as the levels of experience of individuals), our approach achieves an accuracy of 72.11% ± 2.21, a precision of 0.77, a recall of 0.70, and an f1 score of 0.73. This study opens possibilities for developing intelligent, cognitive-aware interfaces. Such interfaces can provide immediate contextual support, mitigating comprehension challenges within content. Whether through translation, content generation, or content summarization using available Large Language Models, this approach has the potential to enhance language comprehension.

Since the proposal of the concept of spherical nucleic acids (SNAs) in 1996, numerous studies have focused on this topic and have achieved great advances. As a new delivery system for nucleic acids, SNAs have advantages over conventional deoxyribonucleic acid (DNA) nanostructures, including independence from transfection reagents, tolerance to nucleases, and lower immune reactions. The flexible structure of SNAs proves that various inorganic or organic materials can be used as the core, and different types of nucleic acids can be conjugated to realize diverse functions and achieve surprising and exciting outcomes. The special DNA nanostructures have been employed for immunomodulation, gene regulation, drug delivery, biosensing, and bioimaging. Despite the lack of rational design strategies, potential cytotoxicity, and structural defects of this technology, various successful examples demonstrate the bright and convincing future of SNAs in fields such as new materials, clinical practice, and pharmacy.

The advantageous optical properties of quantum dots (QDs) motivate their use in a wide variety of applications related to imaging and bioanalysis, including the detection of proteases and their activity. Recent studies have shown that surface chemistry on QDs is able to modulate protease activity, but only nonspecifically. Here, we present a strategy to selectively accelerate the activity of a particular target protease by as much as two orders of magnitude. Exosite-binding \"bait\" peptides were derived from proteins that span a range of biological roles─substrate, receptor, and inhibitor─and were used to increase the affinity of the QD-peptide conjugates for either thrombin or factor Xa, resulting in increased rates of proteolysis for coconjugated substrates. Unlike effects from QD surface chemistry, the acceleration was specific to the target protease with negligible acceleration of other proteases. Benefits of this \"bait and cleave\" sensing approach included detection limits that improved by more than an order of magnitude, reenabled detection of target protease against an overwhelming background of nontarget proteolysis, and mitigation of the action of inhibitors. The cumulative results point to a generalizable strategy, where the mechanism of acceleration, considerations for the design of bait peptides and conjugates, and routes to expanding the scope of this approach are discussed. Overall, this research represents a major step forward in the rational design of nanoparticle-based enzyme sensors that enhance sensitivity and selectivity.