Biosensors & Bioelectronics

A Proof-of-Concept study was conducted to investigate the feasibility of a breast tumor monitoring system using a flexible vest embedded with ultrawideband antennas. The motivation behind this study is to provide a noninvasive, real-time, and continuous monitoring solution for breast cancer, a disease that continues to be one of the most common causes of death among women worldwide. Traditional imaging techniques such as mammography, ultrasound, and magnetic resonance imaging have limitations in terms of cost, radiation exposure, and accessibility, making it imperative to explore alternative methods that offer patient comfort, cost-effectiveness, and high accuracy [1]. The proposed system aims to address these challenges by utilizing UWB technology, which can offer high resolution and deep tissue penetration while minimizing the potential risks associated with radiation.

The development of an electrochemical biosensor for detecting pulmonary embolism and myocardial infarction represents a significant advancement in point-of-care diagnostics for critical cardiovascular conditions. Both conditions pose severe threats to human health, requiring timely diagnosis to prevent mortality and mitigate complications. Traditional diagnostic methods, such as computed tomography pulmonary angiography for PE and electrocardiography or troponin assays for MI, often involve time-consuming procedures, specialized equipment, and hospital-based settings. An electrochemical biosensor offers a promising alternative, providing rapid, sensitive, and portable diagnostics for these life-threatening conditions [1]. The biosensor operates on the principle of electrochemical transduction, which converts a biochemical interaction into a measurable electrical signal. This technology is particularly suitable for medical diagnostics due to its high sensitivity, specificity, and ability to function in compact, portable devices. In this context, the biosensor was designed to detect specific biomarkers associated with PE and MI, namely D-dimer and cardiac troponins, respectively. D-dimer is a fibrin degradation product that is elevated in the bloodstream during thrombotic events, such as those leading to pulmonary embolism. Similarly, cardiac troponins, specifically troponin I and T, are released into the bloodstream following myocardial injury, making them reliable indicators of myocardial infarction

The application of lactase enzymes as bioreceptors in surface plasmon resonance (SPR) biosensors for detecting organic dyes, specifically methylene blue (MB), represents a novel approach to environmental monitoring and biosensor technology. Methylene blue, a commonly used dye in textile and pharmaceutical industries, poses significant environmental concerns due to its toxicity and persistence in aquatic ecosystems. Conventional methods for detecting methylene blue, such as spectrophotometry or chromatography, often require sophisticated equipment, extensive sample preparation, and high costs. In contrast, SPR biosensors offer a label-free, real-time, and highly sensitive detection platform, making them ideal for monitoring organic pollutants like methylene blue [1]. Lactase enzymes, also known as β-galactosidases, are traditionally used in food and pharmaceutical industries for their ability to hydrolyze lactose into glucose and galactose. However, their potential as bioreceptors in biosensors is gaining attention due to their specificity and ability to interact with certain organic compounds, including dyes. The enzyme’s structure, featuring an active site that can bind selectively to specific molecules, makes it an excellent candidate for detecting methylene blue. By immobilizing lactase onto the sensor surface, the SPR biosensor can harness its specificity to bind methylene blue, leading to measurable changes in the refractive index near the sensor surface.

The diagnostic accuracy of interferon-gamma release assays for detecting Mycobacterium tuberculosis infection has been a focal point of tuberculosis diagnostics, especially given the limitations of traditional methods like the tuberculin skin test. IGRAs measure the release of interferon-gamma from sensitized T-cells in response to specific Mtb antigens, providing a more specific and reliable diagnostic tool for both latent tuberculosis infection and, to some extent, active TB. These assays were assessed for their sensitivity, specificity, and overall diagnostic accuracy under varying clinical and demographic conditions. The study enrolled a diverse cohort of participants, including individuals with confirmed active TB, those with presumed latent TB infection, and healthy controls with no history of TB exposure. Participants included adults and children from high-burden and low-burden TB regions to ensure that the assays were evaluated across a range of epidemiological and immunological contexts. The diagnosis of active TB was confirmed through microbiological evidence, such as positive sputum cultures or polymerase chain reaction tests, while latent TB infection was defined based on exposure history and positive TST results.

The integration of artificial intelligence into healthcare has garnered significant attention, particularly in the domain of continuous vital sign monitoring for in-hospital patients. Continuous monitoring systems, augmented by AI algorithms, promise to revolutionize patient care by enabling early detection of clinical deterioration, reducing the burden on healthcare staff, and improving patient outcomes [1]. These systems leverage AI to analyze large volumes of data in real-time, identifying subtle patterns and anomalies that might elude human observation. However, despite these promises, discrepancies often exist between the anticipated capabilities of AI-driven systems and their actual performance in clinical settings. This review critically examines these gaps, highlighting the evidence from current research and real-world applications.

The increasing demand for reliable and rapid detection methods in food safety has led to the exploration of innovative technologies. Nanoporebased sequencing platforms represent one such promising advancement, offering the capability for real-time, high-throughput, and portable analysis. These platforms leverage nanopore technology, where single molecules of DNA or RNA pass through a nanopore, generating an electrical signal that is subsequently translated into a nucleotide sequence. This study investigates the quantification potential of such a platform for food safety applications, focusing on the use of external standards like lambda DNA and lambda-spiked beef samples. Lambda DNA, derived from the bacteriophage lambda, serves as a model system in molecular biology due to its well-characterized genome. It provides a reliable external standard for evaluating the performance and quantification capacity of nanopore sequencing systems. The uniformity and stability of lambda DNA make it an ideal candidate for calibration and performance assessments. In this context, lambda DNA was utilized to assess the precision, reproducibility, and sensitivity of a nanopore sequencing platform, laying the foundation for its application in food safety

The integration of machine learning and wearable technology has opened new possibilities for the continuous monitoring of biomedical signals, offering profound implications for personalized healthcare. One particularly promising application lies in detecting early indicators of migraines by monitoring physiological changes during pre-migraine nights. Migraines are a debilitating neurological condition that affects millions worldwide, characterized by recurring headaches often accompanied by other symptoms such as nausea, sensitivity to light, and aura. Understanding the subtle biomedical signal pattern changes that precede a migraine could provide an opportunity for early intervention, potentially mitigating the severity of symptoms or preventing the onset entirely. Wearable devices have become increasingly sophisticated, capable of monitoring a range of physiological parameters such as heart rate, skin temperature, blood oxygen saturation, electro dermal activity, and sleep patterns. When paired with machine learning algorithms, these devices can analyze complex, multidimensional data streams to identify patterns indicative of an impending migraine. The ability to collect longitudinal data from wearable technology provides a unique advantage for detecting subtle changes in physiology, which might be challenging to observe in clinical settings or through self-reporting alone

The development of innovative biosensing technologies for the early detection of prostate cancer has garnered significant attention in recent years. Among these technologies, portable Amperometric biosensors have shown immense promise due to their sensitivity, specificity, portability, and rapid response times. The integration of enzyme-based ternary nanocomposites into these biosensors represents a groundbreaking advancement, offering enhanced detection capabilities for prostate cancer biomarkers. This discussion explores the principles, design, and advantages of these biosensors, along with their potential for revolutionizing prostate cancer diagnostics. Prostatespecific antigen is the most widely used biomarker for detecting prostate cancer. Elevated PSA levels in blood serum are often indicative of the presence or progression of the disease. While conventional methods such as enzymelinked immunosorbent assays are effective for PSA detection, they are often time-consuming, expensive, and require specialized laboratory equipment. In contrast, Amperometric biosensors provide a faster, cost-effective, and portable alternative, enabling point-of-care testing and real-time monitoring. The integration of enzyme-based ternary nanocomposites into the biosensor architecture significantly enhances its performance, addressing key challenges in biomarker detection.

The detection of rheumatoid arthritis a chronic autoimmune disorder, relies on early diagnosis to manage symptoms and slow disease progression effectively. Among the most specific biomarkers for RA are anti-cyclic citrullinated peptide antibodies, which often appear in the bloodstream long before clinical symptoms manifest. Conventional laboratory-based diagnostic methods, while effective, are time-consuming, require specialized facilities, and are inaccessible to many patients in low-resource settings. To address these limitations, rapid microfluidic biosensors have emerged as a promising solution for point-of-care detection of RA through the analysis of anti-CCP antibodies. These biosensors combine the principles of microfluidics and advanced bio-detection techniques, offering a fast, portable, and highly sensitive alternative for early diagnosis. Microfluidic biosensors are devices that utilize microchannels to manipulate small volumes of fluids, enabling the precise control and analysis of biological samples. By miniaturizing diagnostic processes, these devices achieve remarkable speed and efficiency while reducing reagent and sample consumption. For detecting anti-CCP antibodies, microfluidic biosensors are designed with functionalized surfaces that bind specifically to these biomarkers. The specificity of this interaction ensures accurate detection, even in complex biological matrices such as blood, serum, or synovial fluid.

Smartphone-based electrochemiluminescence biosensors represent a groundbreaking advancement in the field of analytical chemistry and biomedical diagnostics. These systems offer an innovative, fully integrated portable platform that combines the capabilities of electrochemiluminescence with the computational power of deep learning algorithms. This convergence enables highly sensitive, real-time visual monitoring of various analytes, making it particularly appealing for point-of-care testing, environmental monitoring, and clinical diagnostics. The integration of smartphones, with their ubiquitous presence, high-resolution cameras, and advanced processing capabilities, further democratizes access to sophisticated diagnostic tools. At the heart of this technology lies electrochemiluminescence, a phenomenon that combines the principles of electrochemistry and luminescence. In ECL systems, chemical reactions at the electrode surface generate excited-state luminophores, which emit light upon returning to their ground state. The intensity of this light correlates directly with the concentration of the target analyte, providing a quantitative measure that can be visually monitored. Traditionally, ECL systems have been constrained to laboratory settings due to the need for bulky instrumentation, precise control systems, and complex data analysis tools. However, the advent of miniaturized electrochemical cells, coupled with the integration of smartphone technologies, has paved the way for portable and user-friendly ECL biosensors