UNASSIGNED: The increase in diabetes cases has become a major concern in the healthcare sector, necessitating the development of efficient and minimal diagnostic methods. This study aims to provide a comprehensive examination of electrochemical biosensors for detecting diabetes mellitus biomarkers, with a special focus on the utilization of carbon-based electrodes.
UNASSIGNED: A detailed analysis of electrochemical biosensors incorporating various carbon electrodes, including screen-printed carbon electrodes, glassy carbon electrodes, and carbon paste electrodes, is presented. The advantages of carbon-based electrodes in biosensor design are highlighted. The review covers the detection of several key diabetes biomarkers, such as glucose, glycated hemoglobin (HbA1c), glycated human serum albumin (GHSA), insulin, and novel biomarkers.
UNASSIGNED: Recent developments in electrochemical biosensor technology over the last decade are summarized, emphasizing their potential in clinical applications, particularly in point-of-care settings. The utilization of carbon-based electrodes in biosensors is shown to offer significant advantages, including enhanced sensitivity, selectivity, and cost-effectiveness.
UNASSIGNED: This review underscores the importance of carbon-based electrodes in the design of electrochemical biosensors and raises awareness for the detection of novel biomarkers for more specific and personalized diabetes mellitus cases. The advancements in this field highlight the potential of these biosensors in future clinical applications, especially in point-of-care diagnostics.

Sepsis is a life-threatening condition with high mortality rates due to delayed treatment of patients. The conventional methodology for blood diagnosis takes several hours, which suspends treatment, limits early drug administration, and affects the patient\'s recovery. Thus, rapid, accurate, bedside (onsite), economical, and reliable sepsis biomarker reading of the clinical sample is an emergent need for patient lifesaving. Electrochemical label-free biosensors are specific and rapid devices that are able to perform analysis at the patient\'s bedside; thus, they are considered an attractive methodology in a clinical setting. To reveal their full diagnostic potential, electrode architecture strategies of fabrication are highly desirable, particularly those able to preserve specific antibody-antigen attraction, restrict non-specific adsorption, and exhibit high sensitivity with a low detection limit for a target biomarker. The aim of this review is to provide state-of-the-art methodologies allowing the fabrication of ultrasensitive and highly selective electrochemical sensors for sepsis biomarkers. This review focuses on different methods of label-free biomarker sensors and discusses their advantages and disadvantages. Then, it highlights effective ways of avoiding false results and the role of molecular labels and functionalization. Recent literature on electrode materials and antibody grafting strategies is discussed, and the most efficient methodology for overcoming the non-specific attraction issues is listed. Finally, we discuss the existing electrode architecture for specific biomarker readers and promising tactics for achieving quick and low detection limits for sepsis biomarkers.

Detecting and quantifying amino acids is vital in biochemical analyses, especially for diagnosing metabolic disorders. L-proline, among these amino acids, holds significant relevance for various metabolic disorders in living organisms, particularly in humans. hyperprolinemia arises when ineffective breakdown of L-proline occurs due to enzyme deficiencies, leading to its accumulation in the body and underscoring the need for precise monitoring. To address this challenge, molecular imprinting offers a reliable single-step technique for detecting target molecules like proteins, peptides, amino acids, or ions with high selectivity. Moreover, nanoparticles, with significant surface area-to-volume ratios, enable high-level mass transfer and binding kinetics, making them ideal for nano-scale sensitive applications. In this study, 2-hydroxyethyl methacrylate-based molecularly imprinted nanoparticles were synthesized via mini-emulsion polymerization, combining the advantages of molecular imprinting technique and nanoparticles for the specific recognition of L-proline, and were well-characterized by Scanning Electron Microscopy, zeta-sizer particle size analysis, and Fourier Transform Infrared Spectroscopy. Based on zeta-sizer analysis, the estimated diameters of L-proline-imprinted and non-imprinted nanoparticles (Pro-MIPs and NIPs) were determined to be approximately 27.51 nm and 20.66 nm, respectively. The adsorption of L-proline onto nanoparticles from aqueous solutions was investigated in a batch system, and the maximum L-proline adsorption capacity was determined to be 26.58 mg/g for Pro-MIPs and 4.65 mg/g for and NIPs. The selectivity of Pro-MIPs was assessed using Liquid Chromatography-Tandem Mass Spectrometry, even in human serum and in the presence of competing molecules (L-histidine and L-phenylalanine). Additionally, Pro-MIPs maintained their adsorption capacity through up to 10 adsorption-desorption cycles without significant decrease.

Wearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals\' physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a three-dimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise.

Liquid biopsy is expected to become widespread in the coming years thanks to point of care devices, which can include label-free biosensors. The surface functionalization of biosensors is a crucial aspect that influences their overall performance, resulting in the accurate, sensitive, and specific detection of target molecules. Here, the surface of a microring resonator (MRR)-based biosensor was functionalized for the detection of protein biomarkers. Among the several existing functionalization methods, a strategy based on aptamers and mercaptosilanes was selected as the most highly performing approach. All steps of the functionalization protocol were carefully characterized and optimized to obtain a suitable protocol to be transferred to the final biosensor. The functionalization protocol comprised a preliminary plasma treatment aimed at cleaning and activating the surface for the subsequent silanization step. Different plasma treatments as well as different silanes were tested in order to covalently bind aptamers specific to different biomarker targets, i.e., C-reactive protein, SARS-CoV-2 spike protein, and thrombin. Argon plasma and 1% v/v mercaptosilane were found as the most suitable for obtaining a homogeneous layer apt to aptamer conjugation. The aptamer concentration and time for immobilization were optimized, resulting in 1 µM and 3 h, respectively. A final passivation step based on mercaptohexanol was also implemented. The functionalization protocol was then evaluated for the detection of thrombin with a photonic biosensor based on microring resonators. The preliminary results identified the successful recognition of the correct target as well as some limitations of the developed protocol in real measurement conditions.

In recent years, nanoscale detection has played an increasingly important role in the research on viruses, exosomes, small bacteria, and organelles. The small size and complex biological natures of these particles, with the smallest known virus particle measuring only 17 nm in diameter and exosomes ranging from 30 nm to 150 nm in size, pose challenges to the classical large-scale (typically micron-scale) characterization methods, which has become a major obstacle in the research. The emergence of nanoscale detection and analysis technologies has filled the gap of optical microscopy, a conventional technique in this field. These technologies enable the sensitive and robust detection of objects that exceed the lower limit of optical detection, revealing the molecular composition and biological roles simultaneously. Currently, several commercialized instruments based on nanotechnology have emerged, providing complete single-particle detection solutions and achieving unique functionality based on their respective technological advantages. However, it is inevitable that these technologies have limitations in terms of application and detection capabilities, as they continue to evolve. This paper offers a thorough overview of the principles, advantages, limitations, and future development trends of several mainstream commercial instruments, aiming to serve researchers in selecting and utilizing these technologies.

Objective.Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In practice, these data-driven approaches lead to varying learned features for ADHD classification across diverse ADHD datasets. This phenomenon significantly undermines the reliability of identified biomarkers and hampers the interpretability of these methods.Approach.In this study, we propose a cross-dataset feature selection (FS) module using a grouped SVM-based recursive feature elimination approach (G-SVM-RFE) to enhance biomarker consistency across multiple datasets. Additionally, we employ connectome gradient data for ADHD classification. In details, we introduce the G-SVM-RFE method to effectively concentrate gradient components within a few brain regions, thereby increasing the likelihood of identifying these regions as ADHD biomarkers. The cross-dataset FS module is integrated into an existing binary hypothesis testing (BHT) framework. This module utilizes external datasets to identify global regions that yield stable biomarkers. Meanwhile, given a dataset which waits for implementing the classification task as local dataset, we learn its own specific regions to further improve the performance of accuracy on this dataset.Main results.By employing this module, our experiments achieve an average accuracy of 96.7% on diverse datasets. Importantly, the discriminative gradient components primarily originate from the global regions, providing evidence for the significance of these regions. We further identify regions with the high appearance frequencies as biomarkers, where all the used global regions and one local region are recognized.Significance.These biomarkers align with existing research on impaired brain regions in children with ADHD. Thus, our method demonstrates its validity by providing enhanced biological explanations derived from ADHD mechanisms.

Biomarkers are crucial physiological and pathological indicators in the host. Over the years, numerous detection methods have been developed for biomarkers, given their significant potential in various biological and biomedical applications. Among these, the detection system based on functionalized DNA origami has emerged as a promising approach due to its precise control over sensing modules, enabling sensitive, specific, and programmable biomarker detection. We summarize the advancements in biomarker detection using functionalized DNA origami, focusing on strategies for DNA origami functionalization, mechanisms of biomarker recognition, and applications in disease diagnosis and monitoring. These applications are organized into sections based on the type of biomarkers - nucleic acids, proteins, small molecules, and ions - and concludes with a discussion on the advantages and challenges associated with using functionalized DNA origami systems for biomarker detection.

Porous organic frameworks (POFs) represent a significant subclass of nanoporous materials in the field of materials science, offering exceptional characteristics for advanced applications. Covalent organic frameworks (COFs), as a novel and intriguing type of porous material, have garnered considerable attention due to their unique design capabilities, diverse nature, and wide-ranging applications. The unique structural features of COFs, such as high surface area, tuneable pore size, and chemical stability, render them highly attractive for various applications, including targeted and controlled drug release, as well as improving the sensitivity and selectivity of electrochemical biosensors. Therefore, it is crucial to comprehend the methods employed in creating COFs with specific properties that can be effectively utilized in biomedical applications. To address this indispensable fact, this review paper commences with a concise summary of the different methods and classifications utilized in synthesizing COFs. Second, it highlights the recent advancements in COFs for drug delivery, including drug carriers as well as the classification of drug delivery systems and biosensing, encompassing drugs, biomacromolecules, small biomolecules and the detection of biomarkers. While exploring the potential of COFs in the biomedical field, it is important to acknowledge the limitations that researchers may encounter, which could impact the practicality of their applications. Third, this paper concludes with a thought-provoking discussion that thoroughly addresses the challenges and opportunities associated with leveraging COFs for biomedical applications. This review paper aims to contribute to the scientific community\'s understanding of the immense potential of COFs in improving drug delivery systems and enhancing the performance of biosensors in biomedical applications.

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1-42 (Aβ42) for Alzheimer\'s Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aβ42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment. Here, the measurement is based on the total optical loss detection when specially fabricated sensor heads were immersed into ACSF solutions with and without different concentrations of Aβ42 biomarkers since the bulk refractive index change was performed. Baseline stability and the reference ring down times of the sensor head were measured in the air as 0.87% and 441.6μs ± 3.9μs, respectively. Afterward, the total optical loss of the system was measured when the sensor head was immersed in deionized water, ACSF solution, and ACSF solutions with Aβ42 in different concentrations. The lowest Aβ42 concentration of 2 ppm was detected by LOR. Results showed that LOR fabricated by single-mode fibers for FLRDS system design are promising candidates to be utilized as fiber optic biosensors after sensor head modification and have a high potential for early detection applications of not only AD but possibly also several fatal diseases such as diabetes and cancer.