Recent advances in enzymeless-based electrochemical sensors to diagnose neurodegenerative diseases

Electrochemical Biosensor for Point-of-Care Testing of Low-Abundance Biomarkers of Neurological Diseases

The neurofilament protein light chain (NEFL) is a potential biomarker of neurodegenerative diseases, and interleukin-6 (IL-6) is also closely related to neuroinflammation. Especially, NEFL and IL-6 are the two most low-abundance known protein markers of neurological diseases, making their detection very important for the early diagnosis and prognosis prediction of such kinds of diseases. Nevertheless, quantitative detection of low concentrations of NEFL and IL-6 in serum remains quite difficult, especially in the point-of-care test (POCT). Herein, we developed a portable, sensitive electrochemical biosensor combined with smartphones that can be applied to multiple scenarios for the quantitative detection of NEFL and IL-6, meeting the need of the POCT. We used a double-antibody sandwich configuration combined with polyenzyme-catalyzed signal amplification to improve the sensitivity of the biosensor for the detection of NEFL and IL-6 in sera. We could detect NEFL as low as 5.22 pg/mL and IL-6 as low as 3.69 pg/mL of 6 μL of serum within 2 h, demonstrating that this electrochemical biosensor worked well with serum systems. Results also showed its superior detection capabilities over those of high-sensitivity ELISA for serum samples. Importantly, by detecting NEFL and IL-6 in sera, the biosensor showed its potential for the POCT model detection of all known biomarkers of neurological diseases, making it possible for the mass screening of patients with neurodegenerative diseases.

A flexible capacity-metric creatinine sensor based on polygon-shape polyvinylpyrrolidone/CuO and Fe2O3 NRDs electrodeposited on three-dimensional TiO2-V2O5-Polypyrrole nanocomposite

The innovations of the present work include these items; (i) Design and preparation of three-dimensional flexible conductive polymeric nanocomposites (3D-FCPNCs) containing polypyrrole (PPy), V2O5 and TiO2 and modification of their surface with polygon-shape polyvinylpyrrolidone/CuO nanorods (PVP/CuO NRDs) and Fe2O3 NRDs using an hierarchical process based on isoelectric point (IEP), (ii) Application of the prepared surfaces as the flexible enzymeless creatinine sensors using four calibration curves (impedimetric, real capacitance (C'), imaginary capacitance (C″) and double layer capacitance (Cdl)) obtained from electrochemical impedance spectroscopy (EIS) technique. The best results have been obtained using PVP/CuO NRDs-Fe2O3 NRDs/TiO2-V2O5-PPy 3D-FCPNC hierarchical electrode with a wide range of the linear concentration range (10 nmol L-1 -1.3 mmol L-1). Although, determination of creatinine through extraction of parameters such as charge transfer resistance (Rct) and Cdl from measuring impedance at a wide range of frequencies provides useful information about the characteristics of the electrolyte/electrode interface, but measuring real and imaginary capacitances at a specific frequency instead of a wide frequency range can decrease the response time to lower than 1 min. Finally, the prepared hierarchical enzymeless sensors have been successfully used to estimate creatinine concentration in blood serum.

Tetrahedral DNA Nanostructure-Engineered Paper-Based Electrochemical Aptasensor for Fumonisin B1 Detection Coupled with Au@Pt Nanocrystals as an Amplification Label

Fumonisin B1 (FB1), as one of the highest toxicity mycotoxins, poses a serious threat to animal and human health, even at low concentrations. It is significant and challenging to develop a sensitive and reliable analytical device. Herein, a paper-based electrochemical aptasensor was designed utilizing tetrahedral DNA nanostructures (TDNs) to controllably anchor an aptamer (Apt), improving the recognition efficiency of Apt to its target. First, gold nanoparticles (AuNPs)@MXenes were used as a sensing substrate with good conductivity and modified on the electrode for immobilization of complementary DNA-TDNs (cDNA-TDNs). In the absence of FB1, numerous Apt-Au@Pt nanocrystals (NCs) was hybridized with cDNA and assembled on the sensing interface, which accelerated the oxidation of TMB with H2O2 and produced a highly amplified differential pulse voltammetry (DPV) signal. When the target FB1 specifically bound to its Apt, the electrochemical signal was decreased by releasing the Apt-Au@Pt NCs from double-stranded DNA (dsDNA). On account of the strand displacement reaction by FB1 triggering, the aptasensor had a wider dynamic linear range (from 50 fg/mL to 100 ng/mL) with a lower limit of detection (21 fg/mL) under the optimized conditions. More impressively, the designed FB1 aptasensor exhibited satisfactory performance in corn and wheat samples. Therefore, the TDN-engineered sensing platform opens an effective approach for sensitive and accurate analysis of FB1, holding strong potential in food safety and public health.

2D MOF-enhanced SPR sensing platform: Facile and ultrasensitive detection of Sulfamethazine via supramolecular probe

Sulfamethazine (SMZ) is widely present in the environment and can cause severe allergic reactions and cancer in humans. Accurate and facile monitoring of SMZ is crucial for maintaining environmental safety, ecological balance, and human health. In this work, a real-time and label-free surface plasmon resonance (SPR) sensor was devised using a two-dimensional metal-organic framework with superior photoelectric performance as an SPR sensitizer. The supramolecular probe was incorporated at the sensing interface, allowing for the specific capture of SMZ from other analogous antibiotics through host-guest recognition. The intrinsic mechanism of the specific interaction of the supramolecular probe-SMZ was elucidated through the SPR selectivity test in combination with analysis by density functional theory, including p-π conjugation, size effect, electrostatic interaction, π-π stacking, and hydrophobic interaction. This method facilitates a facile and ultrasensitive detection of SMZ with a limit of detection of 75.54 pM. The accurate detection of SMZ in six environmental samples demonstrates the potential practical application of the sensor. Leveraging the specific recognition of supramolecular probes, this direct and simple approach offers a novel pathway for the development of novel SPR biosensors with outstanding sensitivity.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Cholesterol oxidase-immobilized MXene/sodium alginate/silica@n-docosane hierarchical microcapsules for ultrasensitive electrochemical biosensing detection of cholesterol

Electrochemical biosensors usually suffer from the deterioration of detection sensitivity and determination accuracy in a high-temperature environment due to protein denaturation and inactivation of their biological recognition elements such as enzymes. Focusing on an effective solution to this crucial issue, we have developed cholesterol oxidase-immobilized MXene/sodium alginate/silica@n-docosane hierarchical microcapsules as a thermoregulatory electrode material for electrochemical biosensors to meet the requirement of ultrasensitive detection of cholesterol at high temperature. The microcapsules were first fabricated by microencapsulating n-docosane as a phase change material (PCM) in a silica shell, followed by depositing a biocompatible sodium alginate layer, wrapping with electroactive MXene nanosheets and then immobilizing cholesterol oxidase as a biological recognition element for electrochemical biosensing. The fabricated composites not only exhibited a layer-by-layer hierarchical microstructure with the desired chemical and biological components, but also obtained a high latent-heat capacity of over 133 J g^-1 for thermal management through reversible phase transitions of its PCM core. A bare glassy carbon electrode was modified with the developed composites to serve for the cholesterol biosensor. This enables the modified electrode to obtain an in situ thermoregulatory ability to regulate the microenvironmental temperature surrounding the electrode, effectively preventing the protein denaturation of cholesterol oxidase and minimizing heat impact on biosensing performance. Compared to conventional cholesterol biosensors without a PCM, the developed biosensor achieved a higher sensitivity of 4.63 μA μM^-1 cm^-2 and a lower limit of detection of 0.081 μM at high temperature, providing highly accurate and reliable detection of cholesterol for real biological samples over a wide temperature range.

Band gap and defect engineering of bismuth vanadate using La, Ce, Zr dopants to obtain a photoelectrochemical system for ultra-sensitive detection of glucose in blood serum

Bismuth vanadate (BiVO₄) is a promising photoactive material for the design of photoelectrochemical (PEC) analytical devices for the non-enzymatic detection of glucose. In this work, un-doped and La/Ce/Zr doped BiVO₄ photo anodes were developed by spray pyrolysis coating to generate unique 2D hierarchical architectures using the facile ultrasonic spray coating technique without any complex pre or post-treatment. The influence of different dopants on the morphology and photoelectrochemical activity of BiVO₄ coatings was investigated. X-ray diffraction, scanning electron microscopy, UV-vis optical absorbance, and positron annihilation techniques were used to evaluate the structure, defects, and optical properties of BiVO₄ films. DFT simulation confirmed the Zr doping induced band gap reduction in the BiVO₄ lattice. The Zr doping on the Bi site in BiVO₄ lattice provided significantly low Bi and V-based defect density and a higher bulk diffusion length of charge pairs (4 times that of pristine) as well as charge transfer efficiency and this led to the foremost photocurrent for water splitting. The Zr-doped BiVO₄ photo anode showed remarkable sensitivity in glucose sensing. The sensitivity and limit of detection of the Zr-doped BiVO₄ PEC device towards glucose were 0.14 mA cm^-2 mM^-1 and 1.22 μM, respectively, in the concentration range of 1-7 mM. The system showed sensitive detection of glucose in blood serum. This is the first time that a 2D morphology electrode design consisting of Zr-doped BiVO₄, which leads to exceptionally high sensitivity for glucose sensing, has been reported.

Identification of an immune-related gene prognostic index for predicting prognosis, immunotherapeutic efficacy, and candidate drugs in amyotrophic lateral sclerosis

Rationale and objectives

Considering the great insufficiency in the survival prediction and therapy of amyotrophic lateral sclerosis (ALS), it is fundamental to determine an accurate survival prediction for both the clinical practices and the design of treatment trials. Therefore, there is a need for more accurate biomarkers that can be used to identify the subtype of ALS which carries a high risk of progression to guide further treatment.

Methods

The transcriptome profiles and clinical parameters of a total of 561 ALS patients in this study were analyzed retrospectively by analysis of four public microarray datasets. Based on the results from a series of analyses using bioinformatics and machine learning, immune signatures are able to be used to predict overall survival (OS) and immunotherapeutic response in ALS patients. Apart from other comprehensive analyses, the decision tree and the nomogram, based on the immune signatures, were applied to guide individual risk stratification. In addition, molecular docking methodology was employed to screen potential small molecular to which the immune signatures might response.

Results

Immune was determined as a major risk factor contributing to OS among various biomarkers of ALS patients. As compared with traditional clinical features, the immune-related gene prognostic index (IRGPI) had a significantly higher capacity for survival prediction. The determination of risk stratification and assessment was optimized by integrating the decision tree and the nomogram. Moreover, the IRGPI may be used to guide preventative immunotherapy for patients at high risks for mortality. The administration of 2MIU IL2 injection in the short-term was likely to be beneficial for the prolongment of survival time, whose dosage should be reduced to 1MIU if the long-term therapy was required. Besides, a useful clinical application for the IRGPI was to screen potential compounds by the structure-based molecular docking methodology.

Conclusion

Ultimately, the immune-derived signatures in ALS patients were favorable biomarkers for the prediction of survival probabilities and immunotherapeutic responses, and the promotion of drug development.

Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging

The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

Technological advances in electrochemical biosensors for the detection of disease biomarkers

With an increasing focus on health in contemporary society, interest in the diagnosis, treatment, and prevention of diseases has grown rapidly. Accordingly, the demand for biosensors for the early diagnosis of disease is increasing. However, the measurement range of existing electrochemical sensors is relatively high, which is not suitable for early disease diagnosis, requiring the detection of small amounts of biocomponents. Various attempts have been made to overcome this and amplify the signal, including binding with various labeling molecules, such as DNA, enzymes, nanoparticles, and carbon materials. Efforts are also being made to increase the sensitivity of electrochemical sensors, and the combination of nanomaterials, materials, and biotechnology offers the potential to increase sensitivity in a variety of ways. Recent studies suggest that electrochemical sensors can be a powerful tool in providing comprehensive insights into the targeting and detection of disease-associated biomarkers. Significant advances in nanomaterial and biomolecule approaches for improved sensitivity have resulted in the development of electrochemical biosensors capable of detecting multiple biomarkers in real time in clinically relevant samples. In this review, we have discussed the recent studies on electrochemical sensors for detection of diseases such as diabetes, degenerative diseases, and cancer. Further, we have highlighted new technologies to improve sensitivity using various materials, including DNA, enzymes, nanoparticles, and carbon materials.

A Review of Off-On Fluorescent Nanoprobes: Mechanisms, Properties, and Applications

With the development of nanomaterials, fluorescent nanoprobes have attracted enormous attention in the fields of chemical sensing, optical materials, and biological detection. In this paper, the advantages of "off-on" fluorescent nanoprobes in disease detection, such as high sensitivity and short response time, are attentively highlighted. The characteristics, sensing mechanisms, and classifications of disease-related target substances, along with applications of these nanoprobes in cancer diagnosis and therapy are summarized systematically. In addition, the prospects of "off-on" fluorescent nanoprobe in disease detection are predicted. In this review, we presented information from all the papers published in the last 5 years discussing "off-on" fluorescent nanoprobes. This review was written in the hopes of being useful to researchers who are interested in further developing fluorescent nanoprobes. The characteristics of these nanoprobes are explained systematically, and data references and supports for biological analysis, clinical drug improvement, and disease detection have been provided appropriately.