Novel Schiff's base-assisted synthesis of metal-ligand nanostructures for multi-functional applications: Detection of catecholamines/antibiotics, removal of tetracycline, and antifungal treatment against plant pathogens

The development of nanozymes (NZ) for the simultaneous detection of multiple target chemicals is gaining paramount attention in the field of food and health sciences, and waste management industries. Nanozymes (NZ) effectively compensate for the environmental vulnerability of natural enzymes. Considering the development gap of NZ with diverse applications, we synthesized versatile Schiff's base ligands following a facile route and readily available starting reagents (glutaraldehyde, aminopyridines). DPDI, one of the synthesized ligands, readily reacted with transition metal ions (Cu+2, Ag+1, Zn+2 in specific) under ambient conditions, yielding the corresponding nanoparticles/MOF. The structures of ligands and their products were confirmed using various analytical techniques. The enzymatic efficacy of DPDI-Cu (km 0.25 mM=, Vmax = 10.75 µM/sec) surpassed Tremetese versicolor laccase efficacy (km 0. 5 mM=, Vmax = 2.15 µM/sec). Additionally, DPDI-Cu proved resilient to changing pH, temperature, ionic strength, organic solvent, and storage time compared to laccase and provided reusability. DPDI-Cu proved promising for colorimetric detection of dopamine, epinephrine, catechol, tetracycline, and quercetin. The mechanism of oxidative detection of TC was studied through LC/MS analysis. DPDI-Cu-bentonite composite efficiently adsorbed tetracycline with maximum Langmuir adsorption of 208 mg/g. Moreover, DPDI/Cu and DPDI-Ag nanoparticles possessed antifungal activity exhibiting a minimum inhibitory concentration of 400 µg/mL and 3.12 µg/mL against Aspergillus flavus. Florescent dye tracking and SEM/TEM analysis confirmed that DPDI-Ag caused disruption of the plasma membrane and triggered ROS generation and apoptosis-like death in fungal cells. The DPDI-Ag coating treatment of wheat seeds confirmed the non-phytotoxicity of Ag-NPs.

A label-free fluorescence aptamer sensor for point-of-care serotonin detection

Serotonin, a pivotal neurotransmitter regulating various physiological functions, plays a crucial role in disease diagnosis, necessitating precise monitoring of its levels in biological fluids for accurate assessment. Aptamers, known for their high specificity and affinity, have emerged as innovative molecular probes for serotonin analysis. However, existing serotonin aptamer sensing platforms exhibit limitations in terms of portability and rapid detection capabilities. In this study, we introduce a novel, portable, label-free serotonin aptamer sensor utilizing a dye replacement strategy, achieving a short sample-to-result turnaround time and convenient signal readout through a smartphone. The performance of this aptamer sensor was thoroughly assessed across diverse physiological media, demonstrating robust stability in buffer, urine, and serum. Importantly, the detection limit was in the nanomolar range, emphasizing its suitability for the rapid, sensitive, and user-friendly detection of serotonin. This research pioneers an approach for the development of a point-of-care testing (POCT) system for serotonin with practical implications, particularly in resource-limited settings.

Polydiacetylene Liposome-Based Dual-Output Optical Sensor for ppb Level Detection of Dopamine in Solution and Solid Phases

Dopamine (DA), a neurotransmitter, plays a crucial role in regulating motor functions and emotions and can serve as a marker for several diseases. In this study, we report a highly sensitive polydiacetylenes (PDA)-based dual-output sensor for dopamine detection in both solution and solid phases that was developed by modifying PDA liposomes with boronic acid groups at the termini. This sensor exploits the high affinity between the catechol residue of dopamine and the -B(OH)2 group of the PDA-based probe (PDA-PhBA) to form boronate ester bonds, causing a stress-induced blue-to-red color change along with a steady increase in fluorescence response at λmax 622 nm. The PDA-PhBA-based sensor displays high sensitivity toward dopamine with low limit of detection of 6.2 ppb in colorimetric analysis and 0.6 ppb in fluorimetric measurements, demonstrating its dual optical output ability. The sensor works well for adrenaline, another catecholamine, with similar efficacy. Its practical applicability was validated by the successful recovery of trace level dopamine in blood serum and real water samples. Additionally, immobilizing PDA-PhBA liposomes in sodium alginate produced PDA beads for the solid-phase detection of dopamine with an limit of detection (LOD) of 59 nM (9.0 ppb) in colorimetric detection using a smartphone for capturing images and ImageJ software for analysis.

Biosensors for psychiatric biomarkers in mental health monitoring

Psychiatric disorders are associated with serve disturbances in cognition, emotional control, and/or behavior regulation, yet few routine clinical tools are available for the real-time evaluation and early-stage diagnosis of mental health. Abnormal levels of relevant biomarkers may imply biological, neurological, and developmental dysfunctions of psychiatric patients. Exploring biosensors that can provide rapid, in-situ, and real-time monitoring of psychiatric biomarkers is therefore vital for prevention, diagnosis, treatment, and prognosis of mental disorders. Recently, psychiatric biosensors with high sensitivity, selectivity, and reproducibility have been widely developed, which are mainly based on electrochemical and optical sensing technologies. This review presented psychiatric disorders with high morbidity, disability, and mortality, followed by describing pathophysiology in a biomarker-implying manner. The latest biosensors developed for the detection of representative psychiatric biomarkers (e.g., cortisol, dopamine, and serotonin) were comprehensively summarized and compared in their sensitivities, sensing technologies, applicable biological platforms, and integrative readouts. These well-developed biosensors are promising for facilitating the clinical utility and commercialization of point-of-care diagnostics. It is anticipated that mental healthcare could be gradually improved in multiple perspectives, ranging from innovations in psychiatric biosensors in terms of biometric elements, transducing principles, and flexible readouts, to the construction of 'Big-Data' networks utilized for sharing intractable psychiatric indicators and cases.

The potential of a novel enzyme-based surface plasmon resonance biosensor for direct detection of dopamine

Dopamine is one of the significant neurotransmitters and its monitoring in biological fluids is a critical issue in healthcare and modern biomedical technology. Here, we have developed a dopamine biosensor based on surface plasmon resonance (SPR). For this purpose, the carboxymethyl dextran SPR chip was used as a surface to immobilize laccase as a bioaffinity recognition element. Data analysis exhibited that the acidic pH value is the optimal condition for dopamine interaction. Calculated kinetic affinity (KD) (48,545 nM), obtained from a molecular docking study, showed strong association of dopamine with the active site of laccase. The biosensor exhibited a linearity from 0.01 to 189 μg/ml and a lower detection limit of 0.1 ng/ml (signal-to-noise ratio (S/N) = 3) that is significantly higher than the most direct dopamine detecting sensors reported so far. Experiments for specificity in the presence of compounds that can co-exist with dopamine detection such as ascorbic acid, urea and L-dopa showed no significant interference. The current dopamine biosensor with high sensitivity and specificity, represent a novel detection tool that offers a label-free, simple procedure and cost effective monitoring system.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Mussel-inspired chitin nanofiber adherable hydrogel sensor with interpenetrating network and great fatigue resistance for motion and acoustics monitoring

A method for grafting dopamine onto TEMPO-oxidized chitin nanofibers (TOChN) was developed, achieving a surface grafting rate of 54 % through the EDC/NHS reaction. This process resulted in the formation of dopamine-grafted TOChN (TOChN-DA). Subsequently, an adherent, highly sensitive, fatigue-resistant conductive PAM/TOChN-PDA/Fe3+ (PTPF) hydrogel was successfully synthesized based on the composition of polyacrylamide (PAM) and TOChN-DA, which exhibited good cell compatibility, a tensile strength of 89.42 kPa, and a high adhesion strength of 62.56 kPa with 1.2 wt% TOChN-DA. Notably, the PTPF hydrogel showed stable adherence to various surfaces, such as rubber, copper, and human skin. Specifically, the addition of FeCl3 contributed to a multifunctional design in the PTPF interpenetrating network (IPN) hydrogel, endowing it with conductivity, cohesion, and antioxidant properties, which facilitated sensitive motion and acoustics monitoring. Moreover, the PTPF hydrogel demonstrated exceptional fatigue resistance and sensing stability, maintaining performance at 50 % strain over 1000 cycles. These attributes render the PTPF hydrogel a promising candidate for advanced biosensors in medical and athletic applications.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Lab on skin: real-time metabolite monitoring with polyphenol film based subdermal wearable patches

The advent of digital technologies has spurred the development of wearable sensing devices marking a significant shift in obtaining real-time physiological information. The principal objective is to transition from blood-centric monitoring to minimally invasive modalities, which will enable movement from specialised settings to more accessible environments such as the practices of general practitioners or even home settings. While subcutaneously implanted continuous monitoring devices have demonstrated this transition, detection of analytes from sample matrices like skin interstitial fluid (ISF), is a frontier that offers attractive minimally invasive routes for detection of biomarkers. This manuscript presents a comprehensive overview of our work in subdermal wearable biosensing patches for the simultaneous monitoring of glucose and lactate from ISF in ambulatory conditions. The performance of the subdermal wearable glucose monitoring patch was evaluated over a duration of three days, which is the longest reported duration reported till date. The subdermal wearable lactate sensing patch was worn for the duration of the exercise. Our findings highlight a critical observation that biofouling effects become apparent after a 24 h period. The data presented in this manuscript extends on the knowledge in the areas of continuous metabolite monitoring by introducing multifunctional polyphenol polymer films that can be used for both glucose and lactate monitoring with appropriate modifications. This study underscores the potential of subdermal wearable patches as versatile tools for real-time metabolite monitoring, positioning them as valuable assets in the evolution of personalised healthcare in diverse settings.

Nonenzymatic Potentiometric Detection of Ascorbic Acid with Porphyrin/ZnO-Functionalized Laser-Induced Graphene as an Electrode of EGFET Sensors

Laser-induced graphene (LIG) has emerged as a highly versatile material with significant potential in the development of electrochemical sensors. In this paper, we investigate the use of LIG and LIG functionalized with ZnO and porphyrins-ZnO as the gate electrodes of the extended gate field effect transistors (EGFETs). The resultant sensors exhibit remarkable sensitivity and selectivity, particularly toward ascorbic acid. The intrinsic sensitivity of LIG undergoes a notable enhancement through the incorporation of hybrid organic-inorganic materials. Among the variations tested, the LIG electrode coated with zinc tetraphenylporphyrin-capped ZnO nanoparticles demonstrates superior performance, reaching a limit of detection of approximately 3 nM. Furthermore, the signal ratio for 5 μM ascorbic acid relative to the same concentration of dopamine exceeds 250. The practical applicability of these sensors is demonstrated through the detection of ascorbic acid in real-world samples, specifically in a commercially available food supplement containing l-arginine. Notably, formulations with added vitamin C exhibit signals at least 25 times larger than those without, underscoring the sensors' capability to discern and quantify the presence of ascorbic acid in complex matrices. This research not only highlights the enhanced performance of LIG-based sensors through functionalization but also underscores their potential for practical applications in the analysis of vitamin-rich supplements.

Highly sensitive amperometric sensors based on laccase-mimetic nanozymes for the detection of dopamine

The current research presents novel sensors based on laccase-like mimetics for the detection of dopamine (DA). The synthesized laccase-like nanozymes (nAuCu, nPtCu, nCuMnCo, and nCoCuCe) were prepared by a simple hydrothermal method and exhibited an attractive catalytic activity toward DA. The developed amperometric sensors based on laccase nanozymes (nAuCu and nPtCu) are more stable, selective, and revealed a higher sensitivity (6.5-fold than the biosensor based on the natural fungal laccase from Trametes zonata). The amperometric sensors were obtained by modification of the glassy carbon electrodes (GCEs) with AuPt nanoparticles. Functionalization of the electrode surface by AuPt NPs resulted in increased catalytic activity of the laccase-like layer and higher sensitivity. Among studied configurations, the sensor containing nAuCu and nAuPt possesses a wide linear range for dopamine detection (10-170 μM), the lowest limit of detection (20 nM), and the highest sensitivity (10 650 ± 8.3 A M-1 m-2) at a low applied potential (+0.2 V versus Ag/AgCl). The proposed simple and cost-effective sensor electrode was used for the determination of DA in pharmaceuticals.

Co-based Nanozymatic Profiling: Advances Spanning Chemistry, Biomedical, and Environmental Sciences

Nanozymes, next-generation enzyme-mimicking nanomaterials, have entered an era of rational design; among them, Co-based nanozymes have emerged as captivating players over times. Co-based nanozymes have been developed and have garnered significant attention over the past five years. Their extraordinary properties, including regulatable enzymatic activity, stability, and multifunctionality stemming from magnetic properties, photothermal conversion effects, cavitation effects, and relaxation efficiency, have made Co-based nanozymes a rising star. This review presents the first comprehensive profiling of the Co-based nanozymes in the chemistry, biology, and environmental sciences. The review begins by scrutinizing the various synthetic methods employed for Co-based nanozyme fabrication, such as template and sol-gel methods, highlighting their distinctive merits from a chemical standpoint. Furthermore, a detailed exploration of their wide-ranging applications in biosensing and biomedical therapeutics, as well as their contributions to environmental monitoring and remediation is provided. Notably, drawing inspiration from state-of-the-art techniques such as omics, a comprehensive analysis of Co-based nanozymes is undertaken, employing analogous statistical methodologies to provide valuable guidance. To conclude, a comprehensive outlook on the challenges and prospects for Co-based nanozymes is presented, spanning from microscopic physicochemical mechanisms to macroscopic clinical translational applications.

A disposable sensor based on one-pot synthesized tungsten oxide nanostructure-modified screen printed electrodes for selective detection of dopamine and uric acid

Here, screen-printed carbon electrodes (SPCEs) were modified with ultrafine and mainly mono-disperse sea urchin-like tungsten oxide (SUWO3) nanostructures synthesized by a simple one-pot hydrothermal method for non-enzymatic detection of dopamine (DA) and uric acid (UA) in synthetic urine. Sea urchin-like nanostructures were clearly observed in scanning electron microscope images and WO3 composition was confirmed with XRD, Raman, FTIR and UV-Vis spectrophotometer. Modification of SPCEs with SUWO3 nanostructures via the drop-casting method clearly reduced the Rct value of the electrodes, lowered the ∆Ep and enhanced the DA oxidation current due to high electrocatalytic activity. As a result, SUWO3/SPCEs enabled highly sensitive non-enzymatic detection of DA (LOD: 51.4 nM and sensitivity: 127 µA mM-1 cm-2) and UA (LOD: 253 nM and sensitivity: 55.9 µA mM-1 cm-2) at low concentration. Lastly, SUWO3/SPCEs were tested with synthetic urine, in which acceptable recoveries for both molecules (94.02-105.8%) were obtained. Given the high selectivity, the sensor has the potential to be used for highly sensitive simultaneous detection of DA and UA in real biological samples.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Modular Aptamer Switches for the Continuous Optical Detection of Small-Molecule Analytes in Complex Media

Aptamers are a promising class of affinity reagents because signal transduction mechanisms can be built into the reagent, so that they can directly produce a physically measurable output signal upon target binding. However, endowing the signal transduction functionality into an aptamer remains a trial-and-error process that can compromise its affinity or specificity and typically requires knowledge of the ligand binding domain or its structure. In this work, a design architecture that can convert an existing aptamer into a "reversible aptamer switch" whose kinetic and thermodynamic properties can be tuned without a priori knowledge of the ligand binding domain or its structure is described. Finally, by combining these aptamer switches with evanescent-field-based optical detection hardware that minimizes sample autofluorescence, this study demonstrates the first optical biosensor system that can continuously measure multiple biomarkers (dopamine and cortisol) in complex samples (artificial cerebrospinal fluid and undiluted plasma) with second and subsecond-scale time responses at physiologically relevant concentration ranges.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Chitosan stabilized copper iodide nanoparticles enabled nano-bio-engineered platform for efficient electrochemical biosensing of dopamine

Neurodegenerative disorders are one of the significant challenges to the aging society, as per the United Nations, where 1 in 6 people globally over 65 years of age are expected to suffer by 2050. The exact pathophysiological root of these disorders is although not known adequately, but reduced dopamine (most significant neurotransmitters) levels have been reported in people affected by Parkinson's disease. Sensitive detection and effective monitoring of dopamine can help to diagnose these neurodegenerative disorders at a very early stage, which will help to properly treat these disorders and slow down their progression. Therefore, it is crucial to detect physiological and clinically acceptable amounts of dopamine with high sensitivity and selectivity in basic pathophysiology research, medication, and illness diagnosis. Here in this present investigation, nano-bio-engineered stable chitosan stabilized copper iodide nanoparticles (CS@CuI NPs) were synthesized to engineer the active biosensing platform for developing dopamine biosensors. Initially, the as-synthesized nano-bio-engineered CS@CuI NPs were subjected to its drop-casting onto an Indium tin oxide (ITO) conducting glass substrate. This substrate platform was then utilized to immobilize tyrosinase (Tyr) enzyme by drop-casting to fabricate Tyr/CS@CuI NPs/ITO bioelectrode for the ultrasensitive determination of dopamine. Several techniques were used to characterize the structural, optical, and morphological properties of the synthesized CS@CuI NPs and Tyr/CS@CuI NPs/ITO bioelectrode. Further, the as-prepared bioelectrode was evaluated for its suitability and electrocatalytic behaviour towards dopamine by cyclic voltammetry. A perusal of the electroanalytic results of the fabricated biosensor revealed that under the optimized experimental conditions, Tyr/CS@CuI NPs/ITO bioelectrode exhibits a very high electrochemical sensitivity of 11.64 μA μM-1 cm-2 towards dopamine with the low limit of detection and quantification of 0.02 and 0.386 μM, respectively. In addition, the fabricated bioelectrode was stable up to 46 days with only 4.82 % current loss, reusable till 20 scans, and it also performed effectively while real sample analysis. Therefore, the nano-bio-engineered biosensor platform being reported can determine deficient dopamine levels in a very selective and sensitive manner, which can help adequately manage neurodegenerative disorders, further slowing down the disease progression.

Synthesis and structural elucidation of a unique turn-off fluorescent sensor based on oxo-bridged tin (IV) cluster for selective detection of dopamine in biological fluids

An oxo-bridged Sn (IV) Cluster, (TOC) was synthesized and fully characterized by FT-IR, UV-vis, 1H NMR, 119Sn NMR, Mass spectrometry and single crystal X-ray diffraction studies. The single-crystal X-ray analysis revealed that the crystal crystallizes in the monoclinic crystal system possessing the P 21/c space group and exhibited a distorted trigonal bipyramidal geometry. The TOC exhibited a unique turn-off fluorescence response for the selective detection of dopamine (DA) over other analytes. The stoichiometry between the TOC and DA was calculated using Job's plot. The value of the detection limit was found to be 1.33 µM. The Hirshfeld surface analysis was carried out on the crystal structure to investigate the H-H, Cl-H, Cl-Cl, Sn-Cl and Cl-C interaction studies in the molecule. Density Functional Theory (DFT) studies further supported the sensing mechanism, which closely agreed with the experimental results. Furthermore, the TOC chemosensor was used to detect DA in human blood plasma, and molecular docking studies validated the interaction between the chemosensor and protein. Confocal fluorescence imaging studies were carried out and validated TOC sensing ability for DA in human blood plasma.

Wearable Sensor Patch with Hydrogel Microneedles for In Situ Analysis of Interstitial Fluid

Continuous real-time monitoring of biomarkers in interstitial fluid is essential for tracking metabolic changes and facilitating the early detection and management of chronic diseases such as diabetes. However, developing minimally invasive sensors for the in situ analysis of interstitial fluid and addressing signal delays remain a challenge. Here, we introduce a wearable sensor patch incorporating hydrogel microneedles for rapid, minimally invasive collection of interstitial fluid from the skin while simultaneously measuring biomarker levels in situ. The sensor patch is stretchable to accommodate the swelling of the hydrogel microneedles upon extracting interstitial fluid and adapts to skin deformation during measurements, ensuring consistent sensing performance in detecting model biomarker concentrations, such as glucose and lactate, in a mouse model. The sensor patch exhibits in vitro sensitivities of 0.024 ± 0.002 μA mM-1 for glucose and 0.0030 ± 0.0004 μA mM-1 for lactate, with corresponding linear ranges of 0.1-3 and 0.1-12 mM, respectively. For in vivo glucose sensing, the sensor patch demonstrates a sensitivity of 0.020 ± 0.001 μA mM-1 and a detection range of 1-8 mM. By integrating a predictive model, the sensor patch can analyze and compensate for signal delays, improving calibration reliability and providing guidance for potential optimization in sensing performance. The sensor patch is expected to serve as a minimally invasive platform for the in situ analysis of multiple biomarkers in interstitial fluid, offering a promising solution for continuous health monitoring and disease management.

Fluorescent-Based Neurotransmitter Sensors: Present and Future Perspectives

Neurotransmitters (NTs) are endogenous low-molecular-weight chemical compounds that transmit synaptic signals in the central nervous system. These NTs play a crucial role in facilitating signal communication, motor control, and processes related to memory and learning. Abnormalities in the levels of NTs lead to chronic mental health disorders and heart diseases. Therefore, detecting imbalances in the levels of NTs is important for diagnosing early stages of diseases associated with NTs. Sensing technologies detect NTs rapidly, specifically, and selectively, overcoming the limitations of conventional diagnostic methods. In this review, we focus on the fluorescence-based biosensors that use nanomaterials such as metal clusters, carbon dots, and quantum dots. Additionally, we review biomaterial-based, including aptamer- and enzyme-based, and genetically encoded biosensors. Furthermore, we elaborate on the fluorescence mechanisms, including fluorescence resonance energy transfer, photon-induced electron transfer, intramolecular charge transfer, and excited-state intramolecular proton transfer, in the context of their applications for the detection of NTs. We also discuss the significance of NTs in human physiological functions, address the current challenges in designing fluorescence-based biosensors for the detection of NTs, and explore their future development.

Synthesis of 2D VO2 Nanosheets for the Dual Optical Sensor Method by Colorimetric and Fluorometric Sensing of Catecholamines

For the first time, we report a dual optical sensor method (DOSM) using novel 2D VO2 nanosheets to act as fluorometric and colorimetric sensors to perform quantitative analysis of epinephrine (EP) and dopamine (DA). The wide color spectrum of the 2D vanadium oxidation series and specifically metastable blue 2D VO2 nanosheets were used to develop a DOSM biosensor. DA and EP are the major catecholamines in the human body that play vital roles as neurotransmitters and stress-responsive hormones of the endocrine system, respectively. Accurate and selective detection of these biomolecules can assist in the diagnosis of many neuroendocrine system-related diseases. The newly synthesized 2D VO2 nanosheet sensor showed bluish-green fluorescence as the first-ever fluorescence from 2D VO2 nanosheets. This sensor showed dual-function sensing toward EP by a dominant color change and fluorescence quenching. It is capable of individually detecting and quantifying both EP and DA with high selectivity and sensitivity by using both colorimetry and fluorometry simultaneously, with the detection limits of 1.07 and 5.54 μM for colorimetric analysis, respectively, and 48.07 and 3.98 μM for fluorescence analysis, respectively. The DOSM sensor was directly applied to real urine samples and gained satisfactory recovery above 90% by means of spiked concentrations. This study has opened a new platform using the DOSM and the vanadium oxidation spectrum in a much more effective way for biosensing. The fluorescence capabilities of this metal oxide can be further applied to many sensor applications based on both fluorescence and colorimetric detection.

Aptamer Conformational Dynamics Modulate Neurotransmitter Sensing in Nanopores

Aptamers that undergo conformational changes upon small-molecule recognition have been shown to gate the ionic flux through nanopores by rearranging the charge density within the aptamer-occluded orifice. However, mechanistic insight into such systems where biomolecular interactions are confined in nanoscale spaces is limited. To understand the fundamental mechanisms that facilitate the detection of small-molecule analytes inside structure-switching aptamer-modified nanopores, we correlated experimental observations to theoretical models. We developed a dopamine aptamer-functionalized nanopore sensor with femtomolar detection limits and compared the sensing behavior with that of a serotonin sensor fabricated with the same methodology. When these two neurotransmitters with comparable mass and equal charge were detected, the sensors showed an opposite electronic behavior. This distinctive phenomenon was extensively studied using complementary experimental techniques such as quartz crystal microbalance with dissipation monitoring, in combination with theoretical assessment by the finite element method and molecular dynamic simulations. Taken together, our studies demonstrate that the sensing behavior of aptamer-modified nanopores in detecting specific small-molecule analytes correlates with the structure-switching mechanisms of individual aptamers. We believe that such investigations not only improve our understanding of the complex interactions occurring in confined nanoscale environments but will also drive further innovations in biomimetic nanopore technologies.

Biosensor nanoarchitectonics of Cu-Fe-nanoparticles/Zeolite-A/Graphene nanocomposite for enhanced electrooxidation and dopamine detection

Cu-Fe NPs/ZEA/Gr electrochemical biosensor is developed by sol-gel spin coating technique, where copper-iron nanoparticles (Cu-Fe NPs) is synthesized using a chemical reduction method and modified with Zeolite & Graphene to develop a hybrid nanocomposite - Cu-Fe NPs/ZEA/Gr. The synthesized nanocomposite is then mixed with poly (vinyl alcohol) as a binding agent and coated on to the glass substrate to produce thin film electrode. Then the electrode was analyzed for structural and morphological studies using XRD, SEM, TEM, UV-VIS, absorption, and emission spectra. The presence of Cu-Fe NPs, ZEA, and Gr in the nanocomposite is confirmed by the XRD diffraction peaks, while SEM investigation revealed that the hybrid composite has a particle size of around 7.25 nm with a body-centred cubic structure. The TEM images show that bimetallic nanoparticles were incorporated into the ZEA shell, which was surrounded by a layer of transparent graphene. Furthermore, the nanocomposite exhibited a distinct absorption peak at 395 nm, as evidenced by UV-VIS, absorption, and emission spectra. The electrochemical tests demonstrated that the Cu-Fe NPs/ZEA/Gr nanocomposite electrode showed an excellent electrocatalytic and selective properties towards the electrooxidation of dopamine to dopamine-o-quinone. The detection limit of the Cu-Fe NPs/ZEA/Gr nanocomposite thin film was found to be 0.058 μM, with a sensitivity of 1.97 μAμM-1cm-2. The enhanced catalytic performance of the Cu-Fe NPs/ZEA/Gr electrode is attributed to the unique nanostructured materials coating on the glass substrate. The findings suggest that nano-hybrid materials can be a viable option for developing electrochemical biosensors to monitor dopamine levels in biological fluids. This indicates that the concept of nanoarchitectonics utilized to produce dopamine sensors may lead to new diagnostic and therapeutic approaches for neurological disorders associated with dopamine dysregulation.

Tailoring diamondised nanocarbon-loaded poly(lactic acid) composites for highly electroactive surfaces: extrusion and characterisation of filaments for improved 3D-printed surfaces

A new 3D-printable composite has been developed dedicated to electroanalytical applications. Two types of diamondised nanocarbons - detonation nanodiamonds (DNDs) and boron-doped carbon nanowalls (BCNWs) - were added as fillers in poly(lactic acid) (PLA)-based composites to extrude 3D filaments. Carbon black served as a primary filler to reach high composite conductivity at low diamondised nanocarbon concentrations (0.01 to 0.2 S/cm, depending on the type and amount of filler). The aim was to thoroughly describe and understand the interactions between the composite components and how they affect the rheological, mechanical and thermal properties, and electrochemical characteristics of filaments and material extrusion printouts. The electrocatalytic properties of composite-based electrodes, fabricated with a simple 3D pen, were evaluated using multiple electrochemical techniques (cyclic and differential pulse voltammetry and electrochemical impedance spectroscopy). The results showed that the addition of 5 wt% of any of the diamond-rich nanocarbons fillers significantly enhanced the redox process kinetics, leading to lower redox activation overpotentials compared with carbon black-loaded PLA. The detection of dopamine was successfully achieved through fabricated composite electrodes, exhibiting lower limits of detection (0.12 μM for DND and 0.18 μM for BCNW) compared with the reference CB-PLA electrodes (0.48 μM). The thermogravimetric results demonstrated that both DND and BCNW powders can accelerate thermal degradation. The presence of diamondised nanocarbons, regardless of their type, resulted in a decrease in the decomposition temperature of the composite. The study provides insight into the interactions between composite components and their impact on the electrochemical properties of 3D-printed surfaces, suggesting electroanalytic potential.

From Enzymatic Dopamine Biosensors to OECT Biosensors of Dopamine

Neurotransmitters are an important category of substances used inside the nervous system, whose detection with biosensors has been seriously addressed in the last decades. Dopamine, a neurotransmitter from the catecholamine family, was recently discovered to have implications for cardiac arrest or muscle contractions. In addition to having many other neuro-psychiatric implications, dopamine can be detected in blood, urine, and sweat. This review highlights the importance of biosensors as influential tools for dopamine recognition. The first part of this article is related to an introduction to biosensors for neurotransmitters, with a focus on dopamine. The regular methods in their detection are expensive and require high expertise personnel. A major direction of evolution of these biosensors has expanded with the integration of active biological materials suitable for molecular recognition near electronic devices. Secondly, for dopamine in particular, the miniaturized biosensors offer excellent sensitivity and specificity and offer cheaper detection than conventional spectrometry, while their linear detection ranges from the last years fall exactly on the clinical intervals. Thirdly, the applications of novel nanomaterials and biomaterials to these biosensors are discussed. Older generations, metabolism-based or enzymatic biosensors, could not detect concentrations below the micro-molar range. But new generations of biosensors combine aptamer receptors and organic electrochemical transistors, OECTs, as transducers. They have pushed the detection limit to the pico-molar and even femto-molar ranges, which fully correspond to the usual ranges of clinical detection of human dopamine in body humors that cover 0.1 ÷ 10 nM. In addition, if ten years ago the use of natural dopamine receptors on cell membranes seemed impossible for biosensors, the actual technology allows co-integrate transistors and vesicles with natural receptors of dopamine, like G protein-coupled receptors. The technology is still complicated, but the uni-molecular detection selectivity is promising.

Plasmonic Coupling on an Optical Microfiber Surface: Enabling Single-Molecule and Noninvasive Dopamine Detection

Optical fibers can be effective biosensors when employed in early-stage diagnostic point-of-care devices as they can avoid interference from molecules with similar redox potentials. Nevertheless, their sensitivity needs to be improved for real-world applications, especially for small-molecule detection. This work demonstrates an optical microfiber biosensor for dopamine (DA) detection based on the DA-binding-induced aptamer conformational transitions that occur at plasmonic coupling sites on a double-amplified nanointerface. The sensor exhibits ultrahigh sensitivity when detecting DA molecules at the single-molecule level; additionally, this work provides an approach for overcoming optical device sensitivity limits, further extending optical fiber single-molecule detection to a small molecule range (e.g., DA and metal ions). The selective energy enhancement and signal amplification at the binding sites effectively avoid nonspecific amplification of the whole fiber surface which may lead to false-positive results. The sensor can detect single-molecule DA signals in body-fluids. It can detect the released extracellular DA levels and monitor the DA oxidation process. An appropriate aptamer replacement allows the sensor to be used for the detection of other target small molecules and ions at the single-molecule level. This technology offers alternative opportunities for developing noninvasive early-stage diagnostic point-of-care devices and flexible single-molecule detection techniques in theoretical research.

Efficient Metabolic Fingerprinting of Follicular Fluid Encodes Ovarian Reserve and Fertility

Ovarian reserve (OR) and fertility are critical in women's healthcare. Clinical methods for encoding OR and fertility rely on the combination of tests, which cannot serve as a multi-functional platform with limited information from specific biofluids. Herein, metabolic fingerprinting of follicular fluid (MFFF) from follicles is performed, using particle-assisted laser desorption/ionization mass spectrometry (PALDI-MS) to encode OR and fertility. PALDI-MS allows efficient MFFF, showing fast speed (≈30 s), high sensitivity (≈60 fmol), and desirable reproducibility (coefficients of variation <15%). Further, machine learning of MFFF is applied to diagnose diminished OR (area under the curve of 0.929) and identify high-quality oocytes/embryos (p < 0.05) by a single PALDI-MS test. Meanwhile, metabolic biomarkers from MFFF are identified, which also determine oocyte/embryo quality (p < 0.05) from the sampling follicles toward fertility prediction in clinics. This approach offers a powerful platform in women's healthcare, not limited to OR and fertility.

Label-Free Detection of Epinephrine Using Flower-like Biomimetic CuS Antioxidant Nanozymes

A biosensor comprising crystalline CuS nanoparticles (NPs) was synthesized via a one-step simple coprecipitation route without involvement of a surfactant. The powder X-ray diffraction method has been used to evaluate the crystalline nature and different phases consist of the formation of CuS NPs. Mainly hexagonal unit cells consist of the formation of CuS NP unit cells. Most of the surfaces are covered with rhombohedral microparticles with a smooth exterior and surface clustering, examined by SEM images, and the shape of NPs was spherical, having an average size of 23 nm, as confirmed by TEM analysis. This study has focused on the peroxidase-mimicking activity, superoxide dismutase (SOD)-mimicking activity, and chemosensor-based colorimetric determination and detection of epinephrine (EP) neurotransmitters with excellent selectivity. The CuS NPs catalyzed the oxidation of the oxidase substrate 3, 3-5, 5 tetramethyl benzidine (TMB) with the help of supplementary H2O2 that followed Michaelis-Menten kinetics with excellent Km and Vmax values calculated by the Lineweaver-Burk plot. Taking advantage of the drop in absorbance upon introduction of EP for the CuS NPs-TMB/H2O2 system, a colorimetric route has been developed for selective and real-time detection of EP. The sensitivity of the new colorimetric probe was vibrant, having a linear range of 0-16 μM, and achieved a low limit of detection of 457 nM. Moreover, the present nanosystem exhibited appreciable SOD-mimicking activity which could effectively remove O2•- from commercial cigarette smoke, along with it acting as a potential radical scavenger as well. The new nanosystem effectively scavenged •OH, O2.-, and metal chelation which were investigated calorimetrically.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

Aptamer-Based Point-of-Care Devices: Emerging Technologies and Integration of Computational Methods

Recent innovations in point-of-care (POC) diagnostic technologies have paved a critical road for the improved application of biomedicine through the deployment of accurate and affordable programs into resource-scarce settings. The utilization of antibodies as a bio-recognition element in POC devices is currently limited due to obstacles associated with cost and production, impeding its widespread adoption. One promising alternative, on the other hand, is aptamer integration, i.e., short sequences of single-stranded DNA and RNA structures. The advantageous properties of these molecules are as follows: small molecular size, amenability to chemical modification, low- or nonimmunogenic characteristics, and their reproducibility within a short generation time. The utilization of these aforementioned features is critical in developing sensitive and portable POC systems. Furthermore, the deficiencies related to past experimental efforts to improve biosensor schematics, including the design of biorecognition elements, can be tackled with the integration of computational tools. These complementary tools enable the prediction of the reliability and functionality of the molecular structure of aptamers. In this review, we have overviewed the usage of aptamers in the development of novel and portable POC devices, in addition to highlighting the insights that simulations and other computational methods can provide into the use of aptamer modeling for POC integration.

Single-Atom Indium Boosts Electrochemical Dopamine Sensing

A rational design of high-efficiency electrocatalysts and thus achieving sensitive electrochemical sensing remains a great challenge. In this work, single-atom indium anchored on nitrogen-doped carbon (In₁-N-C) with an In-N₄ configuration is prepared successfully through a high-temperature annealing strategy; the product can serve as an advanced electrocatalyst for sensitive electrochemical sensing of dopamine (DA). Compared with In nanoparticle catalysts, In₁-N-C exhibits high catalytic performance for DA oxidation. The theoretical calculation reveals that In₁-N-C has high adsorption energy for hydroxy groups and a low energy barrier in the process of DA oxidation compared to In nanoparticles, indicating that In₁-N-C with atomically dispersed In-N₄ sites possesses enhanced intrinsic activity. An electrochemical sensor for DA detection is established as a concept application with high sensitivity and selectivity. Furthermore, we also verify the feasibility of In₁-N-C catalysts for the simultaneous detection of uric acid, ascorbic acid, and DA. This work extends the application prospect of p-block metal single-atom catalysts in electrochemical sensing.

Selective Aptamer Modification of Au Surfaces in a Microelectrode Sensor Array for Simultaneous Detection of Multiple Analytes

Aptamers have been employed as the biorecognition element in electrochemical aptamer-based (E-AB) biosensors, for the detection of a diverse range of analyte molecules, on electrodes with sizescales ranging from a few microns to several millimeters. Simultaneous detection of multiple different analytes requires the selective modification of multiple electrode surfaces with different aptamers. This process is typically achieved by incubating separate macroscale electrodes in a solution with the desired aptamer, which is unsuitable for microelectrode arrays in which the electrodes are closely spaced. In this work, we selectively modified electrode surfaces with thiolated aptamers of different single-stranded DNA sequences, by successive removal and addition of thiol monolayers. This was achieved by electrodesorption of thiol monolayers using controlled potential, to expose unmodified gold electrodes to be modified with a different thiolated aptamer, thus enabling multiple different aptamers to be used on the surfaces of closely spaced microelectrodes. All aptamers were methylene blue terminated, allowing redox currents to be measured and used to monitor aptamer probe packing density on the electrode surface and the selectivity of the sensors. Here, we demonstrate the microscale E-AB sensor multianalyte detection method using aptamers for target analytes, adenosine triphosphate, dopamine, and serotonin, which can ultimately be applied to perform localized simultaneous detection using electrode arrays.

Au-Pt-Ni nanochains as dopamine catalysts: role of elements and their spatial distribution

Multi-element materials can improve biosensing ability as each element can catalyze different steps in a reaction pathway. By combining Pt and Ni on self-assembled 1D gold nanochains and controlling their spatial distribution, a detailed understanding of each element's role in dopamine oxidation is developed. In addition, the developed synthesis process provides a simple way to fabricate multi-element composites for electrocatalytic applications based on electrical double-layer formation on the surface of charged nanoparticles. The performance parameters of the catalyst, such as its sensitivity, limit of detection, and range of operation for dopamine sensing, are optimized by changing the relative ratios of Pt : Ni and the morphology of the Pt and Ni domains, using the developed understanding. The morphology of the domains also affects the oxidation state of Ni, which is crucial to the performance of the electrocatalyst.

The Role of Silver Nanoparticles in Electrochemical Sensors for Aquatic Environmental Analysis

With rapidly increasing environmental pollution, there is an urgent need for the development of fast, low-cost, and effective sensing devices for the detection of various organic and inorganic substances. Silver nanoparticles (AgNPs) are well known for their superior optoelectronic and physicochemical properties, and have, therefore, attracted a great deal of interest in the sensor arena. The introduction of AgNPs onto the surface of two-dimensional (2D) structures, incorporation into conductive polymers, or within three-dimensional (3D) nanohybrid architectures is a common strategy to fabricate novel platforms with improved chemical and physical properties for analyte sensing. In the first section of this review, the main wet chemical reduction approaches for the successful synthesis of functional AgNPs for electrochemical sensing applications are discussed. Then, a brief section on the sensing principles of voltammetric and amperometric sensors is given. The current utilization of silver nanoparticles and silver-based composite nanomaterials for the fabrication of voltammetric and amperometric sensors as novel platforms for the detection of environmental pollutants in water matrices is summarized. Finally, the current challenges and future directions for the nanosilver-based electrochemical sensing of environmental pollutants are outlined.

On-Demand, Reversible, Ultrasensitive Polymer Membrane Based on Molecular Imprinting Polymer

The development of in vivo, longitudinal, real-time monitoring devices is an essential step toward continuous, precision health monitoring. Molecularly imprinted polymers (MIPs) are popular sensor capture agents that are more robust than antibodies and have been used for sensors, drug delivery, affinity separations, assays, and solid-phase extraction. However, MIP sensors are typically limited to one-time use due to their high binding affinity (>10⁷ M^-1) and slow-release kinetics (<10^-4 μM/sec). To overcome this challenge, current research has focused on stimuli-responsive MIPs (SR-MIPs), which undergo a conformational change induced by external stimuli to reverse molecular binding, requiring additional chemicals or outside stimuli. Here, we demonstrate fully reversible MIP sensors based on electrostatic repulsion. Once the target analyte is bound within a thin film MIP on an electrode, a small electrical potential successfully releases the bound molecules, enabling repeated, accurate measurements. We demonstrate an electrostatically refreshed dopamine sensor with a 760 pM limit of detection, linear response profile, and accuracy even after 30 sensing-release cycles. These sensors could repeatedly detect <1 nM dopamine released from PC-12 cells in vitro, demonstrating they can longitudinally measure low concentrations in complex biological environments without clogging. Our work provides a simple and effective strategy for enhancing the use of MIPs-based biosensors for all charged molecules in continuous, real-time health monitoring and other sensing applications.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Label-Free Electrochemical Biosensor Platforms for Cancer Diagnosis: Recent Achievements and Challenges

With its fatal effects, cancer is still one of the most important diseases of today's world. The underlying fact behind this scenario is most probably due to its late diagnosis. That is why the necessity for the detection of different cancer types is obvious. Cancer studies including cancer diagnosis and therapy have been one of the most laborious tasks. Since its early detection significantly affects the following therapy steps, cancer diagnosis is very important. Despite researchers' best efforts, the accurate and rapid diagnosis of cancer is still challenging and difficult to investigate. It is known that electrochemical techniques have been successfully adapted into the cancer diagnosis field. Electrochemical sensor platforms that are brought together with the excellent selectivity of biosensing elements, such as nucleic acids, aptamers or antibodies, have put forth very successful outputs. One of the remarkable achievements of these biomolecule-attached sensors is their lack of need for additional labeling steps, which bring extra burdens such as interference effects or demanding modification protocols. In this review, we aim to outline label-free cancer diagnosis platforms that use electrochemical methods to acquire signals. The classification of the sensing platforms is generally presented according to their recognition element, and the most recent achievements by using these attractive sensing substrates are described in detail. In addition, the current challenges are discussed.

Framing of Poly(arylene-ethynylene) around Carbon Nanotubes and Iodine Doping for the Electrochemical Detection of Dopamine

Dopamine (DA), an organic biomolecule that acts as both a hormone and a neurotransmitter, is essential in regulating emotions and metabolism in living organisms. The accurate determination of DA is important because it indicates early signs of serious neurological disorders. Covalent organic frameworks (COFs) and metal-organic frameworks (MOFs) have received considerable attention in recent years as promising porous materials with an unrivaled degree of tunability for electrochemical biosensing applications. This study adopted a solvothermal strategy for the synthesis of a conjugated microporous poly(arylene ethynylene)-4 (CMP-4) network using the Sonagashira-Hagihara cross-coupling reaction. To increase the crystallinity and electrical conductivity of the material, CMP-4 was enveloped around carbon nanotubes (CNTs), followed by iodine doping. When used as an electrochemical probe, the as-synthesized material (I₂-CMP-CNT-4) exhibited excellent selectivity and sensitivity to dopamine in the phosphate-buffered solution. The detection limits of the electrochemical sensor were 1 and 1.7 μM based on cyclic voltammetry (CV) and differential pulse voltammetry (DPV).

Self-Powered Biosensors for Monitoring Human Physiological Changes

Human physiological signals have an important role in the guidance of human health or exercise training and can usually be divided into physical signals (electrical signals, blood pressure, temperature, etc.) and chemical signals (saliva, blood, tears, sweat). With the development and upgrading of biosensors, many sensors for monitoring human signals have appeared. These sensors are characterized by softness and stretching and are self-powered. This article summarizes the progress in self-powered biosensors in the past five years. Most of these biosensors are used as nanogenerators and biofuel batteries to obtain energy. A nanogenerator is a kind of generator that collects energy at the nanoscale. Due to its characteristics, it is very suitable for bioenergy harvesting and sensing of the human body. With the development of biological sensing devices, the combination of nanogenerators and classical sensors so that they can more accurately monitor the physiological state of the human body and provide energy for biosensor devices has played a great role in long-range medical care and sports health. A biofuel cell has a small volume and good biocompatibility. It is a device in which electrochemical reactions convert chemical energy into electrical energy and is mostly used for monitoring chemical signals. This review analyzes different classifications of human signals and different forms of biosensors (implanted and wearable) and summarizes the sources of self-powered biosensor devices. Self-powered biosensor devices based on nanogenerators and biofuel cells are also summarized and presented. Finally, some representative applications of self-powered biosensors based on nanogenerators are introduced.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Advanced Nanomaterials-Based Electrochemical Biosensors for Catecholamines Detection: Challenges and Trends

Catecholamines, including dopamine, epinephrine, and norepinephrine, are considered one of the most crucial subgroups of neurotransmitters in the central nervous system (CNS), in which they act at the brain's highest levels of mental function and play key roles in neurological disorders. Accordingly, the analysis of such catecholamines in biological samples has shown a great interest in clinical and pharmaceutical importance toward the early diagnosis of neurological diseases such as Epilepsy, Parkinson, and Alzheimer diseases. As promising routes for the real-time monitoring of catecholamine neurotransmitters, optical and electrochemical biosensors have been widely adopted and perceived as a dramatically accelerating development in the last decade. Therefore, this review aims to provide a comprehensive overview on the recent advances and main challenges in catecholamines biosensors. Particular emphasis is given to electrochemical biosensors, reviewing their sensing mechanism and the unique characteristics brought by the emergence of nanotechnology. Based on specific biosensors' performance metrics, multiple perspectives on the therapeutic use of nanomaterial for catecholamines analysis and future development trends are also summarized.

Wireless, Battery-Free Implants for Electrochemical Catecholamine Sensing and Optogenetic Stimulation

Neurotransmitters and neuromodulators mediate communication between neurons and other cell types; knowledge of release dynamics is critical to understanding their physiological role in normal and pathological brain function. Investigation into transient neurotransmitter dynamics has largely been hindered due to electrical and material requirements for electrochemical stimulation and recording. Current systems require complex electronics for biasing and amplification and rely on materials that offer limited sensor selectivity and sensitivity. These restrictions result in bulky, tethered, or battery-powered systems impacting behavior and that require constant care of subjects. To overcome these challenges, we demonstrate a fully implantable, wireless, and battery-free platform that enables optogenetic stimulation and electrochemical recording of catecholamine dynamics in real time. The device is nearly 1/10th the size of previously reported examples and includes a probe that relies on a multilayer electrode architecture featuring a microscale light emitting diode (μ-LED) and a carbon nanotube (CNT)-based sensor with sensitivities among the highest recorded in the literature (1264.1 nA μM-1 cm-2). High sensitivity of the probe combined with a center tapped antenna design enables the realization of miniaturized, low power circuits suitable for subdermal implantation even in small animal models such as mice. A series of in vitro and in vivo experiments highlight the sensitivity and selectivity of the platform and demonstrate its capabilities in freely moving, untethered subjects. Specifically, a demonstration of changes in dopamine concentration after optogenetic stimulation of the nucleus accumbens and real-time readout of dopamine levels after opioid and naloxone exposure in freely behaving subjects highlight the experimental paradigms enabled by the platform.

Ultrasensitive dopamine detection with graphene aptasensor multitransistor arrays

Detecting physiological levels of neurotransmitters in biological samples can advance our understanding of brain disorders and lead to improved diagnostics and therapeutics. However, neurotransmitter sensors for real-world applications must reliably detect low concentrations of target analytes from small volume working samples. Herein, a platform for robust and ultrasensitive detection of dopamine, an essential neurotransmitter that underlies several brain disorders, based on graphene multitransistor arrays (gMTAs) functionalized with a selective DNA aptamer is presented. High-yield scalable methodologies optimized at the wafer level were employed to integrate multiple graphene transistors on small-size chips (4.5 × 4.5 mm). The multiple sensor array configuration permits independent and simultaneous replicate measurements of the same sample that produce robust average data, reducing sources of measurement variability. This procedure allowed sensitive and reproducible dopamine detection in ultra-low concentrations from small volume samples across physiological buffers and high ionic strength complex biological samples. The obtained limit-of-detection was 1 aM (10–18) with dynamic detection ranges spanning 10 orders of magnitude up to 100 µM (10–8), and a 22 mV/decade peak sensitivity in artificial cerebral spinal fluid. Dopamine detection in dopamine-depleted brain homogenates spiked with dopamine was also possible with a LOD of 1 aM, overcoming sensitivity losses typically observed in ion-sensitive sensors in complex biological samples. Furthermore, we show that our gMTAs platform can detect minimal changes in dopamine concentrations in small working volume samples (2 µL) of cerebral spinal fluid samples obtained from a mouse model of Parkinson’s Disease. The platform presented in this work can lead the way to graphene-based neurotransmitter sensors suitable for real-world academic and pre-clinical pharmaceutical research as well as clinical diagnosis.

Role of Protein Damage Inflicted by Dopamine Metabolites in Parkinson's Disease: Evidence, Tools, and Outlook

Dopamine is an important neurotransmitter that plays a critical role in motivational salience and motor coordination. However, dysregulated dopamine metabolism can result in the formation of reactive electrophilic metabolites which generate covalent adducts with proteins. Such protein damage can impair native protein function and lead to neurotoxicity, ultimately contributing to Parkinson's disease etiology. In this Review, the role of dopamine-induced protein damage in Parkinson's disease is discussed, highlighting the novel chemical tools utilized to drive this effort forward. Continued innovation of methodologies which enable detection, quantification, and functional response elucidation of dopamine-derived protein adducts is critical for advancing this field. Work in this area improves foundational knowledge of the molecular mechanisms that contribute to dopamine-mediated Parkinson's disease progression, potentially assisting with future development of therapeutic interventions.

Curcumin-PLGA based nanocapsule for the fluorescence spectroscopic detection of dopamine

The main purpose of this paper is to design curcumin loaded PLGA nanocapsules for the selective detection of dopamine using fluorescence spectroscopy. In the present work curcumin loaded PLGA nanocapsules were synthesized using a solid-in-oil-in water (s/o/w) emulsion technique. The prepared nanocapsules were coated with a poly(diallyldimethylammonium)chloride (PDDA) polymer to increase the entrapment of curcumin into the core of PLGA polymer. PLGA-Cur-PDDA nanocapsules were characterized using different microscopic and spectroscopic techniques. Unlike free curcumin, the formed CUR-PLGA-PDDA NCs were established as nanoprobes for the selective detection of dopamine molecules. The selectivity and specificity of nanocapsules toward dopamine was achieved by measuring the fluorescence emission spectra of the NCs in the presence of other interference molecules such as tryptophan, melamine, adenine, etc. It was noticed that increasing the concentration of the different molecules had no significant change in the fluorescence signal of the nanocapsules. These results confirm the strong quenching between dopamine and curcumin in the nanocapsules. Hence, this fluorescence emission technique was found to be selective, easy and fast with low cost for the determination of dopamine in a concentration range up to 5 mM with a detection limit equal to 22 nM.