ACS Sensors最新文献

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Microtubule (MT) dynamics is tightly regulated by microtubule-associated proteins (MAPs) and various post-translational modifications (PTMs) of tubulin. Here, we introduce OligoMT and OligoTIP as genetically encoded oligomeric MT binders designed for real-time visualization and manipulation of MT behaviors within living cells. OligoMT acts as a reliable marker to label the MT cytoskeleton, while OligoTIP allows for live monitoring of the growing MT plus-ends. These engineered MT binders have been successfully utilized to label the MT network, monitor cell division, track MT plus-ends, and assess the effect of tubulin acetylation on the MT stability at the single-cell level. Moreover, OligoMT and OligoTIP can be repurposed as biosensors for quantitative assessment of drug actions and for reporting enzymatic activity. Overall, these engineered MT binders hold promise for advancing the mechanistic dissection of MT biology and have translational applications in cell-based high-throughput drug discovery efforts.

The real-time and room-temperature detection of nitrogen dioxide (NO₂) holds significant importance for environmental monitoring. However, the performance of NO₂ sensors has been hampered by the trade-off between the high sensitivity and stability of conventional sensitive materials. Here, we present a novel fully flexible paper-based gas sensing structure by combining a homogeneous screen-printed titanium carbide (Ti₃C₂T_x) MXene-based nonmetallic electrode with a MoS₂ quantum dots/Ti₃C₂T_x (MoS₂ QDs/Ti₃C₂T_x) gas-sensing film. These precisely designed gas sensors demonstrate an improved response value (16.3% at 5 ppm) and a low theoretical detection limit of 12.1 ppb toward NO₂, which exhibit a remarkable 3.5-fold increase in sensitivity compared to conventional Au interdigital electrodes. The outstanding performance can be attributed to the integration of the quantum confinement effect of MoS₂ QDs and the conductivity of Ti₃C₂T_x, establishing the main active adsorption sites and enhanced charge transport pathways. Furthermore, an end-sealing effect strategy was applied to decorate the defect sites with naturally oxygen-rich tannic acid and conductive polymer, and the formed hydrogen bonding network at the interface effectively mitigated the oxidative degradation of the Ti₃C₂T_x-based gas sensors. The exceptional stability has been achieved with only a 1.8% decrease in response over 4 weeks. This work highlights the innovative design of high-performance gas sensing materials and homogeneous gas sensor techniques.

Exosomes, nanosized extracellular vesicles containing biomolecular cargo, are increasingly recognized as promising noninvasive biomarkers for cancer diagnosis, particularly for their role in carrying tumor-specific molecular information. Traditional methods for exosome detection face challenges such as complexity, time consumption, and the need for sophisticated equipment. This study addresses these challenges by introducing a novel droplet microfluidic platform integrated with a surface-enhanced Raman spectroscopy (SERS)-based aptasensor for the rapid and sensitive detection of HER2-positive exosomes from breast cancer cells. Our approach utilized an on-chip salt-induced gold nanoparticles (GNPs) aggregation process in the presence of HER2 aptamers and HER2-positive exosomes, enhancing the hot spot-based SERS signal amplification. This platform achieved a limit of detection of 4.5 log₁₀ particles/mL with a sample-to-result time of 5 min per sample. Moreover, this platform has been successfully applied for HER2 status testing in clinical samples to distinguish HER2-positive breast cancer patients from HER2-negative breast cancer patients. High sensitivity, specificity, and the potential for high-throughput screening of specific tumor exosomes make this SERS-based droplet system a potential liquid biopsy technology for early cancer diagnosis.

Paper-integrated configuration with miniaturized functionality represents one of the future main green electronics. In this study, a paper-based respiration sensor was prepared using a multiwalled carbon nanotube-templated nickel porphyrin covalent organic framework (MWCNTs@COF_NiP-Ph) as an electrical identification component and pencil-drawn graphite electric circuits as interdigitated electrodes (IDEs). The MWCNTs@COF_NiP-Ph not only inherited the high gas sensing performance of porphyrin and the aperture induction effect of COFs but also overcame the shielding effect between phases through the MWCNT template. Furthermore, it possessed highly exposed M-N₄ metallic active sites and unique periodic porosity, thereby effectively addressing the key technical issue of room-temperature sensing for the respiration sensor. Meanwhile, the introduction of a pencil-drawing approach on common printing papers facilitates the inexpensive and simple manufacturing of the as-fabricated graphite IDE. Based on the above advantages, the MWCNTs@COF_NiP-Ph respiration sensor had the characteristics of wide detection range (1-500 ppm), low detection limit (30 ppb), acceptable flexibility for toluene, and rapid response/recovery time (32 s/116 s). These advancements facilitated the integration of the respiration sensor into surgical masks and clothes with maximum functionality at a minimized size and weight. Moreover, the primary internal mechanism of COF_NiP-Ph for this efficient toluene detection was investigated through in situ FTIR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers, resulting in alterations in sensor resistance upon exposure to the target gas. The encouraging results revealed the feasibility of employing a paper-sensing system as a wearable platform in green electronics.

Parkinson's Disease is the second most common neurological disease in the United States, yet there is no cure, no pinpointed cause, and no definitive diagnostic procedure. Parkinson's is typically diagnosed when patients present with motor symptoms such as slowness of movement and tremors. However, none of these are specific to Parkinson's, and a confident diagnosis of Parkinson's is typically only achieved when 60-80% of dopaminergic neurons are no longer functioning, at which point much of the damage to the brain is irreversible. This Perspective details ongoing efforts and accomplishments in biosensor research with the goal of overcoming these issues for Parkinson's diagnosis and care, with a focus on the potential impact of early diagnosis and associated opportunities to pinpoint a cause and a cure. We critically analyze the strengths and shortcomings of current technologies and discuss the ideal characteristics of a diagnostic technology toolbox to guide future research decisions in this space. Finally, we assess what role biosensors can play in facilitating precision medicine for Parkinson's patients.

At present, the application of rare-earth organic frameworks (Ln-MOFs) in fluorescence sensing has entered rapid development and shown great potential in various analytical fields, such as environmental analysis, food analysis, drug analysis, and biological and clinical analysis by utilizing their internal porosity, tunable structural size, and energy transfer between rare-earth ions, ligands, and photosensitizer molecules. In addition, because the luminescence properties of rare-earth ions are highly dependent on the structural details of the coordination environment surrounding the rare-earth ions, and although their excitation lifetimes are long, they are usually not burst by oxygen and can provide an effective platform for chemical sensing. In order to further promote the development of fluorescence sensing technology based on Ln-MOFs, we summarize and review in detail the latest progress of the construction of Ln-MOF materials for fluorescence sensing applications and related sensor components, including design strategies, preparation methods, and modification considerations and initially propose the future development prospects and prospects.

There are more than 50 neurodegenerative disorders, and amyotrophic lateral sclerosis (ALS) is one of the most common disorders that poses diagnostic and treatment challenges. The poly glycine-proline (polyGP) dipeptide repeat is a toxic protein that has been recognized as a pharmacodynamic biomarker of C9orf72-associated (c9+) ALS, a subtype of ALS that originates from genetic mutation. Early detection of polyGP will help healthcare providers start timely gene therapy. Herein, we developed a label-free electrochemical immunoassay for the simple detection of polyGP in unprocessed cerebrospinal fluid (CSF) samples collected from ALS patients in the National ALS Biorepository. For the first time, an electrografted laser-induced graphene (E-LIG) electrode system was employed in a sandwich format to detect polyGP using a label-free electrochemical impedance technique. The results show that the E-LIG-modified surface exhibited high sensitivity and selectivity in buffer and CSF media with limit of detection values of 0.19 and 0.27 ng/mL, respectively. The precision of the calibration model was better in CSF than in the buffer. The E-LIG immunosensor can easily select polyGP targets in the presence of other dipeptide proteins translated from the c9 gene. Further study with CSF samples from ALS patients demonstrated that the label-free E-LIG-based immunosensor not only quantified polyGP in the complex CSF matrix but also distinguished between c9+ and non-c9- ALS patients.

Microwave gas sensors have garnered attention for their high sensitivity and selectivity in the detection of volatile organic compounds (VOCs). However, traditional gas sensors generally rely on sensitive materials that degrade over time and are easily affected by the environment, compromising their stability and accuracy. This study proposes a microwave VOC gas sensor based on the condensation effect. The sensor adopts a novel design without sensitive materials, utilizing the condensation effect to detect acetone gas. The sensor system consists of a microwave sensor and a temperature control device. As the sensor temperature is lowered below the boiling point of acetone, the condensation of acetone gas on the sensor surface is achieved, enabling accurate detection of acetone gas. Experimental results indicate that the accumulated amount of acetone on the sensor surface is positively correlated with its response, with the maximum response of 3000 ppm acetone gas reaching 0.34 dB. Additionally, this study investigated the detection mechanism of the sensor after adding the sensitive material MXene and compared the performance of the sensor at different temperatures (-10 °C, 0 °C, and 60 °C). The results show that at -10 °C the sensor mainly captures acetone through physical adsorption, while at 25 and 60 °C, it primarily responds through chemical adsorption, with a maximum response of 0.29 dB. The VOC sensor based on the condensation effect without sensitive materials not only achieves the same sensitivity as traditional microwave sensors but also demonstrates stronger stability and anti-interference capabilities.