Acrolein, a highly toxic α, β-unsaturated aldehyde, has been a longstanding key biomarker associated with a range of disorders related to oxidative stresses. One of the most promising methods for detecting acrolein involves the use of antibodies that can recognize the acrolein-lysine conjugate, 3-formyl-3, 4-dehydropiperidines (FDP), within oxidatively stressed cells and tissues from various disease states. We have uncovered here that FDP could reduce nitroarenes in high yields at 100 °C in the presence of excess CaCl as a Lewis acid promoter. This unique transformation allowed for the development of a de novo method for detecting levels of FDPs generated from proteins in urine or blood serum samples. Thus we successfully converted a non-fluorescent and inexpensive 4-nitrophthalonitrile probe to the corresponding fluorescent aniline, thereby constituting the concept of fluorescent switching. Its sensitivity level (0.84 nmol/mL) is more than that of ELISA assays (3.13 nmol/mL) and is already equally reliable and reproducible at this early stage of development. More importantly, this method is cost effective and simple to operate, requiring only mixing of samples with a kit solution. Our method thus possesses potential as a future alternative to the more costly and operatively encumbered conventional antibody-based methods.
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http://dx.doi.org/10.1038/srep35872 | DOI Listing |
Sensors (Basel)
November 2024
School of Information Engineering, Huzhou University, Huzhou 313000, China.
Sensors (Basel)
March 2024
College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth's surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the "curse of dimensionality" leading to the "Hughes phenomenon", "strong correlation" due to high resolution, and "nonlinear characteristics" caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task.
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February 2024
Department of Engineering and Technology, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing.
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November 2023
Software College, Northeastern University, Shenyang 110167, China.
With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate.
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October 2023
School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK.
Sleep posture and movements offer insights into neurophysiological health and correlate with overall well-being and quality of life. Clinical practices utilise polysomnography for sleep assessment, which is intrusive, performed in unfamiliar environments, and requires trained personnel. While sensor technologies such as actigraphy are less invasive alternatives, concerns about their reliability and precision in clinical practice persist.
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