This paper presents an advanced diascopic illumination technique for simultaneous multi-wavelength fluorescence excitation and detection without using any spatial filter sets. The proposed system includes a home-built dark-field condenser comprising a high N.A. objective and a light stop-film to excite fluorescence and an ultraviolet-visible-near infrared (UV-VIS-NIR) spectrometer to detect emitted signals. Since no direct light source enters the optical detection system, no complex optical filter is required for multi-wavelength fluorescence detection. This study also designs an optimized stop-film pattern to obtain the best performance in exciting fluorescent samples and reduce background light. Experimental results show that the proposed system can effectively increase the fluorescent signal and simultaneously detect a mixed sample composed of 2',7'-dichlorofluorescein, Rhodamine B, Atto610, and Atto647N. Furthermore, this proposed system successfully separates and detects a mixed bio-sample composed of three single-stranded DNA samples labeled with Cy3, FITC, and Alexa647 fluorescence in a single channel. A simple and fast calculation removes noise and fluorescent cross-effect for conveniently observing the electropherograms. The proposed system has a measured detection limit up to 5x10(-8)M (S/N=3) while detecting a standard fluorescence of 2',7'-dichlorofluoresein, which is capable of detecting fluorescence samples in general applications. The proposed method provides a simple and straightforward way to detect multi-wavelength fluorescence for CE analysis.
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http://dx.doi.org/10.1016/j.bios.2009.07.036 | DOI Listing |
BMC Pharmacol Toxicol
January 2025
Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
Purpose: This study aims to assess the risks associated with drug-induced macular edema and to examine the epidemiological characteristics of this condition.
Methods: This study analyzed data from the U.S.
Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images.
View Article and Find Full Text PDFSci Rep
January 2025
Electronics and Communication Engineering Department, Mansoura University, Mansoura, 35516, Egypt.
As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Monitoring fluid intake and output for congestive heart failure (CHF) patients is an essential tool to prevent fluid overload, a principal cause of hospital admissions. Addressing this, bladder volume measurement systems utilizing bioimpedance and electrical impedance tomography have been proposed, with limited exploration of continuous monitoring within a wearable design. Advancing this format, we developed a conductivity digital twin from radiological data, where we performed exhaustive simulations to optimize electrode sensitivity on an individual basis.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy.
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