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Prostate-specific antigen (PSA) is the most widely used biomarker for the early diagnosis of prostate cancer. Existing methods for PSA detection are burdened with some limitations and require improvement. Herein, we developed a novel microfluidic-electrochemical (μFEC) detection system for PSA detection. First, we constructed an electrochemical biosensor based on screen-printed electrodes (SPEs) with modification of gold nanoflowers (Au NFs) and DNA tetrahedron structural probes (TSPs), which showed great detection performance. Second, we fabricated microfluidic chips by DNA TSP-Au NF-modified SPEs and a PDMS layer with designed dense meandering microchannels. Finally, the μFEC detection system was achieved based on microfluidic chips integrated with the liquid automatic conveying unit and electrochemical detection platform. The μFEC system we developed acquired great detection performance for PSA detection in PBS solution. For PSA assays in spiked serum samples of the μFEC system, we obtained a linear dynamic range of 1-100 ng/mL with a limit of detection of 0.2 ng/mL and a total reaction time <25 min. Real serum samples of prostate cancer patients presented a strong correlation between the "gold-standard" chemiluminescence assays and the μFEC system. In terms of operation procedure, cost, and reaction time, our method was superior to the current methods for PSA detection and shows great potential for practical clinical application in the future.
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http://dx.doi.org/10.1038/s41378-021-00258-x | DOI Listing |
Macromol Biosci
September 2025
IMEM-BRT Group, Departament d'Enginyeria Química, EEBE, Universitat Politècnica de Catalunya, Barcelona, Spain.
This study investigates a multifunctional hydrogel system integrating carboxymethyl cellulose (CMC) in a 3D-printed limonene (LIM) scaffold coated with poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS). The system allows to enhance wound healing, prevent infections, and monitor the healing progress. CMC is crosslinked with citric acid (CA) to form the hydrogel matrix (CMC-CA), while the 3D-printed limonene (LIM) scaffold is embedded within the hydrogel to provide mechanical support.
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September 2025
Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
Epilepsy is a common chronic nervous system disease that threatens human health. However, the role of FOXC1 and its relations with pyroptosis have not been fully studied in epilepsy. Sprague-Dawley rats were obtained for constructing temporal lobe epilepsy (TLE) models.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Strain sensors have received considerable attention in personal healthcare due to their ability to monitor real-time human movement. However, the lack of chemical sensing capabilities in existing strain sensors limits their utility for continuous biometric monitoring. Although the development of dual wearable sensors capable of simultaneously monitoring human motion and biometric data presents significant challenges, the ability to fabricate these sensors with geometries tailored to individual users is highly desirable.
View Article and Find Full Text PDFVestn Oftalmol
September 2025
Korolev Samara National Research University, Samara, Russia.
Objective: This study evaluated the outcomes of a 36-month follow-up after treatment with the ELLEX 2RT nanosecond laser.
Material And Methods: The study included 72 patients divided into two groups. Group 1 received 2RT nanosecond laser therapy, while group 2 did not undergo laser treatment.
J Thorac Oncol
July 2025
Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
Introduction: TNM staging systems create prognostic categories by anatomic extent of disease. Whether therapeutically important molecular alterations in NSCLC augment the prognostic information of TNM staging is unclear. To study this, we analyzed molecular data from the ninth edition of the lung cancer staging system.
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