X-ray detection limit and sensitivity are important figure of merits for perovskite X-ray detectors, but literatures lack a valid mathematic expression for determining the lower limit of detection for a perovskite X-ray detector. In this work, we present a thorough analysis and new method for X-ray detection limit determination based on a statistical model that correlates the dark current and the X-ray induced photocurrent with the detection limit. The detection limit can be calculated through the measurement of dark current and sensitivity with an easy-to-follow practice. Alternatively, the detection limit may also be obtained by the measurement of dark current and photocurrent when repeatedly lowering the X-ray dose rate. While the material quality is critical, we show that the device architecture and working mode also have a significant influence on the sensitivity and the detection limit. Our work establishes a fair comparison metrics for material and detector development.
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http://dx.doi.org/10.1038/s41467-021-25648-7 | DOI Listing |
Adv Sci (Weinh)
January 2025
Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics, Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, School of Laboratory Medicine and Biotechnology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in disease regulation. However, the practical application of EV-circRNAs in the early diagnosis of GC is yet to be thoroughly explored due to the low accuracy of EV-circRNAs analysis.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pathology, University of Texas Medical Branch, Galveston, Texas, United States of America.
Tick-borne spotted fever rickettsioses (SFRs) continue to cause severe illness and death in otherwise-healthy individuals due to lack of a timely and reliable diagnostic laboratory test. We recently identified a diagnostic biomarker for SFRs, the putative N-acetylmuramoyl-l-alanine amidase RC0497. Here, we developed a prototype laboratory test that targets RC0497 for diagnosis of SFRs.
View Article and Find Full Text PDFAnal Chem
January 2025
Anhui Province Key Laboratory of Major Autoimmune Diseases, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei 230032, China.
The absence of an effective imaging tool for diagnosing renal ischemia-reperfusion injury (RIRI) severely delays its treatment, and currently, no definitive clinical interventions are available. Pyroglutamate aminopeptidase-1 (PGP-1), a potential inflammatory cytokine, has shown considerable potential as a biomarker for tracing the inflammatory process in vivo. However, its exact role in the enhanced visualization of RIRI in complex biological systems has yet to be fully established.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Infectious Diseases, The First Affiliated of Wannan Medical College, Wuhu 241001, Anhui Province, China.
Background: Chronic schistosomiasis causes multiple organ and multiple system diseases, especially the digestive system. Schistosome eggs are mainly deposited in the stomach, liver and colorectal, but a few eggs are deposited in the appendix and cause disease. At present, there are few studies on schistosomal appendicitis.
View Article and Find Full Text PDFWater Res X
December 2024
Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.
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