Gastric cancer is a malignant tumor, and its early diagnosis remains challenging due to the lack of simple and sensitive detection methods and specific biomarkers. In this work, to improve the detection reliability, we developed a dual-mode detection strategy for the detection of two biomarkers associated with it. First, an N- and S-doped carbon dots-N-rich porous carbon nanoenzyme (N/S-CDs@NC) was prepared by a two-step pyrolysis of thiourea-penetrated zinc-based zeolite imidazole framework. It was then combined with the 3,3',5,5'-tetramethylbenzidine-HO system for the colorimetric detection of d-amino acids (i.e., d-proline (d-Pro) and d-alanine (d-Ala)) in saliva, based on d-amino acid oxidase catalyzing d-amino acid oxidation to produce HO. In this way, the low detection limits (S/N = 3) of d-Pro and d-Ala were 0.14 and 0.35 μM, respectively. Furthermore, N/S-CDs@NC was combined with the luminol-HO electrochemiluminescence (ECL) system and magnetic immune accumulation/separation strategy to detect the carcinoembryonic antigen (CEA) in serum. The porous N/S-CDs@NC could facilitate participant contact, promote the generation of hydroxyl radical (OH), and electrostatically attract OH, thereby significantly amplifying the ECL signal of luminol and improving the signal stability. Thus, the detection mode showed considerable sensitivity and selectivity, with a low detection limit of 0.26 pg mL. The strategy proposed in this work can also be used for the detection of other disease markers by substituting the recognition elements, thus having good application potential.
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http://dx.doi.org/10.1021/acs.analchem.2c03433 | DOI Listing |
Jpn J Clin Oncol
January 2025
Division of Molecular and Cellular Oncology, Miyagi Cancer Center Research Institute, 47-1 Nodayama, Medeshima-Shiode, Natori, Miyagi 981-1293, Japan.
A Japanese woman with Li-Fraumeni syndrome in her 40s underwent comprehensive genetic profiling accompanied by germline data using the Oncoguide NCC Oncopanel, but no germline pathogenic variants in the tumor suppressor gene TP53 were detected. However, careful examination of additional data in the report suggested the presence of a large TP53 deletion. Custom targeting next-generation sequencing and nanopore sequencing revealed a 3.
View Article and Find Full Text PDFInfect Dis (Lond)
January 2025
Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA.
Background: Whether a detected virus or bacteria is a pathogen that may require treatment, or is merely a commensal 'passenger', remains confusing for many infections. This confusion is likely to increase with the wider use of multi-pathogen PCR.
Objectives: To propose a new statistical procedure to analyse and present data from case-control studies clarifying the probability of causality.
Calcif Tissue Int
January 2025
Endocrinology Department, School of Medicine, Pontificia Universidad Católica de Chile, Av. Diagonal Paraguay 262, Cuarto Piso, Santiago, Chile.
X-linked hypophosphatemia (XLH) is a rare metabolic disorder characterized by elevated FGF23 and chronic hypophosphatemia, leading to impaired skeletal mineralization and enthesopathies that are associated with pain, stiffness, and diminished quality of life. The natural history of enthesopathies in XLH remains poorly defined, partly due to absence of a sensitive quantitative tool for assessment and monitoring. This study investigates the utility of 18F-NaF PET/CT scans in characterizing enthesopathies in XLH subjects.
View Article and Find Full Text PDFBot Stud
January 2025
Crop Science Division, Taiwan Agricultural Research Institute, Ministry of Agriculture, Taichung, 413, Taiwan.
Background: Rice is a staple food for the global population. However, extreme weather events threaten the stability of the water supply for agriculture, posing a critical challenge to the stability of the food supply. The use of technology to assess the water status of rice plants enables the precise management of agricultural water resources.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
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