In this work, a novel electrochemical microRNA (miRNA) detection method based on enzyme amplified biosensing of mir21 from cell lysate of total RNA was demonstrated. The proposed enzymatic detection method was detailed and compared with the conventional guanine oxidation based assay in terms of detection limit and specificity. For the detection of mir21, capture probes and/or cell lysates were covalently attached onto the pencil graphite electrode (PGE) by coupling agents of N-(dimethylamino)propyl-N'-ethylcarbodiimide hydrochloride (EDC) and N-hydroxysulfosuccinimide (NHS). Having immobilized the capture probe onto the surface of PGE, hybridization was achieved with a biotinylated (from its 3' end) complementary target. Extravidin labeled alkaline phosphatase (Ex-Ap) binds to the biotinylated target due to the interaction between biotin-avidin and the enzyme converts electro-inactive alpha naphtyl phosphate (the substrate) to electro-active alpha naphtol (α-NAP, the product). α-NAP was oxidized at +0.23 V vs Ag/AgCl and this signal was measured by Differential Pulse Voltammetry (DPV). The signals obtained from α-NAP oxidation were compared for the probe and hybrid DNA. The specificity of the designed biosensor was proved by using non-complementary sequences instead of complementary sequences and the detection limit of the assay was calculated to be 6 pmol for cell lysates.
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http://dx.doi.org/10.1016/j.bios.2012.05.031 | DOI Listing |
JMIR Form Res
January 2025
Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom.
Background: Traumatic brain injury (TBI) is a significant public health issue and a leading cause of death and disability globally. Advances in clinical care have improved survival rates, leading to a growing population living with long-term effects of TBI, which can impact physical, cognitive, and emotional health. These effects often require continuous management and individualized care.
View Article and Find Full Text PDFMed Oral Patol Oral Cir Bucal
January 2025
Department of Oral Diagnosis, Piracicaba Dental School University of Campinas, 901, Limeira Avenue Postcode: 13414-903. Piracicaba-SP, Brazil
Background: Oral squamous cell carcinoma (OSCC) is an aggressive cancer, with prognosis influenced by clinical variables as well grading systems and perineural invasion (PNI), which are associated to poorer outcomes, including higher rates of recurrence and metastasis. This study aims to evaluate OSCC using three grading systems and assess the impact of PNI and clinicopathologic parameters on patient survival.
Material And Methods: Eighty-one primary OSCC samples were analyzed.
Infect 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 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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