Several different approaches have been taken to development of homogeneous fluorescent aptamer assays including end-labeled beacons and signaling aptamers which are intrinsically quenched by nucleotides. Two new strategies dubbed "intrachain" and "competitive" FRET-aptamer assays are summarized in this review. Intrachain and competitive FRET-aptamers can be engineered on the molecular level through a series exploratory experiments involving prior knowledge of aptamer secondary or tertiary structures and hypotheses about aptamer conformational changes. However, there is an intrinsic risk of altering aptamer affinity or specificity associated with chemical modifications of an aptamer. Natural selection methods for FRET-aptamers have also been devised to potentially obviate the chemical modification problem. The naturally selected aptamers are subjected to fluorophore (F)- and or quencher (Q)-conjugated nucleotide triphosphate (NTP) incorporation by polymerase chain reaction (PCR) with permissive polymerases such as Deep Vent exo-, but still demonstrate sensitive and specific assay performance despite modified bases, because they are ultimately selected after decoration with F and Q. This paper summarizes work in this area and presents some new examples of the engineered and naturally selected FRET-aptamers for detection of vitamin D.
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http://dx.doi.org/10.2174/138620711796367175 | DOI Listing |
Sci Rep
January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Climate change-related risk mitigation is typically addressed using cost-benefit analysis that evaluates mitigation strategies against a wide range of simulated scenarios and identifies a static policy to be implemented, without considering future observations. Due to the substantial uncertainties inherent in climate projections, this identified policy will likely be sub-optimal with respect to the actual climate trajectory that evolves in time. In this work, we thus formulate climate risk management as a dynamic decision-making problem based on Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), taking real-time data into account for evaluating the evolving conditions and related model uncertainties, in order to select the best possible life-cycle actions in time, with global optimality guarantees for the formulated optimization problem.
View Article and Find Full Text PDFInfect Dis Now
January 2025
Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK.
Antimicrobial resistance (AMR) poses a global health challenge, particularly in maritime environments where unique conditions foster its emergence and spread. Characterized by confined spaces, high population density, and extensive global mobility, ships create a setting ripe for the development and dissemination of resistant pathogens. This review aims to analyse the contributing factors, epidemiological challenges, mitigation strategies specific to AMR on ships and to propose future research directions, bridging a significant gap in the literature.
View Article and Find Full Text PDFBackground: Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding.
Purpose: We present a semi-automated analysis of 21 years of R-type National Cancer Institute (NCI) grants to departments of radiation oncology and radiology using natural language processing (NLP).
Methods: We selected all non-education R-type NCI grants from 2000 to 2020 awarded to departments of radiation oncology/radiology with affiliated schools of medicine.
Toxicology
January 2025
Deparment of clinical pharmacy, Jieyang People's Hospital, 522000, China. Electronic address:
Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these challenges, we developed InterDIA, an interpretable machine learning framework for predicting DIA toxicity based on molecular physicochemical properties. Multi-strategy feature selection and advanced ensemble resampling approaches were integrated to enhance prediction accuracy and overcome data imbalance.
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