Background: Measurement of urinary sulfated bile acid (USBA) is a non-invasive method to detect bile congestion. Our aim was to evaluate the feasibility of USBA analysis for the early detection of biliary atresia (BA).
Methods: We determined the USBA-to-creatinine ratio (USBA/cr) in 1148 infants at 10-40 days after birth. All infants were followed until the 3- to 4-month postnatal routine health check. The cutoff value for USBA/cr was 55.0 µmol/g creatinine.
Results: Among the infants tested, 47 (4.10%) had USBA/cr ratios that exceeded the cutoff value. Two of these 47 infants had liver disease; one was diagnosed with neonatal hepatitis syndrome, and the other was diagnosed with BA. The BA patient underwent USBA analysis for the first time on day 18 after birth and hepatoportoenterostomy on day 49. No other infants were diagnosed with hepatobiliary disease during the follow-up period.
Conclusion: This USBA analysis provided the correct assessment without fail and identified a case of BA. This approach could be used for the screening and early detection of BA when the false-positive rate is decreased by improving the methods for sample collection and urine storage.
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http://dx.doi.org/10.1111/j.1442-200X.2010.03268.x | DOI Listing |
Sci Rep
December 2024
India Meteorological Department, New Delhi, 110003, India.
Desert locusts, notorious for their ruinous impact on agriculture, threaten over 20% of Earth's landmass, prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. Recent invasions, such as those in India, where losses reached US$ 3 billion in 2019-20 alone, underscore the urgency of action.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFBAY 2413555 is a novel selective and reversible positive allosteric modulator of the type 2 muscarinic acetylcholine (M2) receptor, aimed at enhancing parasympathetic signaling and restoring cardiac autonomic balance for the treatment of heart failure (HF). This study tested the safety, tolerability and pharmacokinetics of this novel therapeutic option. REMOTE-HF was a multicenter, double-blind, randomized, placebo-controlled, phase Ib dose-titration study with two active arms.
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December 2024
Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea.
In optical imaging of solid tumors, signal contrasts derived from inherent tissue temperature differences have been employed to distinguish tumor masses from surrounding tissue. Moreover, with the advancement of active infrared imaging, dynamic thermal characteristics in response to exogenous thermal modulation (heating and cooling) have been proposed as novel measures of tumor assessment. Contrast factors such as the average rate of temperature changes and thermal recovery time constants have been investigated through an active thermal modulation imaging approach, yielding promising tumor characterization results in a xenograft mouse model.
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