Background And Aims: Pancreatic ductal adenocarcinoma (PDAC) is infrequent. Currently, non-invasive biomarkers for early detection of PDAC are not accessible. Here, we intended to identify a set of urine markers able to discriminate patients with early-stage PDAC from healthy individuals.
Patients And Methods: Seventy-five urine samples from PDAC patients and 50 healthy controls were assayed using quantitative real-time PCR (qPCR). The chosen biomarkers were lymphatic vessel endothelial HA receptor (LYVE-1), regenerating islet-derived 1 alpha (REG1A), and trefoil factor family (TFF1).
Results: LYVE-1, REG1A, and TFF1 expression in PDAC proved to be significantly elevated compared to healthy individuals (p < 0.05). Determination of these markers' expression might be useful for early tumor diagnosis with a sensitivity of 96 %, 100 %, and 73.33 % respectively, and a specificity of 100 %, 82 %, and 100 % respectively.
Conclusion: We have recognized three diagnostic biomarkers REG1A, TFF1, and LYVE1 that can detect patients with early-stage pancreatic cancer in non-invasive urine specimens with improved sensitivity and specificity. To the best of our knowledge, there have been no prior investigations examining the mRNA expression levels of them in urine within the Egyptian population.
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http://dx.doi.org/10.1016/j.compbiolchem.2024.108171 | 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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