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The p16 Immunostaining Predicts the Risk of Recurrence in Prostate Cancer.
Asian Pac J Cancer Prev
January 2025
Postgraduate Program in Oncology, Haroldo Juaçaba Hospital, Ceará Cancer Institute (ICC), Brazil.
Objective: This study aimed to investigate the influence of p16 immunohistochemical expression on the biochemical recurrence rate of pT2-pT3 prostate cancer.
Materials And Methods: A total of 488 pT2-pT3 stage prostate adenocarcinomas undergoing radical prostatectomy were included in this study. Following a review of Gleason classification and retrieval of sociodemographic and clinicopathological data, as well as the date of last consultation and biochemical recurrence, immunohistochemistry for p16 was performed.
Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.
Nucleic Acids Res
January 2025
Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, 0372, Norway.
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient.
View Article and Find Full Text PDFsp. nov., isolated from tree bark ( Chev.) and its antioxidant activity.
Int J Syst Evol Microbiol
January 2025
Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand.
A Gram-stain-positive, facultatively anaerobic, rod-shaped strain, designated SPB1-3, was isolated from tree bark. This strain exhibited heterofermentative production of dl-lactic acid from glucose. Optimal growth was observed at 25-40 °C, pH 4.
View Article and Find Full Text PDFComparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions.
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
View Article and Find Full Text PDFNanoparticle-protein corona enhances accuracy of Ca-19.9-based pancreatic cancer classification.
Nanoscale
January 2025
Institute of Inorganic Chemistry, Graz University of Technology, 8010 Graz, Austria.
Among the various types of pancreatic cancers, pancreatic ductal adenocarcinoma (PDAC) is the most lethal and aggressive, due to its tendency to metastasize quickly and has a particularly low five-year survival rate. Carbohydrate antigen 19-9 (CA 19-9) is the only biomarker approved by the Food and Drug Administration for PDAC and has been a focal point in diagnostic strategies, but its sensitivity and specificity are not sufficient for early and accurate detection. To address this issue, we introduce a synergistic approach combining CA 19-9 levels with a graphene oxide (GO)-based blood test.
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