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Clinical integration of germline findings from a tumor testing precision medicine program.
BMC Cancer
January 2025
Department of Medicine, Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada.
Background: Integrating germline genetic testing (GGT) recommendations from tumor testing into hereditary cancer clinics and precision oncology trials presents challenges that require multidisciplinary expertise and infrastructure. While there have been advancements in standardizing molecular tumor boards, the implementation of tumor profiling for germline-focused assessments has only recently gained momentum. However, this progress remains inconsistent across institutions, largely owing to a lack of systematic approaches for managing these findings.
View Article and Find Full Text PDFIdentification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study.
BMC Cancer
January 2025
Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, 1 Xueyuan Road, Fujian, 350108, China.
Objective: This study aims to identify potential lipid biomarkers and metabolic pathways associated with oral cancer (OC). Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls.
Methods: A total of 41 OC patients and 41 controls were recruited from a hospital in Southeast China to examine the serum lipidomics by Ultra-high Performance Liquid Chromatography Q Exactive Mass Spectrometry (UHPLC-QE-MS).
Versatile waste sorting in small batch and flexible manufacturing industries using deep learning techniques.
Sci Rep
January 2025
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting.
View Article and Find Full Text PDFHierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.
Sci Rep
January 2025
Biotechnology Major, Sangmyung University, Seoul, 03016, South Korea.
Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning models typically perform better with more input data, the available wildlife data are ordinarily limited, specifically for rare or endangered species.
View Article and Find Full Text PDFOptimized convolutional neural network using African vulture optimization algorithm for the detection of exons.
Sci Rep
January 2025
Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons.
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