The future of endoscopy will be dictated by rapid technological advances in the development of light sources, optical fibers, and miniature scanners that will allow for images to be collected in multiple spectral regimes, with greater tissue penetration, and in three dimensions. These engineering breakthroughs will be integrated with novel molecular probes that are highly specific for unique proteins to target diseased tissues. Applications include early cancer detection by imaging molecular changes that occur before gross morphological abnormalities, personalized medicine by visualizing molecular targets specific to individual patients, and image guided therapy by localizing tumor margins and monitoring for recurrence.
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http://dx.doi.org/10.1002/jbio.201100048 | DOI Listing |
World J Microbiol Biotechnol
January 2025
Department of Environmental Engineering, Kyungpook National University, 80 Daehak-Ro, Buk-Gu, Daegu, 41566, South Korea.
Endophytes have significant prospects for applications beyond their existing utilization in agriculture and the natural sciences. They form an endosymbiotic relationship with plants by colonizing the root tissues without detrimental effects. These endophytes comprise several microorganisms, including bacteria and fungi.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFEMBO Mol Med
January 2025
Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
The exposome is the measure of all the exposures of an individual in a lifetime and how those exposures relate to health. Exposomics is the emerging field of research to measure and study the totality of the exposome. Exposomics can assist with molecular medicine by furthering our understanding of how the exposome influences cellular and molecular processes such as gene expression, epigenetic modifications, metabolic pathways, and immune responses.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity.
View Article and Find Full Text PDFSci Rep
January 2025
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction.
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