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AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
PLoS One
January 2025
Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy.
Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design.
View Article and Find Full Text PDFEvaluation of bond durability of different self-adhesive bioactive restorative systems to dentin.
Sci Rep
January 2025
Conservative Dentistry Department, Faculty of Dentistry, Mansoura University, Mansoura, Egypt.
This study aimed to compare the bonding efficacy three bioactive self-adhesive restorative systems to dentin. A total of 80 permanent human molars were utilized in this study. The occlusal enamel was removed to exposed mid-coronal dentin; 40 molars were used for microshear bond strength testing, while the remaining molars were used for micromorphological analysis of restoration/dentin interface.
View Article and Find Full Text PDFSupervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched genotypes.
Heliyon
January 2025
Faculty of Engineering, University of Maragheh, Maragheh, Iran.
Sumac is considered as a medicinal and industrial plant. Climate change threats natural ecosystems and hence, evaluation of sumac's genetic diversity, identification of superior genotypes, and conservation of such materials is important. In this study, 5 wild populations of sumac were investigated.
View Article and Find Full Text PDFLarge Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians.
J Nucl Med
January 2025
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Large language models (LLMs) are poised to have a disruptive impact on health care. Numerous studies have demonstrated promising applications of LLMs in medical imaging, and this number will grow as LLMs further evolve into large multimodal models (LMMs) capable of processing both text and images. Given the substantial roles that LLMs and LMMs will have in health care, it is important for physicians to understand the underlying principles of these technologies so they can use them more effectively and responsibly and help guide their development.
View Article and Find Full Text PDFMassively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS).
Nat Commun
January 2025
School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Chip scale DNA synthesis offers a high-throughput and cost-effective method for large-scale DNA-based information storage. Nevertheless, unbiased information retrieval from low-copy-number sequences remains a barricade that largely arises from the indispensable DNA amplification. Here, we devise a simulation-guided quantitative primer-template hybridization strategy to realize massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS).
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