These authors contributed equally to this work. ⇤ Corresponding authors.The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system 2 All rights reserved. No reuse allowed without permission.
Fungal polyketides with the resorcylic acid lactone (RAL) scaffold are of interest for growth stimulation, the treatment of cancer, and neurodegenerative diseases. The RAL radicicol is a nanomolar inhibitor of the chaperone Hsp90, whose repression leads to a combinatorial blockade of cancer-causing pathways. Clustered genes for radicicol biosynthesis were identified and functionally characterized from the endophytic fungus Chaetomium chiversii, and compared to recently described RAL biosynthetic gene clusters. Radicicol production is abolished upon targeted inactivation of a putative cluster-specific regulator, or either of the two polyketide synthases that are predicted to collectively synthesize the radicicol polyketide core. Genomic evidence supports the existence of flavin-dependent halogenases in fungi: inactivation of such a putative halogenase from the C. chiversii radicicol locus yields dechloro-radicicol (monocillin I). Inactivation of a cytochrome P450 epoxidase furnishes pochonin D, a deepoxy-dihydro radicicol analog.
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
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