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AI predicts efficacy of breast cancer treatment directly from tumor architecture
Researchers from the University of Helsinki have demonstrated the possibilities of artificial intelligence-based algorithms in predicting the efficacy of a targeted cancer therapy based on the tumour tissue architecture only, without specific molecular tests. The results suggest that AI can reveal previously hidden patterns in tumour samples and allow discovery of novel tumour features predictive of outcome and efficacy of treatment.
Artificial intelligence (AI) in the form of machine learning is increasingly used in cancer research and holds great potential in support of medical diagnostics. Algorithms have already been trained to tackle many complicated tasks such as detection of cancerous tissue and tumour grading. Also, prediction of disease outcome directly from a tumour sample without expert interpretation has shown promising results.
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One of the key objectives of the report was to understand the primary growth drivers and estimate the future size of the 3D cell culture market. Based on multiple parameters, such as business segment, price of 3D cell culture products, and likely adoption of the 3D cell culture products, we have provided informed estimates on the likely evolution of the 3D cell culture systems market in the mid to long term.
An insightful assessment of the current market landscape of companies offering various 3D cell culture systems, along with information on a number of relevant parameters, such as year of establishment, size of employee base, geographical presence, 3D cell culture format (scaffold based products, scaffold free products and 3D bioreactors), and type of product (hydrogels / ECMs, micropatterned surfaces, solid scaffolds, microcarriers, attachment resistant surfaces, suspension systems and microfluidic syst
Editorial Article: The quest for precision medicine: Decoding cancer pathogenesis and overcoming drug resistance
Dr. Komal Kumar Javarappa shares insights from the multidisciplinary forefront of immuno-oncology, where translational biologists race against time to optimize new cancer therapies
08 Jan 2021
Dr. Komal Kumar Javarappa
Blood cancers such as multiple myeloma and acute myeloid leukemia (AML) are relatively rare but often result in worse prognoses compared to many other cancer types. In the past few decades, our understanding has vastly improved and extensive research has increased the five-year survival rates by 35 to 50% for some of these diseases. Progress of this sort is made possible by a staunch collaboration amongst clinicians, cancer researchers, and translational biologists.
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Finnish Biomedical Imaging Node Accepted to Euro-BioImaging Research Infrastructure
Finnish Biomedical Imaging Node FiBI has been accepted as a service-providing Node to the pan-European Euro-BioImaging consortium for imaging technologies. The new Node improves the prerequisites for conducting high quality research and increases the researchers’ opportunities for international collaboration.
Euro-BioImaging is European Research Infrastructure Consortium for the top imaging communities in Europe, with 16 member countries and 25 Nodes. Euro-BioImaging offers both academic and industrial researchers open access to cutting-edge imaging technologies in biological and medical sciences. It also offers expertise as well as training and data management services for state of the art research.
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Novel genomic tools increase accuracy of breast cancer risk assessment
Findings from the FinnGen study encompassing 120,000 women indicate that inherited breast cancer risk should be assessed in an increasingly comprehensive manner. Currently, only individual gene mutations are taken into consideration in breast cancer therapy and prevention. The study demonstrates that more extensive genomic data can be used to identify women who are at high risk of breast cancer with considerably greater accuracy. Such knowledge can especially improve risk assessment among the close relatives of breast cancer patients.
In women, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths. Genetic predisposition is one of the key risk factors associated with the disease.