Russia has said that its coronavirus vaccine had more than 95% efficacy according to new preliminary data, giving it a success rate comparable to vaccines being developed by Pfizer and Moderna.The country also said claimed it had greater efficacy than the Oxford/AstraZeneca vaccine because of Russia’s proprietary technology, which it offered to share with British scientists.
Dublin, Nov. 19, 2020 (GLOBE NEWSWIRE) — The “Future of Therapy: Technology Advances in Drug-device Combination Products” report has been added to ResearchAndMarkets.com’s offering.
Drug device combination products are aimed to provide targeted treatment, enable better drug delivery and improve the efficacy of the device and medicine.
This research service (RS) showcases some of these emerging drug-device combination products including drug eluting stents and drug loaded/coated orthopedic implants under implantable drug-device combination and drug eluting lens and drug eluting bandages under non-implantable drug device combinations. The research service discusses the impact of these innovations, patents, technology roadmap and growth opportunities.
Key Topics Covered
1. Executive Summary 1.1 Scope of the Research 1.2 Research Methodology 1.3 Key Findings
2. Industry Overview 2.1 Drug-device Combination Products Improving Delivery of Drugs or Therapeutics Efficacy of the Device 2.2 Segmentation of Drug-device Combination Based on Product Type 2.3 Segmentation of Drug-device Combination Based on
With the advent of pharmacogenomics, machine learning research is well underway to predict the patients’ drug response that varies by individual from the algorithms derived from previously collected data on drug responses. Entering high-quality learning data that can reflect a person’s drug response as much as possible is the starting point for improving the accuracy of the prediction. Previously, preclinical study of animal models were used which were relatively easier to obtain compared to human clinical data.
In light of this, a research team led by Professor Sanguk Kim in the Department of Life Sciences at POSTECH is drawing attention by successfully increasing the accuracy of anti-cancer drug response predictions by using data closest to a real person’s response. The team developed this machine learning technique through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models. These research findings were published in