In a common genetic disorder, a blood test reveals when benign tumours turn cancerous

People with an inherited condition known as neurofibromatosis type 1, or NF1, often develop non-cancerous, or benign, tumours that grow along nerves. These tumours can sometimes turn into aggressive cancers, but there hasn’t been a good way to determine whether this transformation to cancer has happened. Researchers from the National Cancer Institute’s (NCI) Center for Cancer Research, part of the National Institutes of Health, and Washington University School of Medicine in St. Louis have developed a blood test that, they believe, could one day offer a highly sensitive and inexpensive approach to detect cancer early in people with NF1. The blood test could also help doctors monitor how well patients are responding to treatment for their cancer. The findings are published in the August 31 issue of PLOS Medicine.  NF1 is the most common cancer predisposition syndrome, affecting 1 in 3,000 people worldwide. The condition, caused by a mutation in a gene called NF1, is almost always diagnosed in childhood. Roughly half of the people with NF1 will develop large but benign tumours on nerves, called plexiform neurofibromas. In up to 15% of people with plexiform neurofibromas, these benign tumours turn into an aggressive form of cancer known as malignant peripheral nerve sheath tumour or MPNST. Patients with MPNST have a poor prognosis because cancer can quickly spread and often becomes resistant to both chemotherapy and radiation. Among people diagnosed with MPNST, 80% die within five years. “Imagine going through life with a cancer predisposition syndrome like NF1. It’s kind of like a ticking bomb,” said study co-author Jack F. Shern, M.D., a Lasker Clinical Research Scholar in NCI’s Pediatric Oncology Branch. “The doctors are going to be watching for cancerous tumours, and you’re going to be watching for them, but you really want to discover that transformation to cancer as early as possible.” Doctors currently use either imaging scans (MRI or PET scan) or biopsies to determine if plexiform neurofibromas have transformed into MPNST. However, biopsy findings aren’t always accurate and the procedure can be extremely painful for patients because the tumours grow along nerves. Imaging tests, meanwhile, are…

Education, Engineering, Science, Research,

New algorithm boosts the energy efficiency of wireless network

New Delhi, 28th Aug 2021: A team of researchers from the Indian Institute of Technology (IIT) Bombay and Monash University, Australia, has developed a new algorithm that identifies the right amount of power level, enhancing the energy efficiency of a Radio Frequency (RF)-energy harvesting network. The algorithm, in this study, uses a statistical tool called the multi-armed bandit method that does not depend on channel state information parameters, and the source identifies the optimal power output, IIT Bombay statement said. The performance results of the algorithm are presented in the journal IEEE Wireless Communications Letters. Radio Frequency (RF) signals are electromagnetic radiations used in wireless communication. RF signals transmit information and carry an inherent small electrical energy component. Emerging technology harvests this electrical energy and powers many wireless devices (called nodes) over a wide area, such as medical implants or IoTs. The project is funded by the Department of Science & Technology (DST) and Science and Engineering Research Board (SERB), Govt of India, through the Innovation in Science Pursuit for Inspired Research (INSPIRE) Faculty Fellowship and Early Career Research Award (ECRA); and the Australian Research Councils Discovery Early Career Researcher Award (DECRA) Scheme. “An actual transmission system is a complex network with several receivers spread over a region receiving different amounts of energy for harvesting. Also, they will require different amounts of energy for successfully transmitting the information,” says Prof Manjesh Hanawal, lead author of the study. As the environment is uncertain, reinforcing the algorithms with sequential decision making can quickly ascertain the status of the harvested energy, thereby improving the system’s energy efficiency, he adds. However, traditional optimisation techniques drastically increase computation costs as they require information on the channel state parameters. To overcome this hurdle, the team used a sequential optimisation method in the algorithm called the Multi-Armed Bandit technique that relies only on detecting if a receiver’s feedback signal was successfully decoded or not (a yes-no status). This technique is akin to exploring multiple levers and playing the best lever of a slot machine (gambling devices) at a given time. First, the player risks a few losses…


Scientists find a new way to fight cancer

p53 is one of the most well studied proteins in cancer biology. Like all proteins, its levels and activity are tightly controlled andthey go through cycles of birth-existence-death: the protein is synthesised, it does its function and then it is degraded.