In a recent study published in Nature Communications, scientists from Cornell University made new findings in common and fatal gastric cancer research. In the United States, the incidence of gastroesophageal cancer increased 2.5-fold from the 1970s to the early 2000s, however, from the 1950s, the number of all patients with gastric cancer decreased by more than 80%; despite this, gastric cancer is still the fifth most common cancer and the third leading cause of cancer death worldwide.
There are a number of ways by which antimicrobial resistance can spread, and one that is of growing concern is the disposal of medicines by consumers down sinks and toilets. A new technique can help to assess the extent of the spread.
New research, published in The Lancet Infectious Diseases, presents the first clinical results with CAL02 in patients suffering from severe pneumonia, the first cause of infectious mortality in the world.
The findings are of significance for pharmaceutical companies and the medical sector. This is in the context of a time of great struggle for antibiotic companies given the increase in instances of antibiotic resistant bacteria. What is of particular global concern is the acceleration of resistance. U.S. Centers for Disease Control and Prevention (CDC) data finds that many high-income countries are entering a “post-antibiotic era.”
Nowadays, the brightest innovations usually happen at the intersection of different disciplines and technologies. A recent scientific achievement by Dr. Carsten Krieg, a researcher at Hollings Cancer Center (HCC), Medical University of South Carolina, is not an exception to this observation.
With an ambitious goal in mind to advance the field of cancer immunotherapy, Dr. Krieg combines a very powerful analytical technique -- mass cytometry, with artificial intelligence (AI), machine learning and bioinformatics tools to visualize the obtained experimental data and have a bird’s-eye view of the immune system.
With all the hot discussions (for instance, here, here, here and here) going on right now among medicinal chemists, pharmaceutical researchers, and data scientists as to what artificial intelligence (AI) means for the future of drug discovery, the life science world has divided into “AI-believers”, “AI-atheists”, and “AI-agnostics”.
It is useless to repeat what has been many times said about successes of AI in areas like natural language processing, image processing, pattern recognition and self-driving cars (here is the summary), but few of us knew if those sort of results (or any meaningful results at all) could possibly be achieved with such complex systems as biological organisms… Finally, however, a hint of hope arrived.