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Featured Articles

September 17, 2019

Failed Cancer Drug Looks Promising for Scleroderma and Other Fibrotic Conditions 

Sixteen years ago, a research group at Mayo Medical School published results showing that a protein called TRAIL can kill cells that cause liver fibrosis but no one seemed to follow up on these findings. Now, researchers at Johns Hopkins Medicine have improved on this protein and shown that it selectively kills cells that cause the hardening of skin associated with scleroderma, effectively reversing the condition in mice genetically engineered to mimic the disease. A report on these results was published earlier this year in Nature Communications.  Read More  

Novel Approach Leads to Potential Sepsis Prevention in Burn Patients  

Immediately following severe burns, bacteria reach the wound from different sources, including the patient's skin, gastrointestinal tract, respiratory tracts and health care-related human contact. Within the wound, bacteria multiply, establish an infection and move from the infected burn wound into the bloodstream, causing serious complications like sepsis, multiple-organ failure and death. Read More

How Lymphoma Cells Metastasize to the Brain

Lymphomas in the central nervous system are rare but dangerous. Scientists at the German Cancer Research Center (DKFZ) have now discovered which molecular mechanism leads to lymphomas forming metastases in the central nervous system. Using a mouse model, the researchers showed that chronic inflammatory processes in aging brains lead to lymphoma cells that have entered the brain tissue being retained instead of being released directly back into the blood. They also identified key molecules of this mechanism in tissue samples from patients with lymphomas of the central nervous system.  Read More

AI-Based Algorithm May Predict Nonmelanoma Skin Cancer in High-Risk Patients

A nonmelanoma skin cancer risk prediction model using readily available information in the electronic medical records system and a deep learning approach appeared to demonstrate robust discrimination, according to findings published in JAMA Dermatology. “This machine learning-based prediction tool may facilitate determination of which patients are likely to develop [nonmelanoma skin cancer]. Read More