Gene-editing technique could speed up study of cancer mutations
With the new method, scientists can explore many cancer mutations whose roles are unknown, helping them develop new drugs that target those mutations.
With the new method, scientists can explore many cancer mutations whose roles are unknown, helping them develop new drugs that target those mutations.
Ahead of the Institute’s presidential inauguration, panelists describe advances in their research and how these discoveries are being deployed to benefit the public.
Professor Emerita Nancy Hopkins and journalist Kate Zernike discuss the past, present, and future of women at MIT and beyond.
The new diagnostic, which is based on analysis of urine samples, could also be designed to reveal whether a tumor has metastasized.
The technology, which mimics the body’s natural clotting process, could help keep severely injured people alive until they are treated at a hospital.
The printer generates vaccine-filled microneedle patches that can be stored long-term at room temperature and applied to the skin.
In a new study, immunostimulatory drugs slowed tumor growth without producing systemic inflammation.
With sustainability in mind, MIT’s EHS Lab Plastics Recycling Program gathers clean plastics from 212 MIT labs, recycling some 280 pounds per week.
The global health care company Sanofi is providing $25 million to advance RNA research.
Using these RNA-delivery particles, researchers hope to develop new treatments for cystic fibrosis and other lung diseases.
Developed at SMART, the therapy stimulates the host immune system to more effectively clear bacterial infections and accelerate infected wound healing.
Boston teen designers create fashion inspired by award-winning images from MIT laboratories.
A new study reveals that lymph nodes near the lungs create an environment that weakens T-cell responses to tumors.
Using bottlebrush-shaped particles, researchers can identify and deliver synergistic combinations of cancer drugs.
Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.