Lifeboat Foundation

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the result of AutoPET, an international competition in medical image analysis, where researchers of Karlsruhe Institute of Technology (KIT) were ranked fifth.

The seven best autoPET teams report in the journal Nature Machine Intelligence on how algorithms can detect tumor lesions in positron emission tomography (PET) and computed tomography (CT).

Imaging techniques play a key role in the diagnosis of cancer. Precisely determining the location, size, and type of tumor is essential for choosing the right therapy. The most important imaging techniques include positron emission tomography (PET) and computer tomography (CT).

Colorectal cancer (CRC) remains one of the most clinically challenging malignancies facing our public health system. CRC accounts for the second and third most common cancer in males and females, respectively. In addition, CRC represents one of the most deadly cancers, expected to result in over 50,000 mortalities in 2024.

Hereditary colorectal cancer (HCRC) occurs when a parent passes a cancer gene to a child. Unfortunately, we have not identified a single gene that causes the disease. Hereditary CRC syndromes, such as hereditary non-polyposis colorectal cancer (HNPCC; also known as Lynch syndrome) and familial adenomatous polyposis (FAP), describe a group of genetic diseases that confer a high risk of developing CRC. As our knowledge has expanded, we have learned about a growing number of genetic variants in the genes that predispose carriers to CRC. However, the precise role of some variants in the development of CRC cancer remains unclear. Uncovering more information about these variants, called variants of uncertain significance.

As our knowledge has expanded, we have learned about a growing number of genetic variants in the genes which predispose carriers to CRC. However, the precise role of some variants in the development of CRC cancer remains unclear. Uncovering more information about these variants, called variants of uncertain significance (VUS), can aid in optimizing screening and surveillance programs.

Space-time may not be continuous but instead made up of many discrete bits – and we may be able to see their effects near the edges of unusually bright black holes.

By Karmela Padavic-Callaghan

Nanotechnology is poised to transform neurological disorder treatments by overcoming the blood-brain barrier, enabling effective medication delivery for conditions like dementia and Alzheimer’s. This innovative approach also shows promise in dermatology and cancer treatment, enhancing drug absorption and targeting, while minimizing side effects. Experts at AIIMS highlighted ongoing research and potential breakthroughs expected in the next few years.

Skeletal editing has emerged as an appealing strategy for scaffold-hopping-based drug discovery, but the enantioselective single-atom skeletal editing of N-heteroarenes is challenging. Now, using trifluoromethyl N-triftosylhydrazones as carbene precursors, the enantiodivergent dearomative skeletal editing of indoles and pyrroles has been achieved through asymmetric carbon-atom insertion.

One day our Sun will die, and consume our world in fire, but is it possible to refuel our Sun so that it might live trillions and trillions of year to come?

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Continue reading “Extending the Sun’s Lifespan” »

When your mind is wandering, your brain’s “default mode” network (DMN) is active. Its discovery 20 years ago inspired a raft of research into networks of brain regions and how they interact with each other. New research, including a recent study of the brain on psilocybin, is revealing the default mode networks’s role in memory, social awareness and sense of self.

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