Uncovering the molecular drivers of liver cancer
Peer-Reviewed Publication
Updates every hour. Last Updated: 11-Sep-2025 15:11 ET (11-Sep-2025 19:11 GMT/UTC)
In the U.S., credit card fraud costs $5 billion annually, identity theft adds $16.4 billion, and Medicare fraud drains $60 billion each year. A new machine learning breakthrough generates accurate fraud labels from large, imbalanced datasets without costly, time-consuming labeled data. It outperforms traditional methods by reducing false positives and minimizing cases needing further inspection, crucial for sectors like Medicare and credit card fraud, where fast data processing is vital to preventing losses and improving efficiency.
In a major leap toward more comfortable and reliable health monitoring, scientists from Sun Yat-sen University have developed a cutting-edge glucose sensor that combines advanced nanomaterials with machine learning to detect glucose without the need for blood samples. The new technology is built around organic electrochemical transistors (OECTs), which are known for their ability to function safely and efficiently in watery environments, making them ideal for wearable and biocompatible devices.
The integration of the high conductivity of MoS2 and porously structural MOF materials led to a distinct improvement in device transconductance (gm) from 6.5 mS to 19.34 mS. This approach combined with machine leaning paves the way for advancements in OECT technology and broaden the potential of hybrid materials applied in organic biosensors.
To address the growing health threats posed by climate change, Nanyang Technological University, Singapore (NTU Singapore) is launching a new interdisciplinary research centre focused on climate change and environmental health in the tropics.