The article highlights a groundbreaking technology developed by engineers at the University of Mississippi that can detect heart attacks faster and more accurately than traditional methods. Electrical and computer engineering assistant professor Kasem Khalil led the research, which was published in Intelligent Systems, Blockchain and Communication Technologies.

The technology uses artificial intelligence and advanced mathematics to design a chip that analyzes electrocardiograms (ECGs) – graphs of the heart’s electrical signals – and detects a heart attack in real-time. This chip is lightweight and energy efficient enough to be embedded in wearable devices while still being 92.4% accurate, higher than many current methods.

In the United States, someone dies from a heart attack every 40 seconds. Heart disease is the leading cause of death in the country. Khalil’s technology aims to improve heart attack detection methods without sacrificing accuracy. The researchers believe that their wearable device can cut down on diagnosis time, allowing patients to receive faster treatment and reducing the likelihood of permanent damage.

The team used a chip design approach that focuses on all aspects of the technology they hope to create, from software development to hardware implementation. This holistic approach allowed them to optimize the system and make it more efficient.

Current methods of heart attack detection often require a patient to go through an electrocardiogram or blood tests in a medical facility, which can take time that a patient might not have. The researchers see their technology as a potential game-changer in real-time diagnosis, allowing patients to receive faster treatment and reducing the risk of permanent damage.

While Khalil’s team continues developing the technology, they envision other health care applications for these devices, such as predicting or identifying seizures, dementia, and other conditions. The detection of diseases depends on the disease itself, but the researchers are working to find faster, more efficient ways of doing that.

This wearable heart attack detection tech has the potential to save lives by enabling real-time diagnosis and reducing the time-sensitive element of heart attacks. Its impact could be significant in improving patient outcomes and reducing mortality rates associated with heart disease.

The world of digital carpentry has long been dominated by complex computer numerical control (CNC) machines, which use computers to automate manufacturing processes. However, a team of researchers from the University of Tokyo has developed a revolutionary system called Draw2Cut that makes it possible for anyone to create intricate designs and objects without prior knowledge of CNC machines or their typical workflows.

Draw2Cut allows users to draw desired designs directly onto material to be cut or milled using standard marker pens. The colors used in these drawings instruct the system on how to mill and cut the design into wood, making it a highly accessible mode of manufacture. This novel approach has been inspired by the way carpenters mark wood for cutting, making it possible for people without extensive experience to create complex designs.

The key to Draw2Cut lies in its unique drawing language, where colors and symbols are assigned specific meanings to produce unambiguous machine instructions. Purple lines mark the general shape of a path to mill, while red and green marks and lines provide instructions to cut straight down into the material or produce gradients. This intuitive workflow makes it possible for users to create complex designs without prior knowledge of CNC machines.

While Draw2Cut is not yet capable of producing items of professional quality, its main aim is to open up this mode of manufacture to more people, making it a valuable tool for hobbyists and professionals alike. The system has been tested with wood, but can also work on other materials such as metal, depending on the capabilities of the CNC machine.

The developers of Draw2Cut have made their source code open-source, allowing developers with different needs to customize it accordingly. This means that users can tailor the color language and stroke patterns to suit their specific requirements, making it an even more versatile tool for digital fabrication.

Overall, Draw2Cut represents a significant breakthrough in the field of digital carpentry, making it possible for anyone to create complex designs and objects without extensive experience or knowledge of CNC machines. Its potential impact on the world of manufacturing is vast, and its intuitive workflow and unique drawing language make it an invaluable tool for hobbyists and professionals alike.

Smart Bandages have long been envisioned as a “lab on skin” that could monitor and treat chronic wounds in patients. Caltech Professor of Medical Engineering Wei Gao and his colleagues are now one step closer to achieving this goal. After successfully demonstrating the efficacy of their smart bandage, iCares, in animal models, they have now cleared another hurdle by showing its ability to continually sample fluid from human patients with chronic wounds.

The improved version of the smart bandage, which integrates three microfluidic components, is designed to clear excess moisture from wounds while providing real-time data about biomarkers present. The innovative microfluidics system ensures that only fresh samples are analyzed, allowing for accurate measurements of biomarkers such as nitric oxide and hydrogen peroxide.

Gao’s team has demonstrated the potential of their smart bandage to detect signs of inflammation and infection in patients up to three days before symptoms appear. Furthermore, they have developed a machine-learning algorithm that can accurately classify wounds and predict healing time with a level of accuracy comparable to expert clinicians.

The iCares system consists of a flexible, biocompatible polymer strip that can be 3D printed at low cost. It integrates nanoengineered biomarker sensor arrays for single-use applications and reuses signal processing and wireless data transmission through a user interface like a smartphone. The triad of microfluidic modules includes a membrane that draws wound fluid from the surface, a bioinspired component that shuttles it to the sensor array where analysis takes place, and a micropillar module that carries the sampled fluid away from the bandage.

The implications of this innovation are vast, with potential applications extending beyond chronic wound care. By integrating real-time monitoring and treatment capabilities into wearable devices, we may soon see significant improvements in patient outcomes and quality of life.