Lifeboat Foundation

In situ bioprinting, which involves 3D printing biocompatible structures and tissues directly within the body, has seen steady progress over the past few years. In a recent study, a team of researchers developed a handheld bioprinter that addresses key limitations of previous designs, i.e., the ability to print multiple materials and control the physicochemical properties of printed tissues. This device will pave the way for a wide variety of applications in regenerative medicine, drug development and testing, and custom orthotics and prosthetics.

The emergence of regenerative medicine has resulted in substantial improvements in the lives of patients worldwide through the replacement, repair, or regeneration of damaged tissues and organs. It is a promising solution to challenges such as the lack of organ donors or transplantation-associated risks. One of the major advancements in regenerative medicine is on-site (or “in situ”) bioprinting, an extension of 3D printing technology, which is used to directly synthesize tissues and organs within the human body. It shows great potential in facilitating the repair and regeneration of defective tissues and organs.

Although significant progress has been made in this field, currently used in situ bioprinting technologies are not devoid of limitations. For instance, certain devices are only compatible with specific types of bioink, while others can only create small patches of tissue at a time. Moreover, their designs are usually complex, making them unaffordable and restricting their applications.

GW Ron and Joy Paul Kidney Center and GW Transplant Institute. Learn the Symptoms of Kidney Disease with J. Keith Melancon, M.D., Chief of the Division of Tr…

Recent advancements in the field of electronics have enabled the creation of smaller and increasingly sophisticated devices, including wearable technologies, biosensors, medical implants, and soft robots. Most of these technologies are based on stretchy materials with electronic properties.

While material scientists have already introduced a wide range of flexible materials that could be used to create electronics, many of these materials are fragile and can be easily damaged. As damage to materials can result in their failure, while also compromising the overall functioning of the system they are integrated in, several existing soft and conductive materials can end up being unreliable and unsuitable for large-scale implementations.

Researchers at Harbin University of Science and Technology in China recently developed a new conductive and self-healing hydrogel that could be used to create flexible sensors for wearables, robots or other devices. This material and its composition was outlined in the Journal of Science: Advanced Materials and Devices.

Of approximately 250,000 Americans diagnosed with acute pulmonary embolism (PE) in emergency departments each year, most are hospitalized.

But new Michigan Medicine research, published in JAMA Network Open, finds that some patients with PE, a blood clot in one or more pulmonary arteries, may be hospitalized unnecessarily due to computed tomography (CT) imaging results rather than clinical risk factors.

Approximately 40% of the patients in the study had low-risk pulmonary embolism, as defined by the Pulmonary Embolism Severity Index, or PESI score. Roughly half of the low risk patients had CT imaging features that physicians consider “concerning,” and these patients fared just as well in the hospital as those whose CT scans showed no concerning findings.

Summary: A novel study put the diagnostic prowess of generative AI, specifically the chatbot GPT-4, to the test, yielding promising results.

The study involved evaluating the AI’s diagnostic accuracy in handling complex medical cases, with GPT-4 correctly identifying the top diagnosis nearly 40% of the time and including the correct diagnosis in its list of potential diagnoses in 64% of challenging cases.

The success of AI in this study could provide new insights into its potential applications in clinical settings. However, more research is needed to address the benefits, optimal use, and limitations of such technology.

Kids soak up new skills, adults not so much. But neuroscientist Gül Dölen might have found a way—with drugs—to help grownups learn like littles.

Clinical stage generative AI-driven drug discovery company Insilico Medicine has today published a paper on a new multimodal transformer-based aging clock; the new clock is capable of processing diverse data sets and providing insights into biomarkers for aging, mapping them to genes relevant to both aging and disease, and discovering new therapeutic targets to slow or reverse both aging and aging-related diseases.

Insilico calls the aging clock Precious1GPT, in a nod to the powerful “One Ring” in Tolkien’s Lord of the Rings; the findings have been published in the journal Aging.

Longevity. Technology: Insilico has been at the forefront of both generative AI and aging research, and has been publishing studies on biomarkers of aging using advanced bioinformatics since 2014. Later, the company trained deep neural networks (DNNs) on human “multi-omics” longitudinal data and retrained them on diseases to develop its end-to-end Pharma. AI platform for target discovery, drug design, and clinical trial prediction.

Stimulants such as Ritalin and modafinil have a reputation as off-label ‘smart drugs’ that boost cognitive performance. New research suggests they have the opposite effect.

Understanding the mechanisms of interaction between plasma and microparticles is of a critical importance in various fields, including astrophysics, microelectronics, and plasma medicine. A common experimental approach for studying interactions between plasma and microparticles is to place microparticles in a flowing plasma of a gas discharge. In order to achieve a more accurate understanding of the processes occurring in such systems, scientists need fast and efficient tools for calculating forces acting on microparticles in a plasma flow.

Typically, plasma-physicists have to independently develop software tailored to a specific task, which is a significant investment of time and resources. Existing open-source programs frequently encounter challenges related to installation, documentation, and sluggish performance. A group of scientists from the JIHT, the HSE and, MIPT have developed a novel solution: a fast, open-source code which is easy to install and extensively documented.

The outcome—OpenDust—performs ten times faster than existing analogues. In order to accelerate calculations, the algorithm uses multiple GPUs simultaneously.