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Category: biotech/medical – Page 675

A recent study published in Nature Machine Intelligence examines a novel deep-learning method known as BigMHC, which can predict when the immune system will respond to triggers from cancer-related protein fragments, thus killing the tumors. This study was led and conducted by a team of researchers at Johns Hopkins University and holds the potential to develop personalized cancer immunotherapies and vaccines.

Rendition of cytotoxic CD8+ T-cells identifying cancer cells via receptor binding neoantigens. (Credit: Image generated by DALL-E 2 from OpenAI)

“Cancer immunotherapy is designed to activate a patient’s immune system to destroy cancer cells,” said Dr. Rachel Karchin, who is a professor of biomedical engineering, oncology and computer science at Johns Hopkins University, and a co-author on the study. “A critical step in the process is immune system recognition of cancer cells through T-cell binding to cancer-specific protein fragments on the cell surface.”

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A new study led by University of Maryland physicists sheds light on the cellular processes that regulate genes. Published in the journal Science Advances, the paper explains how the dynamics of a polymer called chromatin—the structure into which DNA is packaged—regulate gene expression.

Through the use of machine learning and statistical algorithms, a research team led by Physics Professor Arpita Upadhyaya and National Institutes of Health Senior Investigator Gordon Hager discovered that can switch between a lower and higher mobility state within seconds. The team found that the extent to which chromatin moves inside cells is an overlooked but important process, with the lower mobility state being linked to gene expression.

Notably, (TFs)—proteins that bind specific DNA sequences within the chromatin polymer and turn on or off—exhibit the same mobility as that of the piece of chromatin they are bound to. In their study, the researchers analyzed a group of TFs called , which are targeted by drugs that treat a variety of diseases and conditions.

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We’ve explored bioelectricity in cells. We’ve looked at bioelectricity within the human body. Now, functional use of “electrical engineering” is being found in the realms between.

Physicists learn about electrostatics, the laws governing stationary charges. Then they learn about electrodynamics, the laws governing moving charges. Biologists are finding that life utilizes both systems of laws at all scales, from within the cell to tissues, organs, and entire organisms. Here are some recent discoveries in the emerging science of bioelectricity.

How does that tick jump from its twig onto your clothing as you walk through brush? The answer, says Current Biology, is by hopping on an electrostatic bullet train. A cow or other host animal walking through the bushes carries a net static charge. The tick, regardless of its own charge polarity, is “pulled by these electric fields across air gaps of several body lengths.”

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Researchers have determined a new feature of how the natural ends of our chromosomes are protected from harmful outcomes.

In a new study, University of Michigan researchers looked at how the DNA damage recognition process seems to know the difference between harmful DNA breaks that need repair versus the natural ends of chromosomes, called , that need to be left alone.

“If possible, you repair it, and if you can’t repair it, then the cell dies. You don’t want to keep dividing with broken DNA. That’s what happens in a normal cell, and that’s a good thing,” said Jayakrishnan Nandakumar, a professor of molecular, cellular and developmental biology.

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O.o!!!!!

In the last 28-day period (July 17 to August 13), over 1.4 million new COVID-19 cases and over 2,300 deaths were reported from the World Health Organization’s (WHO) six regions, an increase of 63% and a decrease of 56%, respectively, compared to the previous 28 days, noted the latest WHO report.

As of August 13, over 769 million confirmed cases and over 6.9 million deaths have been reported globally. While four WHO regions have reported decreases in the number of both cases and deaths, the Western Pacific Region has reported an increase in cases and a decrease in deaths.

Also, WHO stressed the increase in cases of contracting the new Covid variant Eris or EG.5, noting that as of August 17, Eris was detected in 50 countries.

Continue reading “WHO: 63% increase recorded in number of COVID-19 cases worldwide in last 28 days” | >

Google DeepMind researchers have finally found a way to make life coaching even worse: infuse it with generative AI.

According to internal documents obtained by The New York Times reports, Google and the Google-owned DeepMind AI lab are working with “generative AI to perform at least 21 different types of personal and professional tasks.” And among those tasks, apparently, is an effort to use generative AI to build a “life advice” tool. You know, because an inhuman AI model knows everything there is to know about navigating the complexities of mortal human existence.

As the NYT points out, the news of the effort notably comes months after AI safety experts at Google said, back in just December, that users of AI systems could suffer “diminished health and well-being” and a “loss of agency” as the result of taking AI-spun life advice. The Google chatbot Bard, meanwhile, is barred from providing legal, financial, or medical advice to its users.

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Recent advancements in deep learning have significantly impacted computational imaging, microscopy, and holography-related fields. These technologies have applications in diverse areas, such as biomedical imaging, sensing, diagnostics, and 3D displays. Deep learning models have demonstrated remarkable flexibility and effectiveness in tasks like image translation, enhancement, super-resolution, denoising, and virtual staining. They have been successfully applied across various imaging modalities, including bright-field and fluorescence microscopy; deep learning’s integration is reshaping our understanding and capabilities in visualizing the intricate world at microscopic scales.

In computational imaging, prevailing techniques predominantly employ supervised learning models, necessitating substantial datasets with annotations or ground-truth experimental images. These models often rely on labeled training data acquired through various methods, such as classical algorithms or registered image pairs from different imaging modalities. However, these approaches have limitations, including the laborious acquisition, alignment, and preprocessing of training images and the potential introduction of inference bias. Despite efforts to address these challenges through unsupervised and self-supervised learning, the dependence on experimental measurements or sample labels persists. While some attempts have used labeled simulated data for training, accurately representing experimental sample distributions remains complex and requires prior knowledge of sample features and imaging setups.

To address these inherent issues, researchers from the UCLA Samueli School of Engineering introduced an innovative approach named GedankenNet, which, on the other hand, presents a revolutionary self-supervised learning framework. This approach eliminates the need for labeled or experimental training data and any resemblance to real-world samples. By training based on physics consistency and artificial random images, GedankenNet overcomes the challenges posed by existing methods. It establishes a new paradigm in hologram reconstruction, offering a promising solution to the limitations of supervised learning approaches commonly utilized in various microscopy, holography, and computational imaging tasks.

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Two molecular languages at the origin of life have been successfully recreated and mathematically validated, thanks to pioneering work by Canadian scientists at Université de Montréal.

The study, “Programming : allostery vs. multivalent mechanism,” published August 15, 2023 in the Journal of the American Chemical Society, opens new doors for the development of nanotechnologies with applications ranging from biosensing, drug delivery and .

Living organisms are made up of billions of nanomachines and nanostructures that communicate to create higher-order entities able to do many essential things, such as moving, thinking, surviving and reproducing.

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In a recent pre-print study posted to the medRxiv* server, researchers conducted a comprehensive genome-wide association study (GWAS) to elucidate the genetic architecture of circulating retinol, identify its potential causal relationships with various clinical phenotypes, and evaluate its therapeutic or nutritional implications.

Study: Genetic influences on circulating retinol and its relationship to human health. Image Credit: SciePro/Shutterstock.com.

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

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What if we could identify the earliest warning signs of cardiovascular disease from a simple saliva sample? Scientists think they have found a way to do so. Gum inflammation leads to periodontitis, which is linked with cardiovascular disease.

The team used a simple oral rinse to see if levels of —an indicator of —in the saliva of healthy adults could be linked to warning signs for cardiovascular disease. they found that high levels correlated with compromised flow-mediated dilation, an early indicator of poor arterial health.

“Even in young healthy adults, low levels of oral inflammatory load may have an impact on cardiovascular health—one of the leading causes of death in North America,” said Dr. Trevor King of Mount Royal University, corresponding author of the study published in Frontiers in Oral Health.

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