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Category: biotech/medical – Page 95

Biomedical engineers at the University of Melbourne have developed a 3D bioprinting system capable of creating structures that closely replicate various human tissues, ranging from soft brain tissue to more rigid materials like cartilage and bone.

This innovative technology provides cancer researchers with a powerful tool for replicating specific organs and tissues, enhancing their ability to predict drug responses and develop new treatments. By offering a more accurate and ethical approach to drug discovery, it also has the potential to reduce reliance on animal testing.

Head of the Collins BioMicrosystems Laboratory at the University of Melbourne, Associate Professor David Collins said: In addition to drastically improving print speed, our approach enables a degree of cell positioning within printed tissues. Incorrect cell positioning is a big reason most 3D bioprinters fail to produce structures that accurately represent human tissue.

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Scientists at the La Jolla Institute for Immunology (LJI) have identified a potential new target for Parkinson’s disease treatment. Their research highlights the role of a specific brain cell protein in triggering the disease and may explain why Parkinson’s is more prevalent in men.

Recent studies from LJI suggest that autoimmunity plays a key role in Parkinson’s onset. Their latest findings, published in The Journal of Clinical Investigation, reveal that the protein PINK1 may label certain brain cells for attack by the immune system, contributing to disease progression.

“This research allows us to better understand the role of the immune system in Parkinson’s disease,” says LJI Professor Alessandro Sette, Dr. Biol. Sci., senior author of the recent study.

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Breakthrough in early detection of cholangiocarcinoma using ai-powered spectroscopy.

In a major advancement for cholangiocarcinoma (CCA) detection, researchers have developed a cutting-edge AI-driven diagnostic method that could revolutionize early cancer screening. Utilizing Surface-Enhanced Raman Spectroscopy (SERS), a powerful non-invasive technique, the team introduced a novel approach combining Discrete Wavelet Transform (DWT) with a one-dimensional Convolutional Neural Network (1D CNN) to distinguish early-stage CCA from precancerous, inflammatory, and healthy conditions.

Unlike traditional Principal Component Analysis (PCA) with Support Vector Machine (SVM), which struggles with nonlinear SERS data and only differentiates late-stage CCA, the new AI-enhanced method provides greater accuracy in detecting early-stage cancer, a crucial factor in improving survival rates. Receiver Operating Characteristic (ROC) curve analysis confirmed its superior performance.

The study, conducted on hamster serum, opens the door for future applications in human diagnostics, potentially transforming cancer detection and treatment. This breakthrough underscores the potential of AI and advanced signal processing in enhancing precision medicine and saving lives through early intervention.

This Early detection of cholangiocarcinoma (CCA) is critical for improving patient prognosis and survival rates. Surface-Enhanced Raman Spectroscopy (SERS) offers a promising non-invasive diagnostic tool due to its high sensitivity and specificity. In this study, we propose a novel approach combining Discrete Wavelet Transform (DWT) and a onedimensional Convolutional Neural Network (1D CNN) for the detection and differentiation of first stage CCA from precancerous, inflammation, and healthy states using SERS data. Our method is compared with a traditional Principal Component Analysis (PCA) followed by Support Vector Machine (SVM) classification. In contrast, the PCA + SVM method could only differentiate late-stage CCA and healthy states due to the nonlinearity of the SERS dataset. Receiver Operating Characteristic (ROC) curve analysis further validates the superior performance of our proposed method. We studied on hamster serum and the concept can be extended to human serum in the near future work.

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A workshop led by scientists at the Department of Energy’s Oak Ridge National Laboratory sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories.

Download the report of the “Shaping the Future of Self-Driving Autonomous Laboratories” workshop.

Scientists have dreamed for generations of high-tech laboratories operated via robotics at the push of a button. Recent advancements in artificial intelligence bring those dreams closer to reality than ever before, said Rafael Ferreira da Silva, an ORNL senior research scientist and lead author of the workshop’s report.

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A groundbreaking international study, led by scientists from Ben-Gurion University of the Negev, has mapped the diverse populations of fat cells across different human fat tissues. Using advanced technology, researchers identified distinct subpopulations of fat cells with more complex functions than previously understood. They also discovered variations in how fat tissues communicate at the cellular level.

Published in Nature Genetics, these findings lay the foundation for future research aimed at advancing personalized medicine for obesity.

The research team, led by Prof. Esti Yeger-Lotem and Prof. Assaf Rudich from the Department of Clinical Biochemistry and Pharmacology at the Faculty of Health Sciences at Ben-Gurion University of the Negev, in collaboration with Prof. Naomi Habib from the Hebrew University of Jerusalem, Profs. Matthias Bluher, Antje Korner and Martin Gericke from the University of Leipzig, Germany, and Prof. Rinki Murphy from the University of Auckland, New Zealand, studied the diversity of fat cells in subcutaneous and intra-abdominal (visceral) fat tissues in humans.

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Summary: A new study challenges the long-held belief that the striatum is responsible for selecting actions. Researchers found that instead of making decisions, the striatum and motor cortex work together to specify movement details, such as how to reach for an object.

Using a novel “reach-to-pull” system, they recorded neural activity in mice and found that both regions were active during movement execution, not decision-making. These findings could reshape our understanding of motor control and help improve treatments for movement disorders like Parkinson’s and Huntington’s disease.

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A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry—a technique used to analyze cells or particles in a fluid using a laser beam—affordable and accessible.

The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.

Peter Lillehoj, the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh, assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems & Nanoengineering.

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