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Blog – Page 389

In a recent study published in the journal Cell Reports, researchers used the machine learning (ML)-based Variational Animal Motion Embedding (VAME) segmentation platform to analyze behavior in Alzheimer’s disease (AD) mouse models and tested the effect of blocking fibrinogen-microglia interactions. They found that AD models showed age-dependent behavioral disruptions, including increased randomness and disrupted habituation, largely prevented by reducing neuroinflammation, with VAME outperforming traditional methods in sensitivity and specificity.

Background

Behavioral alterations, central to neurological disorders, are complex and challenging to measure accurately. Traditional task-based tests provide limited insight into disease-induced changes. However, advances in computer vision and ML tools, such as DeepLabCut, SLEAP, and VAME, now enable the segmentation of spontaneous mouse behavior into postural units (motifs) to uncover sequence and hierarchical structure, offering scalable, unbiased measures of brain dysfunction.

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The integration of quantum computing into personalized medicine holds great promise for revolutionizing disease diagnosis, treatment development, and patient outcomes. Quantum computers have the potential to process vast amounts of genetic data much faster than classical computers, enabling researchers to identify patterns and correlations that may not be apparent with current technology. This could lead to breakthroughs in understanding the genetic basis of complex diseases and developing targeted treatments.

Quantum computing also has the potential to revolutionize medical imaging by enabling the simulation of complex magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Quantum algorithms can efficiently process large-scale imaging data, enabling researchers to reconstruct high-resolution images that reveal subtle details about tissue structure and function. This has significant implications for disease diagnosis and treatment, where accurate imaging is critical for developing effective treatments.

The use of quantum computing in personalized medicine raises important ethical considerations, such as concerns about privacy and informed consent. The ability to rapidly analyze large amounts of genetic data also raises questions about how this information should be used and shared with patients. Regulatory frameworks will play a crucial role in shaping the development and deployment of quantum computing in personalized medicine, balancing the need to promote innovation with the need to protect patient safety and privacy.

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I had wondered if AI could just learn and advance from it s users.

During your first driving class, the instructor probably sat next to you, offering immediate advice on every turn, stop and minor adjustment. If it was a parent, they might have even grabbed the wheel a few times and shouted “Brake!” Over time, those corrections and insights developed experience and intuition, turning you into an independent, capable driver.

Although advancements in artificial intelligence (AI) have made a reality, the used to train them remain a far cry from even the most nervous side-seat driver. Rather than nuance and real-time instruction, AI learns primarily through massive datasets and extensive simulations, regardless of the application.

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Results from a recent clinical trial led by physicians at Emory University and Grady Health System indicate that a twice-yearly injection of Lenacapavir offers a 96% reduced risk of HIV infection overall, significantly more effective than the daily oral PrEP.

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“In vivo measurement of basement membrane stiffness showed that ISCs reside in a more rigid microenvironment at the bottom of the crypt,” the article’s authors wrote. “Three-dimensional and two-dimensional organoid systems combined with bioengineered substrates and a stretching device revealed that PIEZO channels sense extracellular mechanical stimuli to modulate ISC function.”

The paper’s first author is Meryem Baghdadi, PhD, a former researcher at SickKids, and the paper’s senior authors are Tae-Hee Kim, PhD, a senior scientist at SickKids, and Danijela Vignjevic, PhD, a research director at Institut Curie. The study they led expanded on the work of one of the paper’s co-authors, Xi Huang, PhD, a senior scientist at SickKids.

In 2018, Huang found that PIEZO ion channels influence tumor stiffening in brain cancer. Inspired by this research, the collaborators in the current study set out to explore how stem cells in the intestines use PIEZO channels to stay healthy and function properly.

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