Outdated BIOS firmware in Illumina iSeq 100 allows attackers to disable devices or install malware, threatening critical genetic research and vaccine.
Category: biotech/medical – Page 865
Polymer-based network gives artificial cells a life-like cytoskeleton
Just like your body has a skeleton, every cell in your body has a skeleton—a cytoskeleton to be precise. This provides cells with mechanical resilience, as well as assisting with cell division. To understand how real cells work, e.g. for drug and disease research, researchers create artificial cells in the laboratory.
However, many artificial cells to date cannot be used to study how cells respond to forces as they don’t have a cytoskeleton. TU/e researchers have designed a polymer-based network for artificial cells that mimics a real cytoskeleton, thus making it possible to study with greater accuracy in artificial cells how cells respond to forces.
The research is published in the journal Nature Chemistry.
AI Predicts Autoimmune Disease Progression with New Genetic Tool
Summary: Researchers have developed a Genetic Progression Score (GPS) using artificial intelligence to predict the progression of autoimmune diseases from preclinical symptoms to full disease. The GPS model integrates genetic data and electronic health records to provide personalized risk scores, improving prediction accuracy by 25% to 1,000% over existing models.
This method identifies individuals at higher risk earlier, enabling timely interventions and better disease management. The framework could also be adapted to study other underrepresented diseases, offering a breakthrough in personalized medicine and health equity.
New Study Shows Stress-Induced DNA Damage Can Speed Up Aging
A study from the University of Minnesota Medical School links social stress to accelerated aging, finding that stress damages DNA
DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).
Could This Be the Cure? Targeting Protein Imbalances To Stop Alzheimer’s
Scientists have identified a key nucleolar complex that could be instrumental in combating neurodegenerative diseases. This complex plays a critical role in maintaining cellular health by regulating protein homeostasis (proteostasis)—the process by which cells ensure proper protein balance and function.
Research reveals that suppressing this nucleolar complex significantly reduces the toxic effects of proteins associated with Alzheimer’s.
Alzheimer’s disease is a progressive neurological disorder that primarily affects older adults, leading to memory loss, cognitive decline, and behavioral changes. It is the most common cause of dementia. The disease is characterized by the buildup of amyloid plaques and tau tangles in the brain, which disrupt cell function and communication. There is currently no cure, and treatments focus on managing symptoms and improving quality of life.
New Biomarker Links Brain Waste Clearance to Vascular Dementia
Summary: A new study has identified a biomarker, DTI-ALPS, which connects glymphatic system dysfunction to vascular dementia. By analyzing over 3,750 participants, researchers found that lower DTI-ALPS scores correlated with worse executive function, highlighting the glymphatic system’s role in clearing brain waste.
The study also uncovered a potential pathway linking impaired waste clearance to cognitive decline, mediated by free water accumulation in white matter. These findings provide a robust tool for clinical trials and potential interventions, including lifestyle changes and medications, to enhance glymphatic function and treat vascular dementia.
Could AI Help Predict the Next Pandemic?
This article outlines examples of where AI has been utilized to predict disease outbreaks and how AI models could help inform future strategies for controlling the spread of infectious diseases to prevent possible pandemics.
AI’s contribution to pandemic preparedness
In August 2024, the World Health Organization (WHO) updated its list of pathogens that could spark the next pandemic, which grew to include more than 30 pathogens. The microorganisms were selected based on available evidence showing them to be highly transmissible and virulent, with limited access to vaccines and treatments. While some pathogens on the list may never cause an epidemic, the growing number of pathogens of concern highlights the need for new tools to help predict and control the spread of infectious diseases.
Jellyfish Protein Shines Bright in Quantum Sensor for Biomedical Applications
While most of us are familiar with magnets from childhood games of marveling at the power of their repulsion or attraction, fewer realize the magnetic fields that surround us—and the ones inside us. Magnetic fields are not just external curiosities; they play essential roles in our bodies and beyond, influencing biological processes and technological systems alike. A recent arXiv publication from the University of Chicago’s Pritzker School of Molecular Engineering and Argonne National Laboratory highlights how magnetic fields in the body may be analyzed using quantum-enabled fluorescent proteins, with hopes of applying to cell formation or early disease detection.
Detecting subtle changes in magnetic fields may equate to beyond subtle impacts in certain fields. For instance, quantum sensors could be applied to the detection of electromagnetic anomalies in data centers, potentially revealing evidence of malicious tampering. Similarly, they might be used to study changes in the brain’s electromagnetic signals, offering insights into neurological diseases such as the onset of dementia. However, these applications demand sensors that are not only sensitive but also capable of operating reliably in real-world conditions.
Spin qubits, known for their notable sensitivity to magnetic fields, are introduced in the study as a compelling solution. Traditionally, spin qubits have been formed from nitrogen-vacancy centers in diamonds. While these systems have demonstrated remarkable precision, the diamonds’ bulky size in relation to molecules and complex surface chemistry limit their usability in biological environments. This creates a need for a more adaptable and biologically compatible sensor.
A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost
Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.
Snap judgments: How first impressions of faces shape inferences of mental states
When we first meet another person, we typically form an initial impression of them based on their facial features and appearance. These first impressions of others could potentially influence our subsequent cognitive processes, such as what mental states we believe that the people we meet are experiencing at a given time.
Researchers at the University of California San Diego (UCSD), the California Institute of Technology and Dartmouth College carried out a study investigating the potential relationship between first impressions of faces and the inference of mental states. Their findings, published in Nature Human Behavior, suggest that first impressions of faces influence the inference of other people’s mental states.
“Over the years there have been a lot of surprising findings showing how first impressions from faces can predict important outcomes, such as which candidates would win an election, which politicians would be convicted of corruption, and which offenders would be sentenced to death,” Chujun Lin, first author of the paper, told Medical Xpress.