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Exosomes as Biomarkers Exosomes as biomarkers represent a breakthrough in diagnostics and personalized medicine. Exosomes are small extracellular vesicles secreted by cells that carry a variety of proteins, lipids, and nucleic acids (such as mRNA, miRNA, and DNA) that reflect the physiological and pathological status of their parent cells. This makes them highly valuable as biomarkers for diagnosing and monitoring diseases. One of the main advantages of exosomes as biomarkers is that they are found in biological fluids such as blood, urine, saliva, and cerebrospinal fluid. Their presence in these easily accessible samples enables non-invasive or minimally invasive diagnostic procedures. This is in stark contrast to traditional biopsies, which are invasive and often uncomfortable for patients. Exosomes in body fluids carry molecular signatures that reflect the status of tissues (including tumors) and can therefore be used to monitor disease progression or treatment response in real time. For example, in cancer, tumor-derived exosomes (TDEs) carry oncogenic proteins, miRNAs, and DNA fragments that can be detected in the bloodstream. These exosomes provide insights into tumor heterogeneity and metastasis, helping clinicians monitor disease progression, detect early recurrence, or evaluate treatment efficacy. Likewise, exosomes from immune cells can serve as biomarkers for autoimmune or inflammatory diseases because they carry molecules that reflect immune status. Exosomes also show promise as prognostic tools, predicting the likelihood of disease recurrence or treatment response. For example, specific miRNA profiles in exosomes have been associated with chemotherapy resistance in cancer patients, allowing clinicians to adjust treatment accordingly. In summary, exosomes are emerging as a powerful tool in the biomarker field, providing a non-invasive method for diagnosing, monitoring, and predicting disease progression. Their potential to provide real-time information about disease status and treatment response is changing the landscape of personalized medicine and diagnostics. References [1] Md Nurul Huda et al., ACS Biomaterials Science & Engineering 2021 (https://lnkd.in/eg7UByn6) [2] Maria Mosquera-Heredia et al., Biomedicines 2021 (doi: 10.3390/biomedicines9081061) [3] Yupeng Huang et al., Frontiers in Immunology 2020 (DOI:10.3389/fimmu.2020.01509) #Exosomes #Biomarkers #PersonalizedMedicine #Diagnostics #CancerResearch #NonInvasiveTesting #ExosomeBiomarkers #BiomedicalInnovation #DiseaseMonitoring #ExosomeTherapy #PrecisionMedicine #BiotechResearch #HealthcareInnovation #Newsletter #MedicalAdvances

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 Nobel Prize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”   The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.   The diversity of life testifies to proteins’ amazing capacity as chemical tools. They control and drive all the chemical reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues.   Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.   The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.   In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.   Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind. Learn more Press release: https://bit.ly/3TM8oVs Popular information: https://bit.ly/3XYHZGp Advanced information: https://bit.ly/4ewMBta