#biomolecules

Understanding biomolecules is key to mastering bio-chemistry for NEET.

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The field of synthetic and structural biology is experiencing a revolution, driven by advancements in artificial intelligence. Scientists are now able to design new proteins with highly specific functions – from targeted antibodies to crucial blood clotting agents – thanks to computer algorithms that can accurately predict the three-dimensional structure based solely on an amino acid sequence. A…

Leveraging Uncertainty for Enhanced Biomolecule Efficacy Prediction Recent advancements in artificial intelligence have opened exciting avenues for predicting the efficacy of biomolecules, crucial for drug discovery and personalized medicine. A new study explores how in-context learners, particularly TabPFN models, can be significantly improved through a novel uncertainty-guided approach. This…

Cryo electron microscopy (cryo-EM or cryo-TEM) is a powerful technique used to determine the structure of biomolecules at near-atomic resolution. In cryo-EM, biological samples such as proteins, nucleic acids, and protein complexes are rapidly frozen in a thin film of vitreous ice and imaged using an electron microscope. Cryo-EM overcomes many of the limitations of X-ray crystallography by not requiring crystallization of samples. This allows structure determination of more heterogeneous and dynamic molecules.  

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Glycomics, the study of complex carbohydrates known as glycans, represents a multifaceted field that delves into the diverse roles these molecules play in biological systems. By unraveling the intricate relationships between glycans and cellular function, researchers navigate a complex landscape that holds promise for advancing our understanding of health, disease, and beyond.

Glycomics in Biological Context: Glycans are ubiquitous in nature, adorning cell surfaces and influencing a myriad of physiological processes, from cell-cell recognition to immune response modulation.

Understanding the functional significance of Glycomics requires comprehensive analysis of their structures, interactions, and dynamics within biological systems.

Glycan Biosynthesis and Regulation

The intricate process of glycan biosynthesis is tightly regulated within cells, involving a complex network of enzymes, transporters, and regulatory factors.

Dysregulation of glycan biosynthesis pathways can have profound implications for cellular function, contributing to disease states such as cancer, autoimmune disorders, and metabolic syndromes.

Glycomics Technologies and Analytical Approaches

Advances in glycomics technologies, including mass spectrometry, glycan microarrays, and glycan profiling techniques, have revolutionized our ability to study glycans in unprecedented detail.

These analytical approaches enable researchers to map glycan structures, characterize glycan-protein interactions, and elucidate glycan-mediated signaling pathways, providing valuable insights into their functional roles.

New Post has been published on https://thedigitalinsider.com/machine-learning-aided-non-invasive-imaging-for-rapid-liver-fat-visualization-technology-org/

Machine Learning-Aided Non-Invasive Imaging for Rapid Liver Fat Visualization - Technology Org

The proposed framework, which is label-free and rapid, can enable an early diagnosis, treatment, and prevention of liver diseases.

Steatotic liver disease (SLD), previously known as non-alcoholic fatty liver disease, which includes a range of conditions caused by fat build-up in the liver due to abnormal lipid metabolism, affects about 25% of the population worldwide, making it the most common liver disorder.

Often referred to as “silent liver disease,” SLD progresses without noticeable symptoms and can lead to more severe conditions like cirrhosis (liver scarring) and liver cancer.

A liver biopsy―an invasive procedure involving liver tissue sample extraction from the body―is the conventional method of testing for SLD. To simplify detection, a research team led by Professor Kohei Soga of Tokyo University of Science (TUS) had previously introduced near-infrared hyperspectral imaging (NIR-HSI) as a non-invasive method to visualize the total lipid content in the liver.

NIR light, with longer wavelengths (800-2500 nm) than ultraviolet and visible light shows absorption attributed to various organic substances, including biomolecules in tissues, enabling the identification of fat distribution in the liver.

Now, in a new study published in the journal Scientific Reports, the research team, including Prof. Kohei Soga, Associate Professor Masakazu Umezawa, and Associate Professor Masao Kamimura from TUS, and Professor Naoko Ohtani from Osaka Metropolitan University, has improved upon this method by having a machine learning model differentiate the type of lipids present in the liver at a pixel-by-pixel level.

The framework differentiates lipids based on the hydrocarbon chain length (HCL) and degree of saturation (DS) of fatty acids, helping estimate the risk of SLD progression, steatohepatitis (NASH), and SLD/NASH-associated liver cancer.

“In addition to qualitative information, such as the total lipid content, we can now also visualize qualitative information, such as the characteristics of the distribution of fatty acids contained in lipids, mainly triglycerides,” says Dr. Umezawa.

Notably, identifying lipids based on molecular composition using NIR-HSI faced challenges due to the overlapping absorption spectra of various biomolecules. To address this, the researchers used a support vector regression machine learning model, which was trained to recognize the composition of 16 fatty acids.

This training data was obtained through gas chromatography analysis of liver samples of mice that were fed diets of varying fat content. By applying machine learning to NIR-HSI data, it became possible to interpret the spectral information in terms of the distribution of fat (DS and HCL) within the liver.

DS, indicating the double bonds or degree of saturation of the fatty acids, is calculated as the CH2 fraction from the sum of the CH and CH2 numbers. HCL, representing the fatty acid chain length, is determined by the ratio of CH3 + CH2 + CH + 1(COOH) groups to the number of CH3 groups.

Using this method, the researchers successfully determined the fatty acid composition in mice livers, revealing correlations with the fat contents in their diets. For instance, the livers of mice on a diet rich in saturated fats like palmitic acid and myristic acid exhibited a notably high DS, whereas mice fed with unsaturated fats such as α-linoleic acid showed a low DS.

The DS, HCL, and total lipid content were depicted as a color map, offering a unique visual representation of fat distribution in the liver, thus simplifying the diagnosis of fatty liver conditions. “Visualization of lipid distribution in higher-dimensional information rather than simply using total lipid content as a single parameter provides a novel tool for revealing the pathophysiological conditions of liver diseases and metabolism,” remarks Dr. Umezawa.

Indeed, by providing a rapid and label-free technique to identify fatty liver, which affects a large population segment, the method could be a potential alternative to invasive liver biopsy procedures, transforming liver care.

This novel framework could also find potential applications in pharmacological research, such as drug metabolism, toxicity, and efficacy; studies on metabolic disorders through metabolic imaging; and identifying responders and non-responders in clinical trials.

The researchers also expect the framework to find applications in identifying personalized nutritional strategies―tailoring plans and optimizing interventions for better nutrition―through biomarker identification and treatment response prediction. In summary, the novel framework developed by the researchers could revolutionize healthcare and related research.

Source: Tokyo University of Science