A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It therefore represents both a global public health threat and a precision medicine challenge. The use of artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in the context of analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national level ‘data commons’ (SteatoSITE) as an exemplar, the opportunities as well as the technical challenges of large-scale databases in MASLD research are highlighted

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis

Onchocerciasis, caused by the filarial parasite Onchocerca volvulus, afflicts millions of people, causing such debilitating symptoms as blindness and acute dermatitis. There are no accurate, sensitive means of diagnosing O. volvulus infection. Clinical diagnostics are desperately needed in order to achieve the goals of controlling and eliminating onchocerciasis and neglected tropical diseases in general. In this study, a metabolomics approach is introduced for the discovery of small molecule biomarkers that can be used to diagnose O. volvulus infection. Blood samples from O. volvulus infected and uninfected individuals from different geographic regions were compared using liquid chromatography separation and mass spectrometry identification. Thousands of chromatographic mass features were statistically compared to discover 14 mass features that were significantly different between infected and uninfected individuals. Multivariate statistical analysis and machine learning algorithms demonstrated how these biomarkers could be used to differentiate between infected and uninfected individuals and indicate that the diagnostic may even be sensitive enough to assess the viability of worms. This study suggests a future potential of these biomarkers for use in a field-based onchocerciasis diagnostic and how such an approach could be expanded for the development of diagnostics for other neglected tropical diseases

Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells

Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectroscopic microscopy, emphasizing their potential for studying LDs without molecular labels, and we highlight the growing integration of artificial intelligence. Clinical applications in disease diagnosis and therapy are also considered

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field