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Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. As executive director of the XPRIZE Healthspan competition, Jamie Justice oversees a professional science fair for cutting-edge longevity science around the world. With her team of more than a dozen judges, Justice, a gerontology researcher, has spent the past several years soliciting and then reviewing applications from the world's best scientific minds, looking for the most exciting ideas on how to live longer, better.

Exosomes are touted as a trendy cure-all. We don't know if they work. There's a trendy new cure-all in town--you might have seen ads pop up on social media or read rave reviews in beauty magazines. Exosomes are being touted as a miraculous treatment for hair loss, aging skin, acne, eczema, pain conditions, long covid, and even neurological diseases like Parkinson's and Alzheimer's. That's, of course, if you can afford the price tag--which can stretch to thousands of dollars.

The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.

Exosomes are small vesicles crucial for cell signaling and disease biomarkers. Due to their complexity, an "omics" approach is preferable to individual biomarkers. While Raman spectroscopy is effective for exosome analysis, it requires high sample concentrations and has limited sensitivity to lipids and proteins. Surface-enhanced Raman spectroscopy helps overcome these challenges. In this study, we leverage Neo4j graph databases to organize 3,045 Raman spectra of exosomes, enhancing data generalization. To further refine spectral analysis, we introduce a novel spectral filtering process that integrates the PageRank Filter with optimal Dimensionality Reduction. This method improves feature selection, resulting in superior classification performance. Specifically, the Extra Trees model, using our spectral processing approach, achieves 0.76 and 0.857 accuracy in classifying hyperglycemic, hypoglycemic, and normal exosome samples based on Raman spectra and surface, respectively, with group 10-fold cross-validation. Our results show that graph-based spectral filtering combined with optimal dimensionality reduction significantly improves classification accuracy by reducing noise while preserving key biomarker signals. This novel framework enhances Raman-based exosome analysis, expanding its potential for biomedical applications, disease diagnostics, and biomarker discovery.

Dr. Seneff is a Senior Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory in Cambridge, Massachusetts, USA. She has a BS from MIT in biology and MS, EE, and PhD degrees from MIT in electrical engineering and computer science. Her recent interests have focused on the role of toxic chemicals and micronutrient deficiencies in health and disease, with a special emphasis on the pervasive herbicide, glyphosate, and the mineral, sulfur. This is an edited segment from the weekly live General Assembly meeting on January 3, 2022. The full meeting can be viewed here. This clip is also available on Rumble and Odysee. "Thank you so much Dr. Seneff!!! Genius presentation, so many important information brought to us easy to understand." -Dr. "Thank you for your important work. The mitigating treatments are hopeful for those who have been coerced into accepting these injections." "Dr Seneff, I hope you will come back and tell us more about your work and the mechanism for the other types of harms that your work has predicted." "Thank you so much, Dr. Seneff." -Helena K "Thank you Dr Seneff, amazing presentation." "Thank you Dr Seneff, that was amazing!" -Dr Tess Lawrie "Beautiful and substantial presentation – thank you, Dr. Seneff!" – Susan I just want to read this quote at the end of this book "The Real Anthony Fauci" and it's because it's Martin Luther King Jr.