Roy H. Campbell

Dual modal photoacoustic/ultrasound localization (PAUL) imaging for monitoring blood brain barrier disruption (Conference Presentation)

Shensheng Zhao, Sayantani Basu, Ji Shi, Kewei Song et autres

The blood-brain barrier (BBB) plays a crucial role in normal brain functioning. Impairment of the BBB is linked to various neurological disorders. Therefore, monitoring BBB disruption is essential for understanding its pathogenesis and guiding therapeutic interventions. However, current imaging techniques lack the …

An unsupervised framework for comparing SARS-CoV-2 protein sequences using LLMs

Sayantani B. Littlefield, Roy H. Campbell

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic led to 700 million infections and 7 million deaths worldwide. While studying these viruses, scientists developed a large amount of sequencing data that was made available to researchers. Large language models (LLMs) are …

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Prediction, prognosis and monitoring of neurodegeneration at biobank-scale via machine learning and imaging

Anant Dadu, Michael Ta, Nicholas J. Tustison, Ali Daneshmand et autres

Summary Background Alzheimer’s disease and related dementias (ADRD) and Parkinson’s disease (PD) are the most common neurodegenerative conditions. These central nervous system disorders impact both the structure and function of the brain and may lead to imaging changes that precede symptoms. Patients …

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Whole-brain functional photoacoustic/ultrasound localization (PAUL) imaging for monitoring blood-brain barrier modulation

Yun‐Sheng Chen, Shensheng Zhao, Sayantani Basu, Ji Shi et autres

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Abstract 4164: Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment

Shensheng Zhao, Sayantani Basu, Roy H. Campbell, Yang Zhao et autres

Abstract Photoacoustic (PA) imaging can map the physiological conditions of tissues and track the biodistribution of contrast agents. Ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. The integration of these techniques offers a potential …

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Prediction, Prognosis and Monitoring of Neurodegeneration at Biobank-Scale via Machine Learning and Imaging

Anant Dadu, Michael Ta, Ali Daneshmand, Kenneth Marek et autres

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Detection of Novel COVID-19 Variants with Zero-Shot Learning

Sayantani Basu, Roy H. Campbell, Karrie Karahalios

The COVID-19 (COrona VIrus Disease) pandemic has lead to several genes being sequenced, which are then assigned variant names based on their lineages. Several new variants have evolved with time where machine learning algorithms can be used to rapidly assign variants to …

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Application of Aligned-UMAP to longitudinal biomedical studies

Anant Dadu, Vipul Satone, Rachneet Kaur, Mathew J. Koretsky et autres

High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. The recently proposed Aligned-UMAP, an extension of the …

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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

Anant Dadu, Vipul Satone, Rachneet Kaur, Sayed Hadi Hashemi et autres

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well …

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Application of Aligned-UMAP to longitudinal biomedical studies

Anant Dadu, Vipul Satone, Rachneet Kaur, Mathew J. Koretsky et autres

Abstract Longitudinal multi-dimensional biological datasets are ubiquitous and highly abundant. These datasets are essential to understanding disease progression, identifying subtypes, and drug discovery. Discovering meaningful patterns or disease pathophysiologies in these datasets is challenging due to their high dimensionality, making it difficult …

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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts.

Faraz Faghri, Anant Dadu, Vipul Satone, Rachneet Kaur et autres

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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

Anant Dadu, Vipul Satone, Rachneet Kaur, Sayed Hadi Hashemi et autres

Abstract Background The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes …

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