Translational Genomics in Neurocritical Care: a Review

Myserlis, Evangelos Pavlos; Radmanesh, Farid; Anderson, Chris

doi:10.1007/s13311-020-00838-1

Cited by 8 publications

(6 citation statements)

References 158 publications

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“…Additionally, the mitochondrial DNA haplotype K was associated with favorable outcome in a large cohort of patients with TBI [12]. Further technological refinements termed "next-generation sequencing" methodologies permit high-throughput sequencing and identification of de novo variants with higher reliability [70,89]. Methods include whole-exome sequencing (WES) and whole-genome sequencing (WGS), where nucleotides are determined in the exome or the genome, respectively.…”

Section: Genomicsmentioning

confidence: 99%

“Omics” in traumatic brain injury: novel approaches to a complex disease

et al. 2021

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Background To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics. Methods In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI. Results The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI. Conclusion The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical “big data” predictive modeling in the near future.

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Section: Genomicsmentioning

confidence: 99%

“Omics” in traumatic brain injury: novel approaches to a complex disease

et al. 2021

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“…Personalized data regarding genomic transcription and translation is a novel and expanding area of research that provides a better understanding of individual risk from injury and potentially treatment, and can serve to guide pharmacological management, as well as interpret data from bed-side monitoring tools. Previously thought to only be beneficial in chronic diseases, new research is shedding light on its utility in real-time critical care settings of inflammatory diseases to improve patient outcomes [107].…”

Section: Rapid Whole Exome Sequencing (Wes) and Rna Sequencingmentioning

confidence: 99%

Neuroinflammation and Precision Medicine in Pediatric Neurocritical Care: Multi-Modal Monitoring of Immunometabolic Dysfunction

Woodward

Jesus

Esser

2020

IJMS

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The understanding of molecular biology in neurocritical care (NCC) is expanding rapidly and recognizing the important contribution of neuroinflammation, specifically changes in immunometabolism, towards pathological disease processes encountered across all illnesses in the NCC. Additionally, the importance of individualized inflammatory responses has been emphasized, acknowledging that not all individuals have the same mechanisms contributing towards their presentation. By understanding cellular processes that drive disease, we can make better personalized therapy decisions to improve patient outcomes. While the understanding of these cellular processes is evolving, the ability to measure such cellular responses at bedside to make acute care decisions is lacking. In this overview, we review cellular mechanisms involved in pathological neuroinflammation with a focus on immunometabolic dysfunction and review non-invasive bedside tools that have the potential to measure indirect and direct markers of shifts in cellular metabolism related to neuroinflammation. These tools include near-infrared spectroscopy, transcranial doppler, elastography, electroencephalography, magnetic resonance imaging and spectroscopy, and cytokine analysis. Additionally, we review the importance of genetic testing in providing information about unique metabolic profiles to guide individualized interpretation of bedside data. Together in tandem, these modalities have the potential to provide real time information and guide more informed treatment decisions.

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“…In therapeutic development research, the conventional forward translation paradigm adopts a benchtop-to-bedside scheme, which aims to find genomic associations or therapeutic biomarkers starting from in vitro studies and animal models to validation in human subjects for further refinements and therapeutic development. On the other hand, reverse translation offers a different research paradigm that starts from human studies to identify and generate hypotheses for validation in animal or in vitro studies ( 1 ). This alternative research paradigm is poised to address several bottlenecks in conventional forward translation due to its more patient-centered, seamless, continuous, and cyclical process ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Pasquale

Girard

et al. 2023

Front. Ophthalmol.

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Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

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Translational Genomics in Neurocritical Care: a Review

Cited by 8 publications

References 158 publications

“Omics” in traumatic brain injury: novel approaches to a complex disease

“Omics” in traumatic brain injury: novel approaches to a complex disease

Neuroinflammation and Precision Medicine in Pediatric Neurocritical Care: Multi-Modal Monitoring of Immunometabolic Dysfunction

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

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