After another decade: LC–MS/MS became routine in clinical diagnostics

Clinical Biochemistry, Journal Year: 2020, Volume and Issue: 82, P. 2 - 11

Published: March 15, 2020

Tandem mass spectrometry – especially in combination with liquid chromatography (LC–MS/MS) is applied a multitude of important diagnostic niches laboratory medicine. It unquestioned its routine use and often unreplaceable by alternative technologies. This overview illustrates the development past decade (2009–2019) intends to provide insight into current standing future directions field. The instrumentation matured significantly, applications are well understood, vitro diagnostics (IVD) industry shaping market providing assay kits, certified instruments, first automated LC–MS/MS instruments as an analytical core. In many settings application still burdensome locally lab developed test (LDT) designs relying on highly specialized staff. cover wide range analytes therapeutic drug monitoring, endocrinology including newborn screening, toxicology. tasks that remain be mastered are, for example, quantification proteins means transition from targeted untargeted omics approaches pattern recognition/pattern discrimination key technology establishment decisions.

Language: Английский

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A roadmap for multi-omics data integration using deep learning

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 23(1)

Published: Oct. 7, 2021

Abstract High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These have revolutionized biomedical research by providing more comprehensive understanding the biological systems and molecular mechanisms disease development. Recently, deep learning (DL) algorithms become one most promising methods in analysis, due their predictive performance capability capturing nonlinear hierarchical features. While integrating translating into useful functional insights remain biggest bottleneck, there is clear trend towards incorporating analysis help explain complex relationships between layers. Multi-omics role improve prevention, early detection prediction; monitor progression; interpret patterns endotyping; design personalized treatments. In this review, we outline roadmap integration using DL offer practical perspective advantages, challenges barriers implementation data.

Language: Английский

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Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Nature Protocols, Journal Year: 2021, Volume and Issue: 16(8), P. 3737 - 3760

Published: July 9, 2021

Language: Английский

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Applications of multi‐omics analysis in human diseases

MedComm, Journal Year: 2023, Volume and Issue: 4(4)

Published: July 31, 2023

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell proteomics and metabolomics, spatial so on, which play a great role in promoting study human diseases. Most current reviews focus on describing development multi-omics technologies, data integration, particular disease; however, few them provide comprehensive systematic introduction multi-omics. This review outlines existing technical categories multi-omics, cautions for experimental design, focuses integrated analysis methods especially approach machine learning deep integration corresponding tools, medical researches (e.g., cancer, neurodegenerative diseases, aging, drug target discovery) as well open-source tools databases, finally, discusses challenges future directions precision medicine. With algorithms, important disease research, also provided detailed introduction. will guidance researchers, who are just entering into research.

Language: Английский

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Long-term cancer survival prediction using multimodal deep learning

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: June 29, 2021

Abstract The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal deep learning method for long-term pan-cancer survival prediction. MultiSurv uses dedicated submodels establish feature representations clinical, imaging, and different omics data modalities. A fusion layer aggregates the representations, prediction submodel generates conditional probabilities follow-up time intervals spanning several decades. is first non-linear non-proportional that leverages In addition, can missing data, including single values complete was applied from 33 cancer types yields accurate curves. quantitative comparison with previous methods showed Multisurv achieves best results according time-dependent metrics. also generated visualizations learned representation which revealed insights on characteristics heterogeneity.

Language: Английский

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A guide to multi-omics data collection and integration for translational medicine

Computational and Structural Biotechnology Journal, Journal Year: 2022, Volume and Issue: 21, P. 134 - 149

Published: Dec. 1, 2022

The emerging high-throughput technologies have led to the shift in design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, complexity integrating these datasets has triggered new questions regarding appropriateness available computational methods. Currently, there is no clear consensus on best combination omics include and data integration methodologies required for This article aims guide studies field types method choose. We review articles that perform multiple measurements from samples. identify five objectives applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, (v) understand regulatory processes. describe common trends selection omic combined different diseases. To choice tools, we group them into scientific they aim address. main methods adopted achieve present examples tools. compare tools based how deal with challenges comment against predefined objective-specific evaluation criteria. Finally, discuss downstream analysis further extraction novel insights datasets.

Language: Английский

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The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(13), P. 8297 - 8346

Published: June 15, 2023

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection integration clinical information enable a new era of healthcare. In this comprehensive review, we highlight utility Raman spectroscopy (RS) as an emerging technology for clinically relevant applications using significant samples models. We discuss use RS both label-free approach probing intrinsic metabolites biological materials, labeled where signal from reporters conjugated nanoparticles (NPs) serve indirect measure tracking protein biomarkers

Language: Английский

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The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Biosensors and Bioelectronics, Journal Year: 2023, Volume and Issue: 235, P. 115387 - 115387

Published: May 11, 2023

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to fore low-cost wearable/portable smart devices. Although numerous devices that track digital biomarkers been successfully translated from bench-to-bedside, only a few follow same fate when it comes traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted clinical setting as they require trained personnel specialized equipment. In fact, real-time, passive/active robust sensing physiological behavioural data patients can feed artificial intelligence (AI)-based models significantly improve decision-making, diagnosis treatment at point-of-procedure, circumventing conventional methods sampling, person investigation expert pathologists, who are scarce developing countries. This review brings together biomarker through portable autonomous miniaturized We first summarise technological advances each field vs current we conclude merging two worlds AI/ML technologies patient treatment. The fundamental role, limitations prospects AI realizing this potential enhancing existing facilitate development translation "point-of-care" (POC) diagnostics is finally showcased.

Language: Английский

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Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 4, 2023

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, treatment optimization. Prominent AI technologies such as machine learning (ML) deep (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, telemedicine. Significant innovations improvements in identification early intervention been made using AI-generated algorithms for clinical decision support systems prediction models. remarkably impacted trials by amplifying research into efficacy, adverse events, candidate molecular design. AI's precision analysis regarding patients' genetic, environmental, lifestyle factors led individualized strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent remote delivery follow-up. Moreover, applications wearable devices allowed ambulatory monitoring of vital signs. However, apart from being transformative, contribution is subject ethical regulatory concerns. AI-backed data protection algorithm transparency should be strictly adherent principles. Vigorous governance frameworks place before incorporating mental health interventions through AI-operated chatbots, education enhancements, virtual reality-based training. The role decision-making certain limitations, necessitating importance hands-on experience. Therefore, reaching an optimal balance between capabilities considerations ensure impartial neutral performance crucial. This narrative review focuses on impact balanced incorporation make use its full potential.

Language: Английский

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Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

Pain, Journal Year: 2023, Volume and Issue: 164(9), P. 1912 - 1926

Published: June 15, 2023

Abstract Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying development of chronic poorly understood. Pain biomarkers could potentially identify and measure biological pathways phenotypical expressions that are altered by pain, provide insight into treatment targets, help at-risk patients who might benefit from early intervention. Biomarkers used to diagnose, track, treat other diseases, but no validated clinical exist yet for pain. To address this problem, National Institutes Health Common Fund launched Acute Signatures (A2CPS) program evaluate candidate biomarkers, develop them biosignatures, discover novel chronification after surgery. This article discusses identified A2CPS evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, behavioral measures. will most comprehensive investigation transition postsurgical undertaken date. Data analytic resources generatedby be shared with scientific community hopes investigators extract valuable insights beyond A2CPS's initial findings. review rationale them, current state science on acute gaps literature, how these gaps.

Language: Английский

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