Plasma metabolomic profiles enhance precision medicine for volunteers of normal health

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

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The Gut Microbiome: Reaching the Promise Through Discovery— Advancing Knowledge and Discovery of the Gut Microbiome in the Age of Precision Medicine

Gastroenterology ◽

10.1053/j.gastro.2020.12.035 ◽

2020 ◽

Author(s):

Beth A. McCormick ◽

Eugene B. Chang

Keyword(s):

Precision Medicine ◽

Gut Microbiome

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Exploring the Applications of Machine Learning in Healthcare

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191220103417 ◽

2020 ◽

Vol 10 (4) ◽

pp. 458-472

Author(s):

Tausifa Jan Saleem ◽

Mohammad Ahsan Chishti

Keyword(s):

Machine Learning ◽

Disease Risk ◽

Disease Diagnosis ◽

Machine Intelligence ◽

Healthcare Applications ◽

Comprehensive Overview ◽

Machine Learning Applications ◽

Remote Healthcare ◽

Healthcare Monitoring ◽

Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Cancer Cell International ◽

10.1186/s12935-021-01981-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Muhammad Javed Iqbal ◽

Zeeshan Javed ◽

Haleema Sadia ◽

Ijaz A. Qureshi ◽

Asma Irshad ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cancer Diagnosis ◽

Disease Risk ◽

Clinical Applications ◽

Optimal Decision ◽

Great Promise ◽

Time Range ◽

Base System ◽

The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

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Precision Medicine in Neurodegenerative Diseases: Some Promising Tips Coming from the microRNAs’ World

Cells ◽

10.3390/cells9010075 ◽

2019 ◽

Vol 9 (1) ◽

pp. 75 ◽

Cited By ~ 3

Author(s):

Nicoletta Nuzziello ◽

Loredana Ciaccia ◽

Maria Liguori

Keyword(s):

Neurodegenerative Diseases ◽

Precision Medicine ◽

Target Genes ◽

Drug Disposition ◽

Therapeutic Potential ◽

Response To Treatment ◽

Therapeutic Effects ◽

Exciting Potential ◽

The Central Nervous System ◽

Effective Use

Novel insights in the development of a precision medicine approach for treating the neurodegenerative diseases (NDDs) are provided by emerging advances in the field of pharmacoepigenomics. In this context, microRNAs (miRNAs) have been extensively studied because of their implication in several disorders related to the central nervous system, as well as for their potential role as biomarkers of diagnosis, prognosis, and response to treatment. Recent studies in the field of neurodegeneration reported evidence that drug response and efficacy can be modulated by miRNA-mediated mechanisms. In fact, miRNAs seem to regulate the expression of pharmacology target genes, while approved (conventional and non-conventional) therapies can restore altered miRNAs observed in NDDs. The knowledge of miRNA pharmacoepigenomics may offers new clues to develop more effective treatments by providing novel insights into interindividual variability in drug disposition and response. Recently, the therapeutic potential of miRNAs is gaining increasing attention, and miRNA-based drugs (for cancer) have been under observation in clinical trials. However, the effective use of miRNAs as therapeutic target still needs to be investigated. Here, we report a brief review of representative studies in which miRNAs related to therapeutic effects have been investigated in NDDs, providing exciting potential prospects of miRNAs in pharmacoepigenomics and translational medicine.

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Engineering Safer Psychedelics for Treating Addiction

Neuroscience Insights ◽

10.1177/26331055211033847 ◽

2021 ◽

Vol 16 ◽

pp. 263310552110338

Author(s):

Jamie Peters ◽

David E Olson

Keyword(s):

Executive Control ◽

Neural Circuitry ◽

Opioid Addiction ◽

Adult Brain ◽

Great Promise ◽

Therapeutic Effects ◽

Preclinical Models ◽

Drug Seeking ◽

Reward Circuitry

Addiction is best described as a disorder of maladaptive neuroplasticity involving the simultaneous strengthening of reward circuitry that drives compulsive drug seeking and weakening of circuits involved in executive control over harmful behaviors. Psychedelics have shown great promise for treating addiction, with many people attributing their therapeutic effects to insights gained while under the influence of the drug. However, psychedelics are also potent psychoplastogens—molecules capable of rapidly re-wiring the adult brain. The advent of non-hallucinogenic psychoplastogens with anti-addictive properties raises the intriguing possibility that hallucinations might not be necessary for all therapeutic effects of psychedelic-based medicines, so long as the underlying pathological neural circuitry can be remedied. One of these non-hallucinogenic psychoplastogens, tabernanthalog (TBG), appears to have long-lasting therapeutic effects in preclinical models relevant to alcohol and opioid addiction. Here, we discuss the implications of these results for the development of addiction treatments, as well as the next steps for advancing TBG and related non-hallucinogenic psychoplastogens as addiction therapeutics.

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Methodology and Applications of Disease Biomarker Identification in Human Serum

Biomarker Insights ◽

10.1177/117727190700200034 ◽

2007 ◽

Vol 2 ◽

pp. 117727190700200 ◽

Cited By ~ 54

Author(s):

Ziad J. Sahab ◽

Suzan M. Semaan ◽

Qing-Xiang Amy Sang

Keyword(s):

Human Serum ◽

Protein Separation ◽

High Sensitivity ◽

Disease Diagnosis ◽

Plasma Lipoproteins ◽

Great Promise ◽

Protein Biomarkers ◽

Serum Samples ◽

Disease Biomarkers ◽

Diagnosis And Prognosis

Biomarkers are biomolecules that serve as indicators of biological and pathological processes, or physiological and pharmacological responses to a drug treatment. Because of the high abundance of albumin and heterogeneity of plasma lipoproteins and glycoproteins, biomarkers are difficult to identify in human serum. Due to the clinical significance the identification of disease biomarkers in serum holds great promise for personalized medicine, especially for disease diagnosis and prognosis. This review summarizes some common and emerging proteomics techniques utilized in the separation of serum samples and identification of disease signatures. The practical application of each protein separation or identification technique is analyzed using specific examples. Biomarkers of cancers of prostate, breast, ovary, and lung in human serum have been reviewed, as well as those of heart disease, arthritis, asthma, and cystic fibrosis. Despite the advancement of technology few biomarkers have been approved by the Food and Drug Administration for disease diagnosis and prognosis due to the complexity of structure and function of protein biomarkers and lack of high sensitivity, specificity, and reproducibility for those putative biomarkers. The combination of different types of technologies and statistical analysis may provide more effective methods to identify and validate new disease biomarkers in blood.

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Machine Learning, Low-Rank Models, and Their Application to Disease Risk Prediction and Precision Medicine

Big Data in Omics and Imaging ◽

10.1201/9781315370507-8 ◽

2017 ◽

pp. 531-643

Author(s):

Momiao Xiong

Keyword(s):

Machine Learning ◽

Precision Medicine ◽

Risk Prediction ◽

Disease Risk ◽

Low Rank ◽

Low Rank Models

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Molecular Pathology: A Paradigm Shift towards Precision Medicine

Pakistan Journal of Medicine and Dentistry ◽

10.36283/pjmd10-1/001 ◽

2021 ◽

Keyword(s):

Precision Medicine ◽

Human Genome ◽

Molecular Pathology ◽

Human Genome Project ◽

Paradigm Shift ◽

Disease Diagnosis ◽

Genome Project ◽

Early Disease ◽

P4 Medicine ◽

Drug Designing

Healthcare system is experiencing a paradigm shift to precision medicine. Genotypic–phenotypic affiliation has been found to be a fundamental percept in biology after the completion of Human Genome project. The first era of precision medicine is now split into groups and subgroups, making it a meaningful strategy concurrently throughout the clinical phases of drug designing and development. It likewise recommends healthcare reshaping that suggests disease perceptivity or remedial treatment. Thus, translational genomics addresses bench to bedside approach to achieve P4 medicine (personalized, predictive, preventive, and participatory), i.e., early disease diagnosis and specifically designed treating plans instead of one size fits all1.

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The gut microbiome in sickle cell disease: Characterization and potential implications

PLoS ONE ◽

10.1371/journal.pone.0255956 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255956

Author(s):

Hassan Brim ◽

James Taylor ◽

Muneer Abbas ◽

Kimberly Vilmenay ◽

Mohammad Daremipouran ◽

...

Keyword(s):

Sickle Cell Disease ◽

Sickle Cell ◽

Gut Microbiome ◽

Tissue Injury ◽

Organ Damage ◽

Composition Analysis ◽

Cell Disease ◽

Blood Disorder ◽

Major Player ◽

Next Generation Sequencing Ngs

Background Sickle Cell Disease (SCD) is an inherited blood disorder that leads to hemolytic anemia, pain, organ damage and early mortality. It is characterized by polymerized deoxygenated hemoglobin, rigid sickle red blood cells and vaso-occlusive crises (VOC). Recurrent hypoxia-reperfusion injury in the gut of SCD patients could increase tissue injury, permeability, and bacterial translocation. In this context, the gut microbiome, a major player in health and disease, might have significant impact. This study sought to characterize the gut microbiome in SCD. Methods Stool and saliva samples were collected from healthy controls (n = 14) and SCD subjects (n = 14). Stool samples were also collected from humanized SCD murine models including Berk, Townes and corresponding control mice. Amplified 16S rDNA was used for bacterial composition analysis using Next Generation Sequencing (NGS). Pairwise group analyses established differential bacterial groups at many taxonomy levels. Bacterial group abundance and differentials were established using DeSeq software. Results A major dysbiosis was observed in SCD patients. The Firmicutes/Bacteroidetes ratio was lower in these patients. The following bacterial families were more abundant in SCD patients: Acetobacteraceae, Acidaminococcaceae, Candidatus Saccharibacteria, Peptostreptococcaceae, Bifidobacteriaceae, Veillonellaceae, Actinomycetaceae, Clostridiales, Bacteroidacbactereae and Fusobacteriaceae. This dysbiosis translated into 420 different operational taxonomic units (OTUs). Townes SCD mice also displayed gut microbiome dysbiosis as seen in human SCD. Conclusion A major dysbiosis was observed in SCD patients for bacteria that are known strong pro-inflammatory triggers. The Townes mouse showed dysbiosis as well and might serve as a good model to study gut microbiome modulation and its impact on SCD pathophysiology.

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