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Zhao K, Fonzo GA, Xie H, Oathes DJ, Keller CJ, Carlisle N, Etkin A, Garza-Villarreal EA, Zhang Y. A generalizable functional connectivity signature characterizes brain dysfunction and links to rTMS treatment response in cocaine use disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.21.23288948. [PMID: 37162878 PMCID: PMC10168499 DOI: 10.1101/2023.04.21.23288948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Cocaine use disorder (CUD) is a prevalent substance abuse disorder, and repetitive transcranial magnetic stimulation (rTMS) has shown potential in reducing cocaine cravings. However, a robust and replicable biomarker for CUD phenotyping is lacking, and the association between CUD brain phenotypes and treatment response remains unclear. Our study successfully established a cross-validated functional connectivity signature for accurate CUD phenotyping, using resting-state functional magnetic resonance imaging from a discovery cohort, and demonstrated its generalizability in an independent replication cohort. We identified phenotyping FCs involving increased connectivity between the visual network and dorsal attention network, and between the frontoparietal control network and ventral attention network, as well as decreased connectivity between the default mode network and limbic network in CUD patients compared to healthy controls. These abnormal connections correlated significantly with other drug use history and cognitive dysfunctions, e.g., non-planning impulsivity. We further confirmed the prognostic potential of the identified discriminative FCs for rTMS treatment response in CUD patients and found that the treatment-predictive FCs mainly involved the frontoparietal control and default mode networks. Our findings provide new insights into the neurobiological mechanisms of CUD and the association between CUD phenotypes and rTMS treatment response, offering promising targets for future therapeutic development.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Desmond J. Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Corey J. Keller
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Mexico
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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2
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Vogeser M, Bendt AK. From research cohorts to the patient - a role for "omics" in diagnostics and laboratory medicine? Clin Chem Lab Med 2023; 61:974-980. [PMID: 36592431 DOI: 10.1515/cclm-2022-1147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/16/2022] [Indexed: 01/03/2023]
Abstract
Human pathologies are complex and might benefit from a more holistic diagnostic approach than currently practiced. Omics is a concept in biological research that aims to comprehensively characterize and quantify large numbers of biological molecules in complex samples, e.g., proteins (proteomics), low molecular weight molecules (metabolomics), glycans (glycomics) or amphiphilic molecules (lipidomics). Over the past decades, respective unbiased discovery approaches have been intensively applied to investigate functional physiological and pathophysiological relationships in various research study cohorts. In the context of clinical diagnostics, omics approaches seem to have potential in two main areas: (i) biomarker discovery i.e. identification of individual marker analytes for subsequent translation into diagnostics (as classical target analyses with conventional laboratory techniques), and (ii) the readout of complex, higher-dimensional signatures of diagnostic samples, in particular by means of spectrometric techniques in combination with biomathematical approaches of pattern recognition and artificial intelligence for diagnostic classification. Resulting diagnostic methods could potentially represent a disruptive paradigm shift away from current one-dimensional (i.e., single analyte marker based) laboratory diagnostics. The underlying hypothesis of omics approaches for diagnostics is that complex, multigenic pathologies can be more accurately diagnosed via the readout of "omics-type signatures" than with the current one-dimensional single marker diagnostic procedures. While this is indeed promising, one must realize that the clinical translation of high-dimensional analytical procedures into routine diagnostics brings completely new challenges with respect to long-term reproducibility and analytical standardization, data management, and quality assurance. In this article, the conceivable opportunities and challenges of omics-based laboratory diagnostics are discussed.
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Affiliation(s)
- Michael Vogeser
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Anne K Bendt
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
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3
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Jain N, Nagaich U, Pandey M, Chellappan DK, Dua K. Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements. EPMA J 2022; 13:561-580. [PMID: 36505888 PMCID: PMC9727029 DOI: 10.1007/s13167-022-00304-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/01/2022] [Indexed: 11/15/2022]
Abstract
In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.
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Affiliation(s)
- Neha Jain
- Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University, Noida, 201303 UP India
| | - Upendra Nagaich
- Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University, Noida, 201303 UP India
| | - Manisha Pandey
- Department of Pharmaceutical Sciences, Central University of Haryana, Mahendergarh, 123031 India
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil 57000, Kuala Lumpur, Malaysia
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007 Australia
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007 Australia
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4
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A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma. Anal Bioanal Chem 2021; 413:4077-4090. [PMID: 33907864 DOI: 10.1007/s00216-021-03364-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
Improving the reliability of quantification in lipidomic analyses is crucial for its successful application in the discovery of new biomarkers or in clinical practice. In this study, we propose a workflow to improve the accuracy and precision of lipidomic results issued by the laboratory. Lipid species from 11 classes were analyzed by a targeted RPLC-MRM/MS method. The peak areas of species were used to estimate concentrations by an internal standard calibration approach (IS-calibration) and by an alternative normalization signal calibration schema (NS-calibration). The latter uses a long-term reference plasma material as a matrix-matched external calibrator whose accuracy was compared to the NIST SRM-1950 mean consensus values reported by the Interlaboratory Lipidomics Comparison Exercise. The bias of lipid concentrations showed a good accuracy for 69 of 89 quantified lipids. The quantitation of species by the NS-calibration schema improved the within- and between-batch reproducibility in quality control samples, in comparison to the usual IS-calibration approach. Moreover, the NS-calibration workflow improved the robustness of the lipidomics measurements reducing the between-batch variability (relative standard deviation <10% for 95% of lipid species) in real conditions tested throughout the analysis of 120 plasma samples. In addition, we provide a free access web tool to obtain the concentration of lipid species by the two previously mentioned quantitative approaches, providing an easy follow-up of quality control tasks related to lipidomics.
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Pan MM, Wang YF, Wang L, Yu X, Xu L. Recent advances in visual detection for cancer biomarkers and infectious pathogens. J Mater Chem B 2021; 9:35-52. [PMID: 33225338 DOI: 10.1039/d0tb01883j] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
It is an urgency to detect infectious pathogens or cancer biomarkers using rapid, simple, convenient and cost-effective methods in complex biological samples. Many existing approaches (traditional virus culture, ELISA or PCR) for the pathogen and biomarker assays face several challenges in the clinical applications that require lengthy time, sophisticated sample pre-treatment and expensive instruments. Due to the simple and rapid detection manner as well as no requirement of expensive equipment, many visual detection methods have been considered to resolve the aforementioned problems. Meanwhile, various new materials and colorimetric/fluorescent methods have been tried to construct new biosensors for infectious pathogens and biomarkers. However, the recent progress of these aspects is rarely reviewed, especially in terms of integration of new materials, microdevice and detection mechanism into the visual detection systems. Herein, we provide a broad field of view to discuss the recent progress in the visual detection of infectious pathogens and cancer biomarkers along with the detection mechanism, new materials, novel detection methods, special targets as well as multi-functional microdevices and systems. The novel visual approaches for the infectious pathogens and biomarkers, such as bioluminescence resonance energy transfer (BRET), metal-induced metallization and clustered regularly interspaced short palindromic repeats (CRISPR)-based biosensors, are discussed. Additionally, recent advancements in visual assays utilizing various new materials for proteins, nucleic acids, viruses, exosomes and small molecules are comprehensively reviewed. Future perspectives on the visual sensing systems for infectious pathogens and cancers are also proposed.
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Affiliation(s)
- Meng-Meng Pan
- Tongji School of Pharmacy, HuaZhong University of Science and Technology, Wuhan 430030, China.
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Abstract
A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.
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Affiliation(s)
- Brent M Kuenzi
- Division of Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
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7
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Bradshaw RA, Hondermarck H, Rodriguez H. Cancer Proteomics and the Elusive Diagnostic Biomarkers. Proteomics 2019; 19:e1800445. [PMID: 31172665 DOI: 10.1002/pmic.201800445] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/02/2019] [Indexed: 12/11/2022]
Abstract
Despite progress in genomic and proteomic technology and applications, the validation of cancer biomarkers of use as clinical early detection diagnostics has remained elusive. As described in this brief viewpoint, there are now recognized to be many types of clinical biomarkers and proteomic analyses, particularly when combined with other 'omic analyses, have been effective in many such biomarker identifications. However, in the area of early diagnosis of cancers, the problems associated with the conversion from identification to diagnostic have largely not been overcome. Notably, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI), has been particularly successful in refining the analytical steps needed to tackle this challenging issue and has provided positive insight into how to solve many of the underlying problems. The potential for developing clinical diagnostics for early detection of highly lethal cancers and possible new therapeutic strategies through proteomic analyses, as seen through these CPTAC successes, is more promising than ever.
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Affiliation(s)
- Ralph A Bradshaw
- Department of Physiology and Biophysics, University of California, Irvine, CA, 92697, USA.,Department of Pharmacology, University of California, San Diego, CA, 92093, USA
| | - Hubert Hondermarck
- School of Biomedical Sciences and Pharmacy, Hunter Medical Research Institute, University of Newcastle, Callaghan, NSW, 2308, Australia
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
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Lord SJ, St John A, Bossuyt PM, Sandberg S, Monaghan PJ, O'Kane M, Cobbaert CM, Röddiger R, Lennartz L, Gelfi C, Horvath AR. Setting clinical performance specifications to develop and evaluate biomarkers for clinical use. Ann Clin Biochem 2019; 56:527-535. [PMID: 30987429 DOI: 10.1177/0004563219842265] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Biomarker discovery studies often claim ‘promising’ findings, motivating further studies and marketing as medical tests. Unfortunately, the patient benefits promised are often inadequately explained to guide further evaluation, and few biomarkers have translated to improved patient care. We present a practical guide for setting minimum clinical performance specifications to strengthen clinical performance study design and interpretation. Methods We developed a step-by-step approach using test evaluation and decision-analytic frameworks and present with illustrative examples. Results We define clinical performance specifications as a set of criteria that quantify the clinical performance a new test must attain to allow better health outcomes than current practice. We classify the proposed patient benefits of a new test into three broad groups and describe how to set minimum clinical performance at the level where the potential harm of false-positive and false-negative results does not outweigh the benefits. (1) For add-on tests proposed to improve disease outcomes by improving detection, define an acceptable trade-off for false-positive versus true-positive results; (2) for triage tests proposed to reduce unnecessary tests and treatment by ruling out disease, define an acceptable risk of false-negatives as a safety threshold; (3) for replacement tests proposed to provide other benefits, or reduce costs, without compromising accuracy, use existing tests to benchmark minimum accuracy levels. Conclusions Researchers can follow these guidelines to focus their study objectives and to define statistical hypotheses and sample size requirements. This way, clinical performance studies will allow conclusions about whether test performance is sufficient for intended use.
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Affiliation(s)
- Sarah J Lord
- 1 School of Medicine, University of Notre Dame, Darlinghurst, New South Wales, Australia.,2 National Health and Medical Research Council (NHMRC) Clinical Trials Centre, University of Sydney, Sydney, Australia
| | | | - Patrick Mm Bossuyt
- 4 Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Sverre Sandberg
- 5 Department of Global Public Health and Primary Health Care, University of Bergen, Norway.,6 The Norwegian Quality Improvement of Primary Care Laboratories (NOKLUS), Haraldsplass Deaconess Hospital, Bergen, Norway.,7 Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway
| | - Phillip J Monaghan
- 8 Department of Clinical Biochemistry, The Christie Pathology Partnership, The Christie NHS Foundation Trust, Manchester, UK
| | - Maurice O'Kane
- 9 Clinical Chemistry Department, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, UK
| | - Christa M Cobbaert
- 10 Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Ralf Röddiger
- 9 Clinical Chemistry Department, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, UK.,11 Clinical Operations, Global Medical and Scientific Affairs, Roche Diagnostics GmbH, Mannheim, Germany
| | | | - Cecilia Gelfi
- 13 Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
| | - Andrea R Horvath
- 14 Department of Clinical Chemistry & Endocrinology, Prince of Wales Hospital, New South Wales Health Pathology and School of Medical Sciences, University of New South Wales, Randwick, Australia.,15 School of Public Health, University of Sydney, Camperdown, Australia
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9
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Monaghan PJ, Bossuyt PM. Test evaluation: major challengesahead - Opportunities abound. Ann Clin Biochem 2019; 56:524-526. [PMID: 30971105 DOI: 10.1177/0004563219837301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Phillip J Monaghan
- 1 The Christie Pathology Partnership, The Christie NHS Foundation Trust, Manchester, UK.,2 Faculty of Medical and Human Sciences, Institute of Inflammation and Repair, University of Manchester, Manchester, UK
| | - Patrick Mm Bossuyt
- 3 Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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