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Coskun A, Ertaylan G, Pusparum M, Van Hoof R, Kaya ZZ, Khosravi A, Zarrabi A. Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167339. [PMID: 38986819 DOI: 10.1016/j.bbadis.2024.167339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/25/2024] [Accepted: 07/03/2024] [Indexed: 07/12/2024]
Abstract
Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.
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Affiliation(s)
- Abdurrahman Coskun
- Acibadem University, School of Medicine, Department of Medical Biochemistry, Istanbul, Turkey.
| | - Gökhan Ertaylan
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
| | - Murih Pusparum
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium; I-Biostat, Data Science Institute, Hasselt University, Hasselt 3500, Belgium
| | - Rebekka Van Hoof
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
| | - Zelal Zuhal Kaya
- Nisantasi University, School of Medicine, Department of Medical Biochemistry, Istanbul, Turkey
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Turkey
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Turkey; Graduate School of Biotehnology and Bioengeneering, Yuan Ze University, Taoyuan 320315, Taiwan; Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600 077, India
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2
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Koch E, Pardiñas AF, O'Connell KS, Selvaggi P, Camacho Collados J, Babic A, Marshall SE, Van der Eycken E, Angulo C, Lu Y, Sullivan PF, Dale AM, Molden E, Posthuma D, White N, Schubert A, Djurovic S, Heimer H, Stefánsson H, Stefánsson K, Werge T, Sønderby I, O'Donovan MC, Walters JTR, Milani L, Andreassen OA. How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry. Biol Psychiatry 2024; 96:543-551. [PMID: 38185234 DOI: 10.1016/j.biopsych.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.
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Affiliation(s)
- Elise Koch
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - José Camacho Collados
- CardiffNLP, School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | | | - Erik Van der Eycken
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Cecilia Angulo
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California; Departments of Radiology, Psychiatry, and Neurosciences, University of California, San Diego, La Jolla, California
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nathan White
- CorTechs Laboratories, Inc., San Diego, California
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; The Norwegian Centre for Mental Disorders Research Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hakon Heimer
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Nordic Society of Human Genetics and Precision Medicine, Copenhagen, Denmark
| | | | | | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark; Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Ida Sønderby
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
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Ghosh S, Bhaskar R, Mishra R, Arockia Babu M, Abomughaid MM, Jha NK, Sinha JK. Neurological insights into brain-targeted cancer therapy and bioinspired microrobots. Drug Discov Today 2024; 29:104105. [PMID: 39029869 DOI: 10.1016/j.drudis.2024.104105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/03/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024]
Abstract
Cancer, a multifaceted and pernicious disease, continuously challenges medicine, requiring innovative treatments. Brain cancers pose unique and daunting challenges due to the intricacies of the central nervous system and the blood-brain barrier. In this era of precision medicine, the convergence of neurology, oncology, and cutting-edge technology has given birth to a promising avenue - targeted cancer therapy. Furthermore, bioinspired microrobots have emerged as an ingenious approach to drug delivery, enabling precision and control in cancer treatment. This Keynote review explores the intricate web of neurological insights into brain-targeted cancer therapy and the paradigm-shifting world of bioinspired microrobots. It serves as a critical and comprehensive overview of these evolving fields, aiming to underscore their integration and potential for revolutionary cancer treatments.
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Affiliation(s)
- Shampa Ghosh
- GloNeuro, Sector 107, Vishwakarma Road, Noida, Uttar Pradesh 201301, India
| | - Rakesh Bhaskar
- School of Chemical Engineering, Yeungnam University, Gyeonsang 38541, Republic of Korea; Research Institute of Cell Culture, Yeungnam University, Gyeonsang 38541, Republic of Korea
| | - Richa Mishra
- Department of Computer Science and Engineering, Parul University, Vadodara, Gujrat 391760, India
| | - M Arockia Babu
- Institute of Pharmaceutical Research, GLA University, Mathura, India
| | - Mosleh Mohammad Abomughaid
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Bisha, Bisha 61922, Saudi Arabia
| | - Niraj Kumar Jha
- Centre of Research Impact and Outcome, Chitkara University, Rajpura 140401, Punjab, India; Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India; School of Bioengineering & Biosciences, Lovely Professional University, Phagwara 144411, India; Department of Biotechnology Engineering and Food Technology, Chandigarh University, Mohali, India.
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Zhang Q, Cai Z, Gerratana L, Davis AA, D'Amico P, Chawla A, Jacob S, Zhang Y, Jiao J, Qin W, Reduzzi C, Flaum L, Shah A, Gradishar WJ. Early Evaluation of Risk Stratification and Clinical Outcomes for Patients with Advanced Breast Cancer through Combined Monitoring of Baseline Circulating Tumor Cells and DNA. Clin Cancer Res 2024; 30:3470-3480. [PMID: 38829582 DOI: 10.1158/1078-0432.ccr-24-0535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 04/19/2024] [Accepted: 05/31/2024] [Indexed: 06/05/2024]
Abstract
PURPOSE Early evaluation of tumor heterogeneity related to metastasis and outcomes is a major challenge in the management of advanced breast cancer (BCa) in the clinic. In this study, we introduced the value of baseline circulating tumor cells (CTC) and ctDNA for early differentiation of clinical stages, tumor heterogeneity, and prognosis in clinic. EXPERIMENTAL DESIGN A total of 292 patients with BCa were enrolled in this study, including 254 Stage IV and 38 Stage III patients, and examined the baseline levels of CTCs, CTC-clusters, and plasma ctDNA before initiating therapies. Outcomes including progression-free survival (PFS) and overall survival were evaluated using proportional hazards regression analysis. RESULTS The baseline CTCs, including HER2+ CTCs, in Stage IV patients were approximately 9.5 times higher than those detected in Stage III patients. Baseline CTC counts with a cutoff of 5 were significantly associated with the prognosis. Within each stage, patients with <5 CTCs had significantly longer PFS. Stage III patients with no CTCs exhibited the longest survival compared with patients with ≥1 CTC. CTC-clusters were only found in Stage IV patients, among whom 15 Stage IV patients with ≥5 CTC-clusters had the worst PFS compared with the 239 Stage IV patients with <5 CTC-clusters. Similar outcomes were observed in 28 out of 254 Stage IV patients who had at least one CTC-cluster detected, as these patients had shorter PFS compared with CTC-cluster negative group. The major differences in ctDNA mutations between patients with Stage III and Stage IV BCa were in PIK3CA and ESR1, which were associated with specific organ metastasis and worse outcomes. CONCLUSIONS Assessing the baseline levels of CTCs, CTC-clusters, and mutational ctDNA profile could reliably aid in differentiation of clinical stage and early prediction of metastasis and outcomes in advanced BCa.
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Affiliation(s)
- Qiang Zhang
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Circulating Tumor Cell (CTC) Core Facility, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois
| | - Zheng Cai
- Biostatistics Department, University of Washington, Seattle, Washington
| | - Lorenzo Gerratana
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
| | - Andrew A Davis
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | | | - Akhil Chawla
- Division of Surgical Oncology, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Saya Jacob
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Youbin Zhang
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Circulating Tumor Cell (CTC) Core Facility, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois
| | - Jianhua Jiao
- Department of Urology, Xijing Hospital, Innovation Center for Tumor Immunocytology Therapy Technology, Xijing Innovation Research Institute, Fourth Military Medical University, Xi'an, China
| | - Weijun Qin
- Department of Urology, Xijing Hospital, Innovation Center for Tumor Immunocytology Therapy Technology, Xijing Innovation Research Institute, Fourth Military Medical University, Xi'an, China
| | - Carolina Reduzzi
- Division of Hematology and Medical Oncology, Weill Department of Medicine (WDOM), Cornell University, New York, New York
| | - Lisa Flaum
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ami Shah
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Circulating Tumor Cell (CTC) Core Facility, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois
| | - William J Gradishar
- Division of Hematology/Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Circulating Tumor Cell (CTC) Core Facility, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois
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Xie W, Sharma A, Kaushik H, Sharma L, Nistha, Anwer MK, Sachdeva M, Elossaily GM, Zhang Y, Pillappan R, Kaur M, Behl T, Shen B, Singla RK. Shaping the future of gastrointestinal cancers through metabolic interactions with host gut microbiota. Heliyon 2024; 10:e35336. [PMID: 39170494 PMCID: PMC11336605 DOI: 10.1016/j.heliyon.2024.e35336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/19/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024] Open
Abstract
Gastrointestinal (GI) cancers represent a significant global health challenge, driving relentless efforts to identify innovative diagnostic and therapeutic approaches. Recent strides in microbiome research have unveiled a previously underestimated dimension of cancer progression that revolves around the intricate metabolic interplay between GI cancers and the host's gut microbiota. This review aims to provide a comprehensive overview of these emerging metabolic interactions and their potential to catalyze a paradigm shift in precision diagnosis and therapeutic breakthroughs in GI cancers. The article underscores the groundbreaking impact of microbiome research on oncology by delving into the symbiotic connection between host metabolism and the gut microbiota. It offers valuable insights into tailoring treatment strategies to individual patients, thus moving beyond the traditional one-size-fits-all approach. This review also sheds light on novel diagnostic methodologies that could transform the early detection of GI cancers, potentially leading to more favorable patient outcomes. In conclusion, exploring the metabolic interactions between host gut microbiota and GI cancers showcases a promising frontier in the ongoing battle against these formidable diseases. By comprehending and harnessing the microbiome's influence, the future of precision diagnosis and therapeutic innovation for GI cancers appears more optimistic, opening doors to tailored treatments and enhanced diagnostic precision.
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Affiliation(s)
- Wen Xie
- Department of Pharmacy and Institutes for Systems Genetics, Center for High Altitude Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Aditi Sharma
- School of Pharmaceutical Sciences, Shoolini University, Solan, H.P, 173229, India
| | - Hitesh Kaushik
- School of Pharmaceutical Sciences, Shoolini University, Solan, H.P, 173229, India
| | - Lalit Sharma
- School of Pharmaceutical Sciences, Shoolini University, Solan, H.P, 173229, India
| | - Nistha
- School of Pharmaceutical Sciences, Shoolini University, Solan, H.P, 173229, India
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Monika Sachdeva
- Fatima College of Health Sciences, Al Ain, United Arab Emirates
| | - Gehan M. Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, P.O. Box 71666, Riyadh, 11597, Saudi Arabia
| | - Yingbo Zhang
- Institutes for Systems Genetics, West China Tianfu Hospital, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610218, China
| | - Ramkumar Pillappan
- Nitte (Deemed to be University), NGSM Institute of Pharmaceutical Sciences, Mangaluru, Karnataka, India
| | - Maninderjit Kaur
- Department of Pharmaceutical Sciences, lovely Professional University, Phagwara, India
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Sahibzada Ajit Singh Nagar, Punjab, India
| | - Bairong Shen
- Department of Pharmacy and Institutes for Systems Genetics, Center for High Altitude Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Rajeev K. Singla
- Department of Pharmacy and Institutes for Systems Genetics, Center for High Altitude Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab, 1444411, India
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Grasso-Cladera A, Bremer M, Ladouce S, Parada F. A systematic review of mobile brain/body imaging studies using the P300 event-related potentials to investigate cognition beyond the laboratory. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:631-659. [PMID: 38834886 DOI: 10.3758/s13415-024-01190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2024] [Indexed: 06/06/2024]
Abstract
The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.
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Affiliation(s)
| | - Marko Bremer
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile
- Facultad de Psicología, Programa de Magíster en Neurociencia Social, Diego Portales University, Santiago, Chile
| | - Simon Ladouce
- Department Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Francisco Parada
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile.
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Barrett JS. Artificial Intelligence Opportunities to Guide Precision Dosing Strategies. J Pediatr Pharmacol Ther 2024; 29:434-440. [PMID: 39144390 PMCID: PMC11321806 DOI: 10.5863/1551-6776-29.4.434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 03/12/2024] [Indexed: 08/16/2024]
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Matus P, Sepúlveda-Peñaloza A, Page K, Rodríguez C, Cárcamo M, Bustamante F, Garrido M, Urquidi C. The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform. Front Public Health 2024; 12:1407514. [PMID: 39114513 PMCID: PMC11303229 DOI: 10.3389/fpubh.2024.1407514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.
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Affiliation(s)
| | | | | | | | | | | | | | - Cinthya Urquidi
- Department of Epidemiology and Health Studies, Medicine Faculty, Universidad de los Andes, Santiago, Chile
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Fang Z, Raza U, Song J, Lu J, Yao S, Liu X, Zhang W, Li S. Systemic aging fuels heart failure: Molecular mechanisms and therapeutic avenues. ESC Heart Fail 2024. [PMID: 39034866 DOI: 10.1002/ehf2.14947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/29/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024] Open
Abstract
Systemic aging influences various physiological processes and contributes to structural and functional decline in cardiac tissue. These alterations include an increased incidence of left ventricular hypertrophy, a decline in left ventricular diastolic function, left atrial dilation, atrial fibrillation, myocardial fibrosis and cardiac amyloidosis, elevating susceptibility to chronic heart failure (HF) in the elderly. Age-related cardiac dysfunction stems from prolonged exposure to genomic, epigenetic, oxidative, autophagic, inflammatory and regenerative stresses, along with the accumulation of senescent cells. Concurrently, age-related structural and functional changes in the vascular system, attributed to endothelial dysfunction, arterial stiffness, impaired angiogenesis, oxidative stress and inflammation, impose additional strain on the heart. Dysregulated mechanosignalling and impaired nitric oxide signalling play critical roles in the age-related vascular dysfunction associated with HF. Metabolic aging drives intricate shifts in glucose and lipid metabolism, leading to insulin resistance, mitochondrial dysfunction and lipid accumulation within cardiomyocytes. These alterations contribute to cardiac hypertrophy, fibrosis and impaired contractility, ultimately propelling HF. Systemic low-grade chronic inflammation, in conjunction with the senescence-associated secretory phenotype, aggravates cardiac dysfunction with age by promoting immune cell infiltration into the myocardium, fostering HF. This is further exacerbated by age-related comorbidities like coronary artery disease (CAD), atherosclerosis, hypertension, obesity, diabetes and chronic kidney disease (CKD). CAD and atherosclerosis induce myocardial ischaemia and adverse remodelling, while hypertension contributes to cardiac hypertrophy and fibrosis. Obesity-associated insulin resistance, inflammation and dyslipidaemia create a profibrotic cardiac environment, whereas diabetes-related metabolic disturbances further impair cardiac function. CKD-related fluid overload, electrolyte imbalances and uraemic toxins exacerbate HF through systemic inflammation and neurohormonal renin-angiotensin-aldosterone system (RAAS) activation. Recognizing aging as a modifiable process has opened avenues to target systemic aging in HF through both lifestyle interventions and therapeutics. Exercise, known for its antioxidant effects, can partly reverse pathological cardiac remodelling in the elderly by countering processes linked to age-related chronic HF, such as mitochondrial dysfunction, inflammation, senescence and declining cardiomyocyte regeneration. Dietary interventions such as plant-based and ketogenic diets, caloric restriction and macronutrient supplementation are instrumental in maintaining energy balance, reducing adiposity and addressing micronutrient and macronutrient imbalances associated with age-related HF. Therapeutic advancements targeting systemic aging in HF are underway. Key approaches include senomorphics and senolytics to limit senescence, antioxidants targeting mitochondrial stress, anti-inflammatory drugs like interleukin (IL)-1β inhibitors, metabolic rejuvenators such as nicotinamide riboside, resveratrol and sirtuin (SIRT) activators and autophagy enhancers like metformin and sodium-glucose cotransporter 2 (SGLT2) inhibitors, all of which offer potential for preserving cardiac function and alleviating the age-related HF burden.
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Affiliation(s)
- Zhuyubing Fang
- Cardiovascular Department of Internal Medicine, Karamay Hospital of People's Hospital of Xinjiang Uygur Autonomous Region, Karamay, Xinjiang Uygur Autonomous Region, China
| | - Umar Raza
- School of Basic Medical Sciences, Shenzhen University, Shenzhen, Guangdong Province, China
| | - Jia Song
- Department of Medicine (Cardiovascular Research), Baylor College of Medicine, Houston, Texas, USA
| | - Junyan Lu
- Department of Cardiology, Zengcheng Branch of Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Xiaohong Liu
- Cardiovascular Department of Internal Medicine, Karamay Hospital of People's Hospital of Xinjiang Uygur Autonomous Region, Karamay, Xinjiang Uygur Autonomous Region, China
| | - Wei Zhang
- Outpatient Clinic of Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Shujuan Li
- Department of Pediatric Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
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Fu ES, Berkel C, Merle JL, St George SM, Graham AK, Smith JD. A Scoping Review of Tailoring in Pediatric Obesity Interventions. Child Obes 2024. [PMID: 39008426 DOI: 10.1089/chi.2024.0214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Background: Families with children who have or are at risk for obesity have differing needs and a one-size-fits-all approach can negatively impact program retention, engagement, and outcomes. Individually tailored interventions could engage families and children through identifying and prioritizing desired areas of focus. Despite literature defining tailoring as individualized treatment informed by assessment of behaviors, intervention application varies. This review aims to exhibit the use of the term "tailor" in pediatric obesity interventions and propose a uniform definition. Methods: We conducted a scoping review following PRISMA-ScR guidelines among peer-reviewed pediatric obesity prevention and management interventions published between 1995 and 2021. We categorized 69 studies into 6 groups: (1) individually tailored interventions, (2) computer-tailored interventions/tailored health messaging, (3) a protocolized group intervention with a tailored component, (4) only using the term tailor in the title, abstract, introduction, or discussion, e) using the term tailor to describe another term, and (5) interventions described as culturally tailored. Results: The scoping review exhibited a range of uses and lack of explicit definitions of tailoring in pediatric obesity interventions including some that deviate from individualized designs. Effective tailored interventions incorporated validated assessments for behaviors and multilevel determinants, and recipient-informed choice of target behavior(s) and programming. Conclusions: We urge interventionists to use tailoring to describe individualized, assessment-driven interventions and to clearly define how an intervention is tailored. This can elucidate the role of tailoring and its potential for addressing the heterogeneity of behavioral and social determinants for the prevention and management of pediatric obesity.
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Affiliation(s)
- Emily S Fu
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Cady Berkel
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| | - James L Merle
- Department of Population Health Sciences, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, USA
| | - Sara M St George
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Andrea K Graham
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Justin D Smith
- Department of Population Health Sciences, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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11
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Rodríguez-Ces AM, Rapado-González Ó, Salgado-Barreira Á, Santos MA, Aroso C, Vinhas AS, López-López R, Suárez-Cunqueiro MM. Liquid Biopsies Based on Cell-Free DNA Integrity as a Biomarker for Cancer Diagnosis: A Meta-Analysis. Diagnostics (Basel) 2024; 14:1465. [PMID: 39061602 PMCID: PMC11276058 DOI: 10.3390/diagnostics14141465] [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: 05/27/2024] [Revised: 06/28/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
Liquid biopsies have been identified as a viable source of cancer biomarkers. We aim to evaluate the diagnostic accuracy of cell-free DNA integrity (cfDI) in liquid biopsies for cancer. A comprehensive literature search was conducted through PubMed, Embase, Web of Science, and Cochrane Library up to June 2024. Seventy-two study units from forty-six studies, comprising 4286 cancer patients, were identified and evaluated. The Quality Assessment for Studies of Diagnostic Accuracy-2 (QUADAS-2) was used to assess study quality. Meta-regression analysis was employed to investigate the underlying factors contributing to heterogeneity, alongside an evaluation of publication bias. The bivariate random-effect model was utilized to compute the primary diagnostic outcomes and their corresponding 95% confidence intervals (CIs). The pooled sensitivity, specificity, and positive and negative likelihood ratios of cfDI in cancer diagnosis were 0.70 and 0.77, 3.26 and 0.34, respectively. The overall area under the curve was 0.84, with a diagnostic odds ratio of 10.63. This meta-analysis suggested that the cfDI index has a promising potential as a non-invasive and accurate diagnostic tool for cancer. Study registration: The study was registered at PROSPERO (reference No. CRD42021276290).
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Affiliation(s)
- Ana María Rodríguez-Ces
- Department of Surgery and Medical-Surgical Specialties, Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain; (A.M.R.-C.); (Ó.R.-G.)
- Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain;
- Liquid Biopsy Analysis Unit, Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago (IDIS), 15706 Santiago de Compostela, Spain
| | - Óscar Rapado-González
- Department of Surgery and Medical-Surgical Specialties, Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain; (A.M.R.-C.); (Ó.R.-G.)
- Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain;
- Liquid Biopsy Analysis Unit, Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago (IDIS), 15706 Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
| | - Ángel Salgado-Barreira
- Department of Public Health, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health—CIBERESP), 28029 Madrid, Spain
- Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain
| | - María Arminda Santos
- UNIPRO-Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS), CESPU, 4585-116 Gandra, Portugal; (M.A.S.); (C.A.); (A.S.V.)
| | - Carlos Aroso
- UNIPRO-Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS), CESPU, 4585-116 Gandra, Portugal; (M.A.S.); (C.A.); (A.S.V.)
| | - Ana Sofia Vinhas
- UNIPRO-Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS), CESPU, 4585-116 Gandra, Portugal; (M.A.S.); (C.A.); (A.S.V.)
| | - Rafael López-López
- Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain;
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago (IDIS), Complexo Hospitalario Universitario de Santiago de Compostela (CHUS, SERGAS), 15706 Santiago de Compostela, Spain
| | - María Mercedes Suárez-Cunqueiro
- Department of Surgery and Medical-Surgical Specialties, Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain; (A.M.R.-C.); (Ó.R.-G.)
- Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain;
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago (IDIS), Complexo Hospitalario Universitario de Santiago de Compostela (CHUS, SERGAS), 15706 Santiago de Compostela, Spain
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12
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Abbasi AF, Asim MN, Ahmed S, Vollmer S, Dengel A. Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases. Front Artif Intell 2024; 7:1428501. [PMID: 39021434 PMCID: PMC11252047 DOI: 10.3389/frai.2024.1428501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/12/2024] [Indexed: 07/20/2024] Open
Abstract
Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.
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Affiliation(s)
- Ahtisham Fazeel Abbasi
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Muhammad Nabeel Asim
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Sheraz Ahmed
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Sebastian Vollmer
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
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13
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Wu Y, Yu G, Jin K, Qian J. Advancing non-small cell lung cancer treatment: the power of combination immunotherapies. Front Immunol 2024; 15:1349502. [PMID: 39015563 PMCID: PMC11250065 DOI: 10.3389/fimmu.2024.1349502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
Non-small cell lung cancer (NSCLC) remains an unsolved challenge in oncology, signifying a substantial global health burden. While considerable progress has been made in recent years through the emergence of immunotherapy modalities, such as immune checkpoint inhibitors (ICIs), monotherapies often yield limited clinical outcomes. The rationale behind combining various immunotherapeutic or other anticancer agents, the mechanistic underpinnings, and the clinical evidence supporting their utilization is crucial in NSCLC therapy. Regarding the synergistic potential of combination immunotherapies, this study aims to provide insights to help the landscape of NSCLC treatment and improve clinical outcomes. In addition, this review article discusses the challenges and considerations of combination regimens, including toxicity management and patient selection.
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Affiliation(s)
- Yuanlin Wu
- Department of Thoracic Surgery, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Guangmao Yu
- Department of Thoracic Surgery, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Ketao Jin
- Department of Gastrointestinal, Colorectal and Anal Surgery, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang, China
| | - Jun Qian
- Department of Colorectal Surgery, Xinchang People’s Hospital, Affiliated Xinchang Hospital, Wenzhou Medical University, Xinchang, Zhejiang, China
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14
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Calado CRC. Bridging the gap between target-based and phenotypic-based drug discovery. Expert Opin Drug Discov 2024; 19:789-798. [PMID: 38747562 DOI: 10.1080/17460441.2024.2355330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 05/10/2024] [Indexed: 06/26/2024]
Abstract
INTRODUCTION The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed. AREAS COVERED This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process. EXPERT OPINION The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.
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Affiliation(s)
- Cecília R C Calado
- ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Lisboa, Portugal
- iBB - Institute for Bioengineering and Biosciences, i4HB - The Associate Laboratory Institute for Health and Bioeconomy, IST - Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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15
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Boschiero C, Neupane M, Yang L, Schroeder SG, Tuo W, Ma L, Baldwin RL, Van Tassell CP, Liu GE. A Pilot Detection and Associate Study of Gene Presence-Absence Variation in Holstein Cattle. Animals (Basel) 2024; 14:1921. [PMID: 38998033 PMCID: PMC11240624 DOI: 10.3390/ani14131921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/18/2024] [Accepted: 06/26/2024] [Indexed: 07/14/2024] Open
Abstract
Presence-absence variations (PAVs) are important structural variations, wherein a genomic segment containing one or more genes is present in some individuals but absent in others. While PAVs have been extensively studied in plants, research in cattle remains limited. This study identified PAVs in 173 Holstein bulls using whole-genome sequencing data and assessed their associations with 46 economically important traits. Out of 28,772 cattle genes (from the longest transcripts), a total of 26,979 (93.77%) core genes were identified (present in all individuals), while variable genes included 928 softcore (present in 95-99% of individuals), 494 shell (present in 5-94%), and 371 cloud genes (present in <5%). Cloud genes were enriched in functions associated with hormonal and antimicrobial activities, while shell genes were enriched in immune functions. PAV-based genome-wide association studies identified associations between gene PAVs and 16 traits including milk, fat, and protein yields, as well as traits related to health and reproduction. Associations were found on multiple chromosomes, illustrating important associations on cattle chromosomes 7 and 15, involving olfactory receptor and immune-related genes, respectively. By examining the PAVs at the population level, the results of this research provided crucial insights into the genetic structures underlying the complex traits of Holstein cattle.
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Affiliation(s)
- Clarissa Boschiero
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- Department of Veterinary Medicine, University of Maryland, College Park, MD 20742, USA
| | - Mahesh Neupane
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Liu Yang
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Steven G Schroeder
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Wenbin Tuo
- Animal Parasitic Diseases Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
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16
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Li Z, Liu X, Tang Z, Jin N, Zhang P, Eadon MT, Song Q, Chen YV, Su J. TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease. J Am Med Inform Assoc 2024:ocae158. [PMID: 38916922 DOI: 10.1093/jamia/ocae158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/31/2024] [Accepted: 06/10/2024] [Indexed: 06/26/2024] Open
Abstract
OBJECTIVE Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. MATERIALS AND METHODS We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. RESULTS The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. DISCUSSION The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. CONCLUSION TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.
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Affiliation(s)
- Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Department of Computer Graphics Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Ziyang Tang
- Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Nanxin Jin
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Yingjie V Chen
- Department of Computer Graphics Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, United States
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Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
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Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
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18
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Robertson AJ, Mallett AJ, Stark Z, Sullivan C. It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2024; 5:e55632. [PMID: 38935958 PMCID: PMC11211701 DOI: 10.2196/55632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/08/2024] [Accepted: 04/09/2024] [Indexed: 06/29/2024]
Abstract
Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.
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Affiliation(s)
- Alan J Robertson
- Faculty of Medicine, University of Queensland, Hertson, Australia
- Medical Genomics Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Queensland Digital Health Centre, University of Queensland, Brisbane, Australia
- The Genomic Institute, Department of Health, Queensland Government, Brisbane, Australia
| | - Andrew J Mallett
- Department of Renal Medicine, Townsville University Hospital, Townsville, Australia
- College of Medicine and Dentistry, James Cook University, Townsville, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Australia
- Australian Genomics, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
| | - Clair Sullivan
- Queensland Digital Health Centre, University of Queensland, Brisbane, Australia
- Centre for Health Services Research, Faculty of Medicine, University of Queensland, Woolloongabba, Australia
- Metro North Hospital and Health Service, Department of Health, Queensland Government, Brisbane, Australia
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19
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Prince JB, Davis HL, Tan J, Muller-Townsend K, Markovic S, Lewis DMG, Hastie B, Thompson MB, Drummond PD, Fujiyama H, Sohrabi HR. Cognitive and neuroscientific perspectives of healthy ageing. Neurosci Biobehav Rev 2024; 161:105649. [PMID: 38579902 DOI: 10.1016/j.neubiorev.2024.105649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/17/2024] [Accepted: 03/30/2024] [Indexed: 04/07/2024]
Abstract
With dementia incidence projected to escalate significantly within the next 25 years, the United Nations declared 2021-2030 the Decade of Healthy Ageing, emphasising cognition as a crucial element. As a leading discipline in cognition and ageing research, psychology is well-equipped to offer insights for translational research, clinical practice, and policy-making. In this comprehensive review, we discuss the current state of knowledge on age-related changes in cognition and psychological health. We discuss cognitive changes during ageing, including (a) heterogeneity in the rate, trajectory, and characteristics of decline experienced by older adults, (b) the role of cognitive reserve in age-related cognitive decline, and (c) the potential for cognitive training to slow this decline. We also examine ageing and cognition through multiple theoretical perspectives. We highlight critical unresolved issues, such as the disparate implications of subjective versus objective measures of cognitive decline and the insufficient evaluation of cognitive training programs. We suggest future research directions, and emphasise interdisciplinary collaboration to create a more comprehensive understanding of the factors that modulate cognitive ageing.
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Affiliation(s)
- Jon B Prince
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia.
| | - Helen L Davis
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia
| | - Jane Tan
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia
| | - Katrina Muller-Townsend
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia
| | - Shaun Markovic
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia; Discipline of Psychology, Counselling and Criminology, Edith Cowan University, WA, Australia
| | - David M G Lewis
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia
| | | | - Matthew B Thompson
- School of Psychology, Murdoch University, WA, Australia; Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, WA, Australia
| | - Peter D Drummond
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia
| | - Hakuei Fujiyama
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia; Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, WA, Australia
| | - Hamid R Sohrabi
- School of Psychology, Murdoch University, WA, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, WA, Australia; School of Medical and Health Sciences, Edith Cowan University, WA, Australia; Department of Biomedical Sciences, Macquarie University, NSW, Australia.
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20
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Wang Q, Jin D, Liu C, Shi L, Li T. A Tumor-Specific Cascade-Activating Smart Prodrug System for Enhanced Targeted Therapy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309482. [PMID: 38150668 DOI: 10.1002/smll.202309482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/28/2023] [Indexed: 12/29/2023]
Abstract
Developing intelligently targeted drugs with low side effects is urgent for cancer treatment. Toward this goal, a tumor-specific cascade-activating smart prodrug system consisting of a G-quadruplex(G4)-modulated tumor-targeted DNA vehicle and a well-designed cellular stimuli-responsive ligand-drug conjugates (LDCs) is proposed. An original "donor-acceptor" binary fluorescent ligand, with ultrahigh affinity, brightness, and photostability, is engineered to tightly bind G4 structures and significantly improve the nuclease resistance of the DNA vehicle, which serves as a bridge contributing to the construction of the prodrug system, named ApG4/LDCs. Sodium nitroprusside and doxorubicin are loaded into ApG4/LDCs in one pot and generate nitric oxide and superoxide anion in response to cancer cellular environments, which in cascade generates peroxynitrite to cause DNA damage while promoting the self-monitored drug release to achieve enhanced targeted therapy. Such a cascade activation and self-reinforcement process is executed only when the prodrug system targets the tumor tissue followed by cell uptake, showing significant antitumor efficacy and greatly weakening the damage to normal tissues. Given the unique features, the innovative strategy for prodrug design may open a new door to precision disease treatment.
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Affiliation(s)
- Qiwei Wang
- Department of Chemistry, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China
| | - Duo Jin
- Department of Chemistry, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China
| | - Chengbin Liu
- Department of Chemistry, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China
| | - Lili Shi
- Department of Chemistry, Anhui University, 111 Jiulong Road, Hefei, Anhui, 230601, China
| | - Tao Li
- Department of Chemistry, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China
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21
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van Dellen E. Precision psychiatry: predicting predictability. Psychol Med 2024; 54:1500-1509. [PMID: 38497091 DOI: 10.1017/s0033291724000370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
- Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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22
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Zhou Z, Liu J, Xiong T, Liu Y, Tuan RS, Li ZA. Engineering Innervated Musculoskeletal Tissues for Regenerative Orthopedics and Disease Modeling. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310614. [PMID: 38200684 DOI: 10.1002/smll.202310614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Musculoskeletal (MSK) disorders significantly burden patients and society, resulting in high healthcare costs and productivity loss. These disorders are the leading cause of physical disability, and their prevalence is expected to increase as sedentary lifestyles become common and the global population of the elderly increases. Proper innervation is critical to maintaining MSK function, and nerve damage or dysfunction underlies various MSK disorders, underscoring the potential of restoring nerve function in MSK disorder treatment. However, most MSK tissue engineering strategies have overlooked the significance of innervation. This review first expounds upon innervation in the MSK system and its importance in maintaining MSK homeostasis and functions. This will be followed by strategies for engineering MSK tissues that induce post-implantation in situ innervation or are pre-innervated. Subsequently, research progress in modeling MSK disorders using innervated MSK organoids and organs-on-chips (OoCs) is analyzed. Finally, the future development of engineering innervated MSK tissues to treat MSK disorders and recapitulate disease mechanisms is discussed. This review provides valuable insights into the underlying principles, engineering methods, and applications of innervated MSK tissues, paving the way for the development of targeted, efficacious therapies for various MSK conditions.
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Affiliation(s)
- Zhilong Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
| | - Jun Liu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Shatin, NT, Hong Kong SAR, P. R. China
| | - Tiandi Xiong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Shatin, NT, Hong Kong SAR, P. R. China
| | - Yuwei Liu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, 518000, P. R. China
| | - Rocky S Tuan
- Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Shatin, NT, Hong Kong SAR, P. R. China
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Institute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
| | - Zhong Alan Li
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Shatin, NT, Hong Kong SAR, P. R. China
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Key Laboratory of Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, P. R. China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518057, P. R. China
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23
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Deng YT, Wu BS, Yang L, He XY, Kang JJ, Liu WS, Li ZY, Wu XR, Zhang YR, Chen SD, Ge YJ, Huang YY, Feng JF, Zhu Y, Dong Q, Mao Y, Cheng W, Yu JT. Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults. Nat Hum Behav 2024; 8:1194-1208. [PMID: 38589703 DOI: 10.1038/s41562-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/11/2024] [Indexed: 04/10/2024]
Abstract
While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.
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Affiliation(s)
- Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Xin-Rui Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Ying Zhu
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital Fudan University, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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24
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Guo L, Reddy KP, Van Iseghem T, Pierce WN. Enhancing data practices for Whole Health: Strategies for a transformative future. Learn Health Syst 2024; 8:e10426. [PMID: 38883871 PMCID: PMC11176597 DOI: 10.1002/lrh2.10426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/22/2024] [Accepted: 04/16/2024] [Indexed: 06/18/2024] Open
Abstract
We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.
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Affiliation(s)
- Lei Guo
- Whole Health VA St. Louis Health Care System St. Louis Missouri USA
- School of Interdisciplinary Health Professions Northern Illinois University DeKalb Illinois USA
| | - Kavitha P Reddy
- Whole Health VA St. Louis Health Care System St. Louis Missouri USA
- Department of Veterans Affairs VHA Office of Patient-Centered Care and Cultural Transformation Washington D.C. USA
- School of Medicine Washington University in St. Louis St. Louis Missouri USA
| | - Theresa Van Iseghem
- Whole Health VA St. Louis Health Care System St. Louis Missouri USA
- School of Medicine Saint Louis University St. Louis Missouri USA
| | - Whitney N Pierce
- Whole Health VA St. Louis Health Care System St. Louis Missouri USA
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25
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Marquez G, Dechiraju H, Baniya P, Li H, Tebyani M, Pansodtee P, Jafari M, Barbee A, Orozco J, Teodorescu M, Rolandi M, Gomez M. Delivering biochemicals with precision using bioelectronic devices enhanced with feedback control. PLoS One 2024; 19:e0298286. [PMID: 38743674 PMCID: PMC11093312 DOI: 10.1371/journal.pone.0298286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/22/2024] [Indexed: 05/16/2024] Open
Abstract
Precision medicine endeavors to personalize treatments, considering individual variations in patient responses based on factors like genetic mutations, age, and diet. Integrating this approach dynamically, bioelectronics equipped with real-time sensing and intelligent actuation present a promising avenue. Devices such as ion pumps hold potential for precise therapeutic drug delivery, a pivotal aspect of effective precision medicine. However, implementing bioelectronic devices in precision medicine encounters formidable challenges. Variability in device performance due to fabrication inconsistencies and operational limitations, including voltage saturation, presents significant hurdles. To address this, closed-loop control with adaptive capabilities and explicit handling of saturation becomes imperative. Our research introduces an enhanced sliding mode controller capable of managing saturation, adept at satisfactory control actions amidst model uncertainties. To evaluate the controller's effectiveness, we conducted in silico experiments using an extended mathematical model of the proton pump. Subsequently, we compared the performance of our developed controller with classical Proportional Integral Derivative (PID) and machine learning (ML)-based controllers. Furthermore, in vitro experiments assessed the controller's efficacy using various reference signals for controlled Fluoxetine delivery. These experiments showcased consistent performance across diverse input signals, maintaining the current value near the reference with a relative error of less than 7% in all trials. Our findings underscore the potential of the developed controller to address challenges in bioelectronic device implementation, offering reliable precision in drug delivery strategies within the realm of precision medicine.
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Affiliation(s)
- Giovanny Marquez
- Applied Mathematics, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Harika Dechiraju
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Prabhat Baniya
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Houpu Li
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Maryam Tebyani
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Pattawong Pansodtee
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Mohammad Jafari
- Department of Earth and Space Sciences, Columbus State University, Columbus, GA, United States of America
| | - Alexie Barbee
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Jonathan Orozco
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Mircea Teodorescu
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Marco Rolandi
- Electrical and Computer Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
| | - Marcella Gomez
- Applied Mathematics, Baskin School of Engineering, University of California, Santa Cruz, CA, United States of America
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26
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Yi Z, Lai X, Sun A, Fang S. Tongue feature recognition to monitor rehabilitation: deep neural network with visual attention mechanism. Front Bioeng Biotechnol 2024; 12:1392513. [PMID: 38784768 PMCID: PMC11112418 DOI: 10.3389/fbioe.2024.1392513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024] Open
Abstract
Objective We endeavor to develop a novel deep learning architecture tailored specifically for the analysis and classification of tongue features, including color, shape, and coating. Unlike conventional methods based on architectures like VGG or ResNet, our proposed method aims to address the challenges arising from their extensive size, thereby mitigating the overfitting problem. Through this research, we aim to contribute to the advancement of techniques in tongue feature recognition, ultimately leading to more precise diagnoses and better patient rehabilitation in Traditional Chinese Medicine (TCM). Methods In this study, we introduce TGANet (Tongue Feature Attention Network) to enhance model performance. TGANet utilizes the initial five convolutional blocks of pre-trained VGG16 as the backbone and integrates an attention mechanism into this backbone. The integration of the attention mechanism aims to mimic human cognitive attention, emphasizing model weights on pivotal regions of the image. During the learning process, the allocation of attention weights facilitates the interpretation of causal relationships in the model's decision-making. Results Experimental results demonstrate that TGANet outperforms baseline models, including VGG16, ResNet18, and TSC-WNet, in terms of accuracy, precision, F1 score, and AUC metrics. Additionally, TGANet provides a more intuitive and meaningful understanding of tongue feature classification models through the visualization of attention weights. Conclusion In conclusion, TGANet presents an effective approach to tongue feature classification, addressing challenges associated with model size and overfitting. By leveraging the attention mechanism and pre-trained VGG16 backbone, TGANet achieves superior performance metrics and enhances the interpretability of the model's decision-making process. The visualization of attention weights contributes to a more intuitive understanding of the classification process, making TGANet a promising tool in tongue diagnosis and rehabilitation.
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Affiliation(s)
- Zhengheng Yi
- Shenzhen Fuyong People’s Hospital, Shenzhen, China
- Guangzhou University of Chinese Medicine, Guangzhou, China
- National Famous Traditional Chinese Medicine Expert LAI Xin-sheng Inheritance Studio, Guangzhou, China
| | - Xinsheng Lai
- Guangzhou University of Chinese Medicine, Guangzhou, China
- National Famous Traditional Chinese Medicine Expert LAI Xin-sheng Inheritance Studio, Guangzhou, China
| | - Aining Sun
- Guangdong Zhengyuanchun Traditional Chinese Medicine Clinic Co., Ltd, Guangzhou, China
| | - Senlin Fang
- Faculty of Data Science, City University of Macau, Macau, China
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27
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Montanucci L, Brünger T, Bhattarai N, Boßelmann CM, Kim S, Allen JP, Zhang J, Klöckner C, Fariselli P, May P, Lemke JR, Myers SJ, Yuan H, Traynelis SF, Lal D. Distances from ligands as main predictive features for pathogenicity and functional effect of variants in NMDA receptors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306939. [PMID: 38766179 PMCID: PMC11100844 DOI: 10.1101/2024.05.06.24306939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Genetic variants in genes GRIN1 , GRIN2A , GRIN2B , and GRIN2D , which encode subunits of the N-methyl-D-aspartate receptor (NMDAR), have been associated with severe and heterogeneous neurologic diseases. Missense variants in these genes can result in gain or loss of the NMDAR function, requiring opposite therapeutic treatments. Computational methods that predict pathogenicity and molecular functional effects are therefore crucial for accurate diagnosis and therapeutic applications. We assembled missense variants: 201 from patients, 631 from general population, and 159 characterized by electrophysiological readouts showing whether they can enhance or reduce the receptor function. This includes new functional data from 47 variants reported here, for the first time. We found that pathogenic/benign variants and variants that increase/decrease the channel function were distributed unevenly on the protein structure, with spatial proximity to ligands bound to the agonist and antagonist binding sites being key predictive features. Leveraging distances from ligands, we developed two independent machine learning-based predictors for NMDAR missense variants: a pathogenicity predictor which outperforms currently available predictors (AUC=0.945, MCC=0.726), and the first binary predictor of molecular function (increase or decrease) (AUC=0.809, MCC=0.523). Using these, we reclassified variants of uncertain significance in the ClinVar database and refined a previous genome-informed epidemiological model to estimate the birth incidence of molecular mechanism-defined GRIN disorders. Our findings demonstrate that distance from ligands is an important feature in NMDARs that can enhance variant pathogenicity prediction and enable functional prediction. Further studies with larger numbers of phenotypically and functionally characterized variants will enhance the potential clinical utility of this method.
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28
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Shen J, Wang S, Sun H, Huang J, Bai L, Wang X, Dong Y, Tang Z. A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data. BMC Med Res Methodol 2024; 24:105. [PMID: 38702624 PMCID: PMC11067084 DOI: 10.1186/s12874-024-02232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.
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Affiliation(s)
- Junjie Shen
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Shuo Wang
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, 79085, Freiburg, Germany
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Jie Huang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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30
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Ferrer A, Duffy P, Olson RJ, Meiners MA, Schultz-Rogers L, Macke EL, Safgren S, Morales-Rosado JA, Cousin MA, Oliver GR, Rider D, Williams M, Pichurin PN, Deyle DR, Morava E, Gavrilova RH, Dhamija R, Wierenga KJ, Lanpher BC, Babovic-Vuksanovic D, Kaiwar C, Vitek CR, McAllister TM, Wick MJ, Schimmenti LA, Lazaridis KN, Vairo FPE, Klee EW. Semiautomated approach focused on new genomic information results in time and effort-efficient reannotation of negative exome data. Hum Genet 2024; 143:649-666. [PMID: 38538918 DOI: 10.1007/s00439-024-02664-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/25/2024] [Indexed: 05/18/2024]
Abstract
Most rare disease patients (75-50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genomic databases to enable rapid review of genomic findings. We tested RENEW in an unselected cohort of 1066 undiagnosed cases with a broad spectrum of phenotypes from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. 5741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 s per variant (32 h total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.
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Affiliation(s)
- Alejandro Ferrer
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Patrick Duffy
- Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Rory J Olson
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Michael A Meiners
- Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Laura Schultz-Rogers
- Department of Pathology and Lab Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Erica L Macke
- The Institute of Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | | | - Joel A Morales-Rosado
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Margot A Cousin
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Gavin R Oliver
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - David Rider
- Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Megan Williams
- Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Pavel N Pichurin
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | - David R Deyle
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | - Eva Morava
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | | | - Radhika Dhamija
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | - Klass J Wierenga
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Carolyn R Vitek
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Myra J Wick
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | - Lisa A Schimmenti
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
- Departments of Otorhinolaryngology, Head and Neck Surgery, Ophthalmology, and Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Konstantinos N Lazaridis
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Filippo Pinto E Vairo
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA
| | - Eric W Klee
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
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31
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Pantel AR, Bae SW, Li EJ, O'Brien SR, Manning HC. PET Imaging of Metabolism, Perfusion, and Hypoxia: FDG and Beyond. Cancer J 2024; 30:159-169. [PMID: 38753750 PMCID: PMC11101148 DOI: 10.1097/ppo.0000000000000716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
ABSTRACT Imaging glucose metabolism with [18F]fluorodeoxyglucose positron emission tomography has transformed the diagnostic and treatment algorithms of numerous malignancies in clinical practice. The cancer phenotype, though, extends beyond dysregulation of this single pathway. Reprogramming of other pathways of metabolism, as well as altered perfusion and hypoxia, also typifies malignancy. These features provide other opportunities for imaging that have been developed and advanced into humans. In this review, we discuss imaging metabolism, perfusion, and hypoxia in cancer, focusing on the underlying biology to provide context. We conclude by highlighting the ability to image multiple facets of biology to better characterize cancer and guide targeted treatment.
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Affiliation(s)
- Austin R Pantel
- From the Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | - Seong-Woo Bae
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Elizabeth J Li
- From the Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | - Sophia R O'Brien
- From the Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | - H Charles Manning
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
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32
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Kim Y, Choi SR, Cho KH. Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance. Cancers (Basel) 2024; 16:1337. [PMID: 38611015 PMCID: PMC11010870 DOI: 10.3390/cancers16071337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.
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Affiliation(s)
| | | | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-Inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; (Y.K.); (S.R.C.)
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33
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Lungu CN, Creteanu A, Mehedinti MC. Endovascular Drug Delivery. Life (Basel) 2024; 14:451. [PMID: 38672722 PMCID: PMC11051410 DOI: 10.3390/life14040451] [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: 02/06/2024] [Revised: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Drug-eluting stents (DES) and balloons revolutionize atherosclerosis treatment by targeting hyperplastic tissue responses through effective local drug delivery strategies. This review examines approved and emerging endovascular devices, discussing drug release mechanisms and their impacts on arterial drug distribution. It emphasizes the crucial role of drug delivery in modern cardiovascular care and highlights how device technologies influence vascular behavior based on lesion morphology. The future holds promise for lesion-specific treatments, particularly in the superficial femoral artery, with recent CE-marked devices showing encouraging results. Exciting strategies and new patents focus on local drug delivery to prevent restenosis, shaping the future of interventional outcomes. In summary, as we navigate the ever-evolving landscape of cardiovascular intervention, it becomes increasingly evident that the future lies in tailoring treatments to the specific characteristics of each lesion. By leveraging cutting-edge technologies and harnessing the potential of localized drug delivery, we stand poised to usher in a new era of precision medicine in vascular intervention.
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Affiliation(s)
- Claudiu N. Lungu
- Department of Functional and Morphological Science, Faculty of Medicine and Pharmacy, Dunarea de Jos University, 800010 Galati, Romania;
| | - Andreea Creteanu
- Department of Pharmaceutical Technology, University of Medicine and Pharmacy Grigore T Popa, 700115 Iași, Romania
| | - Mihaela C. Mehedinti
- Department of Functional and Morphological Science, Faculty of Medicine and Pharmacy, Dunarea de Jos University, 800010 Galati, Romania;
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34
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Tang D, Peng X, Wu S, Tang S. Autonomous Nanorobots as Miniaturized Surgeons for Intracellular Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:595. [PMID: 38607129 PMCID: PMC11013175 DOI: 10.3390/nano14070595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/06/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024]
Abstract
Artificial nanorobots have emerged as promising tools for a wide range of biomedical applications, including biosensing, detoxification, and drug delivery. Their unique ability to navigate confined spaces with precise control extends their operational scope to the cellular or subcellular level. By combining tailored surface functionality and propulsion mechanisms, nanorobots demonstrate rapid penetration of cell membranes and efficient internalization, enhancing intracellular delivery capabilities. Moreover, their robust motion within cells enables targeted interactions with intracellular components, such as proteins, molecules, and organelles, leading to superior performance in intracellular biosensing and organelle-targeted cargo delivery. Consequently, nanorobots hold significant potential as miniaturized surgeons capable of directly modulating cellular dynamics and combating metastasis, thereby maximizing therapeutic outcomes for precision therapy. In this review, we provide an overview of the propulsion modes of nanorobots and discuss essential factors to harness propulsive energy from the local environment or external power sources, including structure, material, and engine selection. We then discuss key advancements in nanorobot technology for various intracellular applications. Finally, we address important considerations for future nanorobot design to facilitate their translation into clinical practice and unlock their full potential in biomedical research and healthcare.
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Affiliation(s)
- Daitian Tang
- Luohu Clinical Institute, School of Medicine, Shantou University, Shantou 515000, China; (D.T.); (X.P.)
| | - Xiqi Peng
- Luohu Clinical Institute, School of Medicine, Shantou University, Shantou 515000, China; (D.T.); (X.P.)
| | - Song Wu
- Luohu Clinical Institute, School of Medicine, Shantou University, Shantou 515000, China; (D.T.); (X.P.)
| | - Songsong Tang
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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35
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Wang X, Yang K, Jia T, Gu F, Wang C, Xu K, Shu Z, Xia J, Zhu Q, Zhou X. KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition. Brief Bioinform 2024; 25:bbae161. [PMID: 38605639 PMCID: PMC11009469 DOI: 10.1093/bib/bbae161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/20/2024] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.
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Affiliation(s)
| | - Kuo Yang
- Corresponding author: Kuo Yang and Xuezhong Zhou, Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China. E-mail: and
| | | | | | | | | | | | | | | | - Xuezhong Zhou
- Corresponding author: Kuo Yang and Xuezhong Zhou, Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China. E-mail: and
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36
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Takundwa MM, Thimiri Govinda Raj DB. Novel strategies for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:9-21. [PMID: 38789188 DOI: 10.1016/bs.pmbts.2024.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.
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Affiliation(s)
- Mutsa Monica Takundwa
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - Deepak B Thimiri Govinda Raj
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa.
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37
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Shen J, Wang S, Dong Y, Sun H, Wang X, Tang Z. A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data. BMC Bioinformatics 2024; 25:119. [PMID: 38509499 PMCID: PMC10953151 DOI: 10.1186/s12859-024-05741-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. RESULTS We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. CONCLUSIONS The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.
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Affiliation(s)
- Junjie Shen
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Shuo Wang
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79085, Freiburg, Germany
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China.
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38
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Brbić M, Yasunaga M, Agarwal P, Leskovec J. Predicting drug outcome of population via clinical knowledge graph. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.06.24303800. [PMID: 38496488 PMCID: PMC10942490 DOI: 10.1101/2024.03.06.24303800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Optimal treatments depend on numerous factors such as drug chemical properties, disease biology, and patient characteristics to which the treatment is applied. To realize the promise of AI in healthcare, there is a need for designing systems that can capture patient heterogeneity and relevant biomedical knowledge. Here we present PlaNet, a geometric deep learning framework that reasons over population variability, disease biology, and drug chemistry by representing knowledge in the form of a massive clinical knowledge graph that can be enhanced by language models. Our framework is applicable to any sub-population, any drug as well drug combinations, any disease, and to a wide range of pharmacological tasks. We apply the PlaNet framework to reason about outcomes of clinical trials: PlaNet predicts drug efficacy and adverse events, even for experimental drugs and their combinations that have never been seen by the model. Furthermore, PlaNet can estimate the effect of changing population on the trial outcome with direct implications on patient stratification in clinical trials. PlaNet takes fundamental steps towards AI-guided clinical trials design, offering valuable guidance for realizing the vision of precision medicine using AI.
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Affiliation(s)
- Maria Brbić
- School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Michihiro Yasunaga
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Prabhat Agarwal
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
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Carè M, Chiappalone M, Cota VR. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. Front Neurosci 2024; 18:1363128. [PMID: 38516316 PMCID: PMC10954825 DOI: 10.3389/fnins.2024.1363128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.
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Affiliation(s)
- Marta Carè
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, Genova, Italy
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, Italy
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40
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Duan XP, Qin BD, Jiao XD, Liu K, Wang Z, Zang YS. New clinical trial design in precision medicine: discovery, development and direction. Signal Transduct Target Ther 2024; 9:57. [PMID: 38438349 PMCID: PMC10912713 DOI: 10.1038/s41392-024-01760-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 03/06/2024] Open
Abstract
In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.
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Affiliation(s)
- Xiao-Peng Duan
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Bao-Dong Qin
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Xiao-Dong Jiao
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Ke Liu
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhan Wang
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yuan-Sheng Zang
- Department of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
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41
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Meller A, Kelly D, Smith LG, Bowman GR. Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci 2024; 33:e4902. [PMID: 38358129 PMCID: PMC10868452 DOI: 10.1002/pro.4902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular BiophysicsWashington University in St. LouisSt. LouisMissouriUSA
- Medical Scientist Training ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Devin Kelly
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Louis G. Smith
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Liu Y, Fan L, Yang H, Wang D, Liu R, Shan T, Xia X. Ketogenic therapy towards precision medicine for brain diseases. Front Nutr 2024; 11:1266690. [PMID: 38450235 PMCID: PMC10915067 DOI: 10.3389/fnut.2024.1266690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Precision nutrition and nutrigenomics are emerging in the development of therapies for multiple diseases. The ketogenic diet (KD) is the most widely used clinical diet, providing high fat, low carbohydrate, and adequate protein. KD produces ketones and alters the metabolism of patients. Growing evidence suggests that KD has therapeutic effects in a wide range of neuronal diseases including epilepsy, neurodegeneration, cancer, and metabolic disorders. Although KD is considered to be a low-side-effect diet treatment, its therapeutic mechanism has not yet been fully elucidated. Also, its induced keto-response among different populations has not been elucidated. Understanding the ketone metabolism in health and disease is critical for the development of KD-associated therapeutics and synergistic therapy under any physiological background. Here, we review the current advances and known heterogeneity of the KD response and discuss the prospects for KD therapy from a precision nutrition perspective.
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Affiliation(s)
- Yang Liu
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng, China
- Henan Key Laboratory of Brain Targeted Bio-nanomedicine, School of Life Sciences, Henan University, Kaifeng, China
| | - Linlin Fan
- Henan Key Laboratory of Brain Targeted Bio-nanomedicine, School of Life Sciences, Henan University, Kaifeng, China
| | - Haoying Yang
- Henan Key Laboratory of Brain Targeted Bio-nanomedicine, School of Life Sciences, Henan University, Kaifeng, China
| | - Danli Wang
- Zhoushan People’s Hospital, Zhoushan, China
| | - Runhan Liu
- Henan Key Laboratory of Brain Targeted Bio-nanomedicine, School of Life Sciences, Henan University, Kaifeng, China
| | - Tikun Shan
- Neurosurgery Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Xue Xia
- Henan Key Laboratory of Brain Targeted Bio-nanomedicine, School of Life Sciences, Henan University, Kaifeng, China
- School of Environmental and Life Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW, Australia
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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Wei L, Xin Y, Pu M, Zhang Y. Patient-specific analysis of co-expression to measure biological network rewiring in individuals. Life Sci Alliance 2024; 7:e202302253. [PMID: 37977656 PMCID: PMC10656351 DOI: 10.26508/lsa.202302253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.
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Affiliation(s)
- Lanying Wei
- Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China
| | - Yucui Xin
- Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China
| | - Mengchen Pu
- Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China
| | - Yingsheng Zhang
- Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China
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Chen HY, Lin SY, Shih JC, Kang J, Tai YY, Shaw SW, Chen KC, Mai K, Lee CN. Changing the standardised obstetric care by expanded carrier screening and counselling: a multicentre prospective cohort study. J Med Genet 2024; 61:176-181. [PMID: 37798098 DOI: 10.1136/jmg-2023-109268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Expanded genetic screening before conception or during prenatal care can provide a more comprehensive evaluation of heritable fetal diseases. This study aimed to provide a large cohort to evaluate the significance of expanded carrier screening and to consolidate the role of expanded genetic screening in prenatal care. METHODS This multicentre, retrospective cohort study was conducted between 31 December 2019 and 21 July 2022. A screening panel containing 302 genes and next-generation sequencing were used for the evaluation. The patients were referred from obstetric clinics, infertility centres and medical centres. Genetic counsellors conducted consultation for at least 15 min before and after screening. RESULTS A total of 1587 patients were screened, and 653 pairs were identified. Among the couples who underwent the screening, 62 (9.49%) had pathogenic variants detected on the same genes. In total, 212 pathogenic genes were identified in this study. A total of 1173 participants carried at least one mutated gene, with a positive screening rate of 73.91%. Among the pathogenic variants that were screened, the gene encoding gap junction beta-2 (GJB2) exhibited the highest prevalence, amounting to 19.85%. CONCLUSION Next-generation sequencing carrier screening provided additional information that may alter prenatal obstetric care by 9.49%. Pan-ethnic genetic screening and counselling should be suggested for couples of fertile age.
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Affiliation(s)
- Han-Ying Chen
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
- Program for Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei, Taiwan
| | - Shin-Yu Lin
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
| | - Jin-Chung Shih
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
| | - Jessica Kang
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Yun Tai
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Steven W Shaw
- Department of Obstetrics and Gynecology, Taipei Chang Gung Memorial Hospital, Taipei, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Kuang-Cheng Chen
- College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California, USA
| | - Kevin Mai
- College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California, USA
| | - Chien-Nan Lee
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
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Lv Y, Li W, Liao W, Jiang H, Liu Y, Cao J, Lu W, Feng Y. Nano-Drug Delivery Systems Based on Natural Products. Int J Nanomedicine 2024; 19:541-569. [PMID: 38260243 PMCID: PMC10802180 DOI: 10.2147/ijn.s443692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Natural products have proven to have significant curative effects and are increasingly considered as potential candidates for clinical prevention, diagnosis, and treatment. Compared with synthetic drugs, natural products not only have diverse structures but also exhibit a range of biological activities against different disease states and molecular targets, making them attractive for development in the field of medicine. Despite advancements in the use of natural products for clinical purposes, there remain obstacles that hinder their full potential. These challenges include issues such as limited solubility and stability when administered orally, as well as short durations of effectiveness. To address these concerns, nano-drug delivery systems have emerged as a promising solution to overcome the barriers faced in the clinical application of natural products. These systems offer notable advantages, such as a large specific surface area, enhanced targeting capabilities, and the ability to achieve sustained and controlled release. Extensive in vitro and in vivo studies have provided further evidence supporting the efficacy and safety of nanoparticle-based systems in delivering natural products in preclinical disease models. This review describes the limitations of natural product applications and the current status of natural products combined with nanotechnology. The latest advances in nano-drug delivery systems for delivery of natural products are considered from three aspects: connecting targeting warheads, self-assembly, and co-delivery. Finally, the challenges faced in the clinical translation of nano-drugs are discussed.
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Affiliation(s)
- Ying Lv
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Wenqing Li
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Wei Liao
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Haibo Jiang
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Yuwei Liu
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Jiansheng Cao
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Wenfei Lu
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
| | - Yufei Feng
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, Harbin, 150040, People’s Republic of China
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47
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Chen J, Iraji A, Fu Z, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD. Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301013. [PMID: 38260328 PMCID: PMC10802663 DOI: 10.1101/2024.01.09.24301013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Chowdhury MA, Zhang JJ, Rizk R, Chen WCW. Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence. Front Physiol 2024; 14:1344885. [PMID: 38264333 PMCID: PMC10803627 DOI: 10.3389/fphys.2023.1344885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Stem/progenitor cells have been widely evaluated as a promising therapeutic option for heart failure (HF). Numerous clinical trials with stem/progenitor cell-based therapy (SCT) for HF have demonstrated encouraging results, but not without limitations or discrepancies. Recent technological advancements in multiomics, bioinformatics, precision medicine, artificial intelligence (AI), and machine learning (ML) provide new approaches and insights for stem cell research and therapeutic development. Integration of these new technologies into stem/progenitor cell therapy for HF may help address: 1) the technical challenges to obtain reliable and high-quality therapeutic precursor cells, 2) the discrepancies between preclinical and clinical studies, and 3) the personalized selection of optimal therapeutic cell types/populations for individual patients in the context of precision medicine. This review summarizes the current status of SCT for HF in clinics and provides new perspectives on the development of computation-aided SCT in the era of precision medicine and AI/ML.
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Affiliation(s)
- Mohammed A. Chowdhury
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
- Department of Public Health and Health Sciences, Health Sciences Ph.D. Program, School of Health Sciences, University of South Dakota, Vermillion, SD, United States
- Department of Cardiology, North Central Heart, Avera Heart Hospital, Sioux Falls, SD, United States
| | - Jing J. Zhang
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
| | - Rodrigue Rizk
- Department of Computer Science, University of South Dakota, Vermillion, SD, United States
| | - William C. W. Chen
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States
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Dolzhenko E, English A, Dashnow H, De Sena Brandine G, Mokveld T, Rowell WJ, Karniski C, Kronenberg Z, Danzi MC, Cheung WA, Bi C, Farrow E, Wenger A, Chua KP, Martínez-Cerdeño V, Bartley TD, Jin P, Nelson DL, Zuchner S, Pastinen T, Quinlan AR, Sedlazeck FJ, Eberle MA. Characterization and visualization of tandem repeats at genome scale. Nat Biotechnol 2024:10.1038/s41587-023-02057-3. [PMID: 38168995 DOI: 10.1038/s41587-023-02057-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/06/2023] [Indexed: 01/05/2024]
Abstract
Tandem repeat (TR) variation is associated with gene expression changes and numerous rare monogenic diseases. Although long-read sequencing provides accurate full-length sequences and methylation of TRs, there is still a need for computational methods to profile TRs across the genome. Here we introduce the Tandem Repeat Genotyping Tool (TRGT) and an accompanying TR database. TRGT determines the consensus sequences and methylation levels of specified TRs from PacBio HiFi sequencing data. It also reports reads that support each repeat allele. These reads can be subsequently visualized with a companion TR visualization tool. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 98.38%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all expansions while also identifying methylation signals and mosaicism and providing finer repeat length resolution than existing methods. Additionally, we released a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes.
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Affiliation(s)
| | - Adam English
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Harriet Dashnow
- Departments of Human Genetics and Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Tom Mokveld
- Pacific Biosciences of California, Menlo Park, CA, USA
| | | | | | | | - Matt C Danzi
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Warren A Cheung
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Chengpeng Bi
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Emily Farrow
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Aaron Wenger
- Pacific Biosciences of California, Menlo Park, CA, USA
| | - Khi Pin Chua
- Pacific Biosciences of California, Menlo Park, CA, USA
| | - Verónica Martínez-Cerdeño
- Institute for Pediatric Regenerative Medicine, Shriner's Hospital for Children and UC Davis School of Medicine, Sacramento, CA, USA
- Department of Pathology & Laboratory Medicine, UC Davis School of Medicine, Sacramento, CA, USA
- MIND Institute, UC Davis School of Medicine, Sacramento, CA, USA
| | - Trevor D Bartley
- Institute for Pediatric Regenerative Medicine, Shriner's Hospital for Children and UC Davis School of Medicine, Sacramento, CA, USA
- Department of Pathology & Laboratory Medicine, UC Davis School of Medicine, Sacramento, CA, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - David L Nelson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Stephan Zuchner
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tomi Pastinen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Aaron R Quinlan
- Departments of Human Genetics and Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
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50
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Hong C, Liu M, Wojdyla DM, Hickey J, Pencina M, Henao R. Trans-Balance: Reducing demographic disparity for prediction models in the presence of class imbalance. J Biomed Inform 2024; 149:104532. [PMID: 38070817 PMCID: PMC10850917 DOI: 10.1016/j.jbi.2023.104532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 10/21/2023] [Accepted: 10/28/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Risk prediction, including early disease detection, prevention, and intervention, is essential to precision medicine. However, systematic bias in risk estimation caused by heterogeneity across different demographic groups can lead to inappropriate or misinformed treatment decisions. In addition, low incidence (class-imbalance) outcomes negatively impact the classification performance of many standard learning algorithms which further exacerbates the racial disparity issues. Therefore, it is crucial to improve the performance of statistical and machine learning models in underrepresented populations in the presence of heavy class imbalance. METHOD To address demographic disparity in the presence of class imbalance, we develop a novel framework, Trans-Balance, by leveraging recent advances in imbalance learning, transfer learning, and federated learning. We consider a practical setting where data from multiple sites are stored locally under privacy constraints. RESULTS We show that the proposed Trans-Balance framework improves upon existing approaches by explicitly accounting for heterogeneity across demographic subgroups and cohorts. We demonstrate the feasibility and validity of our methods through numerical experiments and a real application to a multi-cohort study with data from participants of four large, NIH-funded cohorts for stroke risk prediction. CONCLUSION Our findings indicate that the Trans-Balance approach significantly improves predictive performance, especially in scenarios marked by severe class imbalance and demographic disparity. Given its versatility and effectiveness, Trans-Balance offers a valuable contribution to enhancing risk prediction in biomedical research and related fields.
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Affiliation(s)
- Chuan Hong
- Duke University, Department of Biostatistics and Bioinformatics, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA.
| | - Molei Liu
- Columbia University, Department of Biostatistics, New York, NY, USA
| | | | - Jimmy Hickey
- North Carolina State University, Department of Statistics, Raleigh, NC, USA
| | - Michael Pencina
- Duke University, Department of Biostatistics and Bioinformatics, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA
| | - Ricardo Henao
- Duke University, Department of Biostatistics and Bioinformatics, Durham, NC, USA; Duke Clinical Research Institute, Durham, NC, USA
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