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Gombert-Labedens M, Alzueta E, Perez-Amparan E, Yuksel D, Kiss O, de Zambotti M, Simon K, Zhang J, Shuster A, Morehouse A, Pena AA, Mednick S, Baker FC. Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting. J Biol Rhythms 2024; 39:331-350. [PMID: 38767963 PMCID: PMC11294004 DOI: 10.1177/07487304241247893] [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: 05/22/2024]
Abstract
The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.
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Affiliation(s)
| | - Elisabet Alzueta
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | | | - Dilara Yuksel
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Orsolya Kiss
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | | | - Katharine Simon
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Jing Zhang
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Alessandra Shuster
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Allison Morehouse
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | | | - Sara Mednick
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Fiona C. Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa
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2
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Song YM, Jeong J, de Los Reyes AA, Lim D, Cho CH, Yeom JW, Lee T, Lee JB, Lee HJ, Kim JK. Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data. EBioMedicine 2024; 103:105094. [PMID: 38579366 PMCID: PMC11002811 DOI: 10.1016/j.ebiom.2024.105094] [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: 10/02/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.
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Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Jaegwon Jeong
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Aurelio A de Los Reyes
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
| | - Dongju Lim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Chul-Hyun Cho
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Ji Won Yeom
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Taek Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Jung-Been Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Heon-Jeong Lee
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea.
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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3
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Li P, Kim JK. Circadian regulation of sinoatrial nodal cell pacemaking function: Dissecting the roles of autonomic control, body temperature, and local circadian rhythmicity. PLoS Comput Biol 2024; 20:e1011907. [PMID: 38408116 PMCID: PMC10927146 DOI: 10.1371/journal.pcbi.1011907] [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: 08/09/2023] [Revised: 03/11/2024] [Accepted: 02/12/2024] [Indexed: 02/28/2024] Open
Abstract
Strong circadian (~24h) rhythms in heart rate (HR) are critical for flexible regulation of cardiac pacemaking function throughout the day. While this circadian flexibility in HR is sustained in diverse conditions, it declines with age, accompanied by reduced maximal HR performance. The intricate regulation of circadian HR involves the orchestration of the autonomic nervous system (ANS), circadian rhythms of body temperature (CRBT), and local circadian rhythmicity (LCR), which has not been fully understood. Here, we developed a mathematical model describing ANS, CRBT, and LCR in sinoatrial nodal cells (SANC) that accurately captures distinct circadian patterns in adult and aged mice. Our model underscores how the alliance among ANS, CRBT, and LCR achieves circadian flexibility to cover a wide range of firing rates in SANC, performance to achieve maximal firing rates, while preserving robustness to generate rhythmic firing patterns irrespective of external conditions. Specifically, while ANS dominates in promoting SANC flexibility and performance, CRBT and LCR act as primary and secondary boosters, respectively, to further enhance SANC flexibility and performance. Disruption of this alliance with age results in impaired SANC flexibility and performance, but not robustness. This unexpected outcome is primarily attributed to the age-related reduction in parasympathetic activities, which maintains SANC robustness while compromising flexibility. Our work sheds light on the critical alliance of ANS, CRBT, and LCR in regulating time-of-day cardiac pacemaking function and dysfunction, offering insights into novel therapeutic targets for the prevention and treatment of cardiac arrhythmias.
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Affiliation(s)
- Pan Li
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
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4
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Lévi FA, Okyar A, Hadadi E, Innominato PF, Ballesta A. Circadian Regulation of Drug Responses: Toward Sex-Specific and Personalized Chronotherapy. Annu Rev Pharmacol Toxicol 2024; 64:89-114. [PMID: 37722720 DOI: 10.1146/annurev-pharmtox-051920-095416] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
Today's challenge for precision medicine involves the integration of the impact of molecular clocks on drug pharmacokinetics, toxicity, and efficacy toward personalized chronotherapy. Meaningful improvements of tolerability and/or efficacy of medications through proper administration timing have been confirmed over the past decade for immunotherapy and chemotherapy against cancer, as well as for commonly used pharmacological agents in cardiovascular, metabolic, inflammatory, and neurological conditions. Experimental and human studies have recently revealed sexually dimorphic circadian drug responses. Dedicated randomized clinical trials should now aim to issue personalized circadian timing recommendations for daily medical practice, integrating innovative technologies for remote longitudinal monitoring of circadian metrics, statistical prediction of molecular clock function from single-timepoint biopsies, and multiscale biorhythmic mathematical modelling. Importantly, chronofit patients with a robust circadian function, who would benefit most from personalized chronotherapy, need to be identified. Conversely, nonchronofit patients could benefit from the emerging pharmacological class of chronobiotics targeting the circadian clock.
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Affiliation(s)
- Francis A Lévi
- Chronotherapy, Cancers and Transplantation Research Unit, Faculty of Medicine, Paris-Saclay University, Villejuif, France;
- Gastrointestinal and General Oncology Service, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif, France
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Alper Okyar
- Faculty of Pharmacy, Department of Pharmacology, Istanbul University, Beyazit-Istanbul, Turkey
| | - Eva Hadadi
- Laboratory of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
- Laboratory for Myeloid Cell Immunology, Center for Inflammation Research VIB, Zwijnaarde, Belgium
| | - Pasquale F Innominato
- Oncology Department, Ysbyty Gwynedd Hospital, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
- Warwick Medical School and Cancer Research Centre, University of Warwick, Coventry, United Kingdom
| | - Annabelle Ballesta
- Inserm Unit 900, Cancer Systems Pharmacology, Institut Curie, MINES ParisTech CBIO-Centre for Computational Biology, PSL Research University, Saint-Cloud, France
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5
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Lee MP, Hoang K, Park S, Song YM, Joo EY, Chang W, Kim JH, Kim JK. Imputing missing sleep data from wearables with neural networks in real-world settings. Sleep 2024; 47:zsad266. [PMID: 37819273 DOI: 10.1093/sleep/zsad266] [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: 05/15/2023] [Revised: 09/12/2023] [Indexed: 10/13/2023] Open
Abstract
Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.
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Affiliation(s)
- Minki P Lee
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Kien Hoang
- Institute of Mathematics, EPFL, Lausanne, Switzerland
| | - Sungkyu Park
- Department of Artificial Intelligence Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Chang
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Jee Hyun Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
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Diaz-Ramos RE, Noriega I, Trejo LA, Stroulia E, Cao B. Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study. JMIR Res Protoc 2023; 12:e48210. [PMID: 37955959 PMCID: PMC10682927 DOI: 10.2196/48210] [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: 04/15/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Early identification of mental disorder symptoms is crucial for timely treatment and reduction of recurring symptoms and disabilities. A tool to help individuals recognize warning signs is important. We posit that such a tool would have to rely on longitudinal analysis of patterns and trends in the individual's daily activities and mood, which can now be captured through data from wearable activity trackers, speech recordings from mobile devices, and the individual's own description of their mental state. In this paper, we describe such a tool developed by our team to detect early signs of depression, anxiety, and stress. OBJECTIVE This study aims to examine three questions about the effectiveness of machine learning models constructed based on multimodal data from wearables, speech, and self-reports: (1) How does speech about issues of personal context differ from speech while reading a neutral text, what type of speech data are more helpful in detecting mental health indicators, and how is the quality of the machine learning models influenced by multilanguage data? (2) Does accuracy improve with longitudinal data collection and how, and what are the most important features? and (3) How do personalized machine learning models compare against population-level models? METHODS We collect longitudinal data to aid machine learning in accurately identifying patterns of mental disorder symptoms. We developed an app that collects voice, physiological, and activity data. Physiological and activity data are provided by a variety of off-the-shelf fitness trackers, that record steps, active minutes, duration of sleeping stages (rapid eye movement, deep, and light sleep), calories consumed, distance walked, heart rate, and speed. We also collect voice recordings of users reading specific texts and answering open-ended questions chosen randomly from a set of questions without repetition. Finally, the app collects users' answers to the Depression, Anxiety, and Stress Scale. The collected data from wearable devices and voice recordings will be used to train machine learning models to predict the levels of anxiety, stress, and depression in participants. RESULTS The study is ongoing, and data collection will be completed by November 2023. We expect to recruit at least 50 participants attending 2 major universities (in Canada and Mexico) fluent in English or Spanish. The study will include participants aged between 18 and 35 years, with no communication disorders, acute neurological diseases, or history of brain damage. Data collection complied with ethical and privacy requirements. CONCLUSIONS The study aims to advance personalized machine learning for mental health; generate a data set to predict Depression, Anxiety, and Stress Scale results; and deploy a framework for early detection of depression, anxiety, and stress. Our long-term goal is to develop a noninvasive and objective method for collecting mental health data and promptly detecting mental disorder symptoms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/48210.
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Affiliation(s)
- Ramon E Diaz-Ramos
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Isabella Noriega
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
| | - Luis A Trejo
- School of Engineering and Sciences, Tecnologico de Monterrey, Atizapan, Mexico
| | - Eleni Stroulia
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
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7
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Song YM, Choi SJ, Park SH, Lee SJ, Joo EY, Kim JK. A real-time, personalized sleep intervention using mathematical modeling and wearable devices. Sleep 2023; 46:zsad179. [PMID: 37422720 DOI: 10.1093/sleep/zsad179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/03/2023] [Indexed: 07/10/2023] Open
Abstract
The prevalence of artificial light exposure has enabled us to be active any time of the day or night, leading to the need for high alertness outside of traditional daytime hours. To address this need, we developed a personalized sleep intervention framework that analyzes real-world sleep-wake patterns obtained from wearable devices to maximize alertness during specific target periods. Our framework utilizes a mathematical model that tracks the dynamic sleep pressure and circadian rhythm based on the user's sleep history. In this way, the model accurately predicts real-time alertness, even for shift workers with complex sleep and work schedules (N = 71, t = 13~21 days). This allowed us to discover a new sleep-wake pattern called the adaptive circadian split sleep, which incorporates a main sleep period and a late nap to enable high alertness during both work and non-work periods of shift workers. We further developed a mobile application that integrates this framework to recommend practical, personalized sleep schedules for individual users to maximize their alertness during a targeted activity time based on their desired sleep onset and available sleep duration. This can reduce the risk of errors for those who require high alertness during nontraditional activity times and improve the health and quality of life for those leading shift work-like lifestyles.
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Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Su Jung Choi
- Graduate School of Clinical Nursing Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Se Ho Park
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, USA
| | - Soo Jin Lee
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
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Rosch M, Gutowski T, Baehr M, Eggert J, Gottfried K, Gundler C, Nürnberg S, Langebrake C, Dadkhah A. Development of an immediate release excipient composition for 3D printing via direct powder extrusion in a hospital. Int J Pharm 2023; 643:123218. [PMID: 37467818 DOI: 10.1016/j.ijpharm.2023.123218] [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: 05/19/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/21/2023]
Abstract
3D printing offers the possibility to prepare personalized tablets on demand, making it an intriguing technology for hospital pharmacies. For the implementation of 3D-printed tablets into the digital Closed Loop Medication Management system, the required tablet formulation and development of the manufacturing process as well as the pharmaceutical validation were conducted. The goal of the formulation development was to enable an optimal printing process and rapid dissolution of the printed tablets for the selected model drugs Levodopa/Carbidopa. The 3D printed tablets were prepared by direct powder extrusion. Printability, thermal properties, disintegration, dissolution, physical properties and storage stability were investigated by employing analytical methods such as HPLC-UV, DSC and TGA. The developed formulation shows a high dose accuracy and an immediate drug release for Levodopa. In addition, the tablets exhibit high crushing strength and very low friability. Unfortunately, Carbidopa did not tolerate the printing process. This is the first study to develop an immediate release excipient composition via direct powder extrusion in a hospital pharmacy setting. The developed process is suitable for the implementation in Closed-Loop Medication Management systems in hospital pharmacies and could therefore contribute to medication safety.
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Affiliation(s)
- Moritz Rosch
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Gutowski
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael Baehr
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Eggert
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Gottfried
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christopher Gundler
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sylvia Nürnberg
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claudia Langebrake
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Adrin Dadkhah
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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Kim DW, Mayer C, Lee MP, Choi SW, Tewari M, Forger DB. Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data. J R Soc Interface 2023; 20:20230030. [PMID: 37608712 PMCID: PMC10445022 DOI: 10.1098/rsif.2023.0030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 07/31/2023] [Indexed: 08/24/2023] Open
Abstract
Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.
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Affiliation(s)
- Dae Wook Kim
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Minki P. Lee
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Won Choi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Muneesh Tewari
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel B. Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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10
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Kim DW, Byun JM, Lee JO, Kim JK, Koh Y. Chemotherapy delivery time affects treatment outcomes of female patients with diffuse large B cell lymphoma. JCI Insight 2023; 8:164767. [PMID: 36512421 PMCID: PMC9977288 DOI: 10.1172/jci.insight.164767] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUNDChronotherapy is a drug intervention at specific times of the day to optimize efficacy and minimize adverse effects. Its value in hematologic malignancy remains to be explored, in particular in adult patients.METHODSWe performed chronotherapeutic analysis using 2 cohorts of patients with diffuse large B cell lymphoma (DLBCL) undergoing chemotherapy with a dichotomized schedule (morning or afternoon). The effect of a morning or afternoon schedule of rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) on survival and drug tolerability was evaluated in a survival cohort (n = 210) and an adverse event cohort (n = 129), respectively. Analysis of about 14,000 healthy individuals followed to identify the circadian variation in hematologic parameters.RESULTSBoth progression-free survival (PFS) and overall survival (OS) of female, but not male, patients were significantly shorter when patients received chemotherapy mostly in the morning (PFS HR 0.357, P = 0.033; and OS HR 0.141, P = 0.032). The dose intensity was reduced in female patients treated in the morning (cyclophosphamide 10%, P = 0.002; doxorubicin 8%, P = 0.002; and rituximab 7%, P = 0.003). This was mainly attributable to infection and neutropenic fever: female patients treated in the morning had a higher incidence of infections (16.7% vs. 2.4%) and febrile neutropenia (20.8% vs. 9.8%) as compared with those treated in the afternoon. The sex-specific chronotherapeutic effects can be explained by the larger daily fluctuation of circulating leukocytes and neutrophils in female than in male patients.CONCLUSIONIn female DLBCL patients, R-CHOP treatment in the afternoon can reduce toxicity while it improves efficacy and survival outcome.FUNDINGNational Research Foundation of Korea (NRF) grant funded by the Korean government (grant number NRF-2021R1A4A2001553), Institute for Basic Science IBS-R029-C3, and Human Frontiers Science Program Organization Grant RGY0063/2017.
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Affiliation(s)
- Dae Wook Kim
- Department of Mathematical Sciences, KAIST, Daejeon, South Korea.,Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | - Ja Min Byun
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jeong-Ok Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, South Korea.,Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | - Youngil Koh
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
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11
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Joshi K, Das M, Sarma A, Arora MK, SInghal M, Kumar B. Insight on Cardiac Chronobiology and Latest Developments of Chronotherapeutic Antihypertensive Interventions for Better Clinical Outcomes. Curr Hypertens Rev 2023; 19:106-122. [PMID: 36624649 DOI: 10.2174/1573402119666230109142156] [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: 04/23/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 01/11/2023]
Abstract
Cardiac circadian rhythms are an important regulator of body functions, including cardiac activities and blood pressure. Disturbance of circadian rhythm is known to trigger and aggravate various cardiovascular diseases. Thus, modulating the circadian rhythm can be used as a therapeutic approach to cardiovascular diseases. Through this work, we intend to discuss the current understanding of cardiac circadian rhythms, in terms of quantifiable parameters like BP and HR. We also elaborate on the molecular regulators and the molecular cascades along with their specific genetic aspects involved in modulating circadian rhythms, with specific reference to cardiovascular health and cardiovascular diseases. Along with this, we also presented the latest pharmacogenomic and metabolomics markers involved in chronobiological control of the cardiovascular system along with their possible utility in cardiovascular disease diagnosis and therapeutics. Finally, we reviewed the current expert opinions on chronotherapeutic approaches for utilizing the conventional as well as the new pharmacological molecules for antihypertensive chronotherapy.
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Affiliation(s)
- Kumud Joshi
- Department of Pharmacy, Lloyd Institute of Management and Technology, Greater Noida, India
| | - Madhubanti Das
- Department of Zoology, Gauhati University, Guwahati, Assam, India
| | - Anupam Sarma
- Advanced Drug Delivery Laboratory, GIPS, Girijananda Chowdhury University, Guwahati, Assam, India
| | - Mandeep K Arora
- School of Pharmacy and population health informatics, DIT University, Dehradun, India
| | - Manmohan SInghal
- School of Pharmacy and population health informatics, DIT University, Dehradun, India
| | - Bhavna Kumar
- School of Pharmacy and population health informatics, DIT University, Dehradun, India
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12
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Dose B, Yalçin M, Dries SPM, Relógio A. TimeTeller for timing health: The potential of circadian medicine to improve performance, prevent disease and optimize treatment. Front Digit Health 2023; 5:1157654. [PMID: 37153516 PMCID: PMC10155816 DOI: 10.3389/fdgth.2023.1157654] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/27/2023] [Indexed: 05/09/2023] Open
Abstract
Circadian medicine, the study of the effects of time on health and disease has seen an uprising in recent years as a means to enhance health and performance, and optimize treatment timing. Our endogenous time generating system -the circadian clock- regulates behavioural, physiological and cellular processes. Disruptions of the clock, via external factors like shift work or jet lag, or internal perturbations such as genetic alterations, are linked to an increased risk of various diseases like obesity, diabetes, cardiovascular diseases and cancer. By aligning an individual's circadian clock with optimal times for performing daily routines, physical and mental performance, and also the effectiveness of certain therapies can be improved. Despite the benefits of circadian medicine, the lack of non-invasive tools for characterizing the clock limits the potential of the field. TimeTeller is a non-invasive molecular/digital tool for the characterization of circadian rhythms and prediction of daily routines, including treatment timing, to unlock the potential of circadian medicine and implementing it in various settings. Given the multiple known and potentially yet unknown dependent health factors of individual circadian rhythms, the utility of this emerging biomarker is best exploited in data driven, personalized medicine use cases, using health information across lifestyle, care, and research settings.
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Affiliation(s)
| | - Müge Yalçin
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Institute for Systems Medicine and Faculty of Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
| | | | - Angela Relógio
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Institute for Systems Medicine and Faculty of Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
- Correspondence: Angela Relógio
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13
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Erickson ML, Wang W, Counts J, Redman LM, Parker D, Huebner JL, Dunn J, Kraus WE. Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. J Biol Rhythms 2022; 37:260-271. [PMID: 35416084 DOI: 10.1177/07487304221087068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.
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Affiliation(s)
| | - Will Wang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Julie Counts
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Leanne M Redman
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana
| | - Daniel Parker
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Janet L Huebner
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina.,Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | - William E Kraus
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina
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14
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Mathematical analysis of robustness of oscillations in models of the mammalian circadian clock. PLoS Comput Biol 2022; 18:e1008340. [PMID: 35302984 PMCID: PMC8979472 DOI: 10.1371/journal.pcbi.1008340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 04/04/2022] [Accepted: 02/25/2022] [Indexed: 11/23/2022] Open
Abstract
Circadian rhythms in a wide range of organisms are mediated by molecular mechanisms based on transcription-translation feedback. In this paper, we use bifurcation theory to explore mathematical models of genetic oscillators, based on Kim & Forger’s interpretation of the circadian clock in mammals. At the core of their models is a negative feedback loop whereby PER proteins (PER1 and PER2) bind to and inhibit their transcriptional activator, BMAL1. For oscillations to occur, the dissociation constant of the PER:BMAL1 complex, K^d, must be ≤ 0.04 nM, which is orders of magnitude smaller than a reasonable expectation of 1–10 nM for this protein complex. We relax this constraint by two modifications to Kim & Forger’s ‘single negative feedback’ (SNF) model: first, by introducing a multistep reaction chain for posttranscriptional modifications of Per mRNA and posttranslational phosphorylations of PER, and second, by replacing the first-order rate law for degradation of PER in the nucleus by a Michaelis-Menten rate law. These modifications increase the maximum allowable K^d to ~2 nM. In a third modification, we consider an alternative rate law for gene transcription to resolve an unrealistically large rate of Per2 transcription at very low concentrations of BMAL1. Additionally, we studied extensions of the SNF model to include a second negative feedback loop (involving REV-ERB) and a supplementary positive feedback loop (involving ROR). Contrary to Kim & Forger’s observations of these extended models, we find that, with our modifications, the supplementary positive feedback loop makes the oscillations more robust than observed in the models with one or two negative feedback loops. However, all three models are similarly robust when accounting for circadian rhythms (~24 h period) with K^d ≥ 1 nM. Our results provide testable predictions for future experimental studies. The circadian rhythm aligns bodily functions to the day/night cycle and is important for our health. The rhythm originates from an intracellular molecular clock mechanism that mediates rhythmic gene expression. It is long understood that transcriptional negative feedback with sufficient time delay is key to generating circadian oscillations. However, some of the most widely cited mathematical models for the circadian clock suffer from problems of parameter ‘fragilities’. That is, sustained oscillations are possible only for physically unrealistic parameter values. A recent model by Kim & Forger nicely incorporates the inhibitory binding of PER proteins to their transcription activator BMAL1, but oscillations in the Kim-Forger model require a binding affinity between PER and BMAL1 that is orders of magnitude larger than observed binding affinities of protein complexes. To rectify this problem, we make several physiologically credible modifications to the Kim-Forger model, which allow oscillations to occur with more realistic binding affinities. The modified model is further extended to explore the potential roles of supplementary feedback loops in the mammalian clock mechanism. Ultimately, accurate models of the circadian clock will provide better predictive tools for chronotherapy and chrono-pharmacology studies.
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15
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Ode KL, Shi S, Katori M, Mitsui K, Takanashi S, Oguchi R, Aoki D, Ueda HR. A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration. iScience 2022; 25:103727. [PMID: 35106471 PMCID: PMC8784328 DOI: 10.1016/j.isci.2021.103727] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 10/05/2021] [Accepted: 12/30/2021] [Indexed: 11/26/2022] Open
Abstract
Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings. An algorithm for sleep-wake classification based on arm acceleration is presented The algorithm only uses a derivative of triaxial arm acceleration (jerk) The algorithm can accurately detect temporal awake during sleep
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16
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Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness. iScience 2021; 24:103129. [PMID: 34622173 PMCID: PMC8479255 DOI: 10.1016/j.isci.2021.103129] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/09/2021] [Accepted: 09/10/2021] [Indexed: 11/29/2022] Open
Abstract
Shift workers and many other groups experience irregular sleep-wake patterns. This can induce excessive daytime sleepiness that decreases productivity and elevates the risk of accidents. However, the degree of daytime sleepiness is not correlated with standard sleep parameters like total sleep time, suggesting other factors are involved. Here, we analyze real-world sleep-wake patterns of shift workers measured with wearables by developing a computational package that simulates homeostatic sleep pressure – physiological need for sleep – and the circadian rhythm. This reveals that shift workers who align sleep-wake patterns with their circadian rhythm have lower daytime sleepiness, even if they sleep less. The alignment, quantified by the sleep parameter, circadian sleep sufficiency, can be increased by dynamically adjusting daily sleep durations according to varying bedtimes. Our computational package provides flexible and personalized real-time sleep-wake patterns for individuals to reduce their daytime sleepiness and could be used with wearables to develop smart alarms. Sleep-wake patterns measured by wearables are analyzed with a mathematical model A new sleep parameter CSS measures the circadian alignment of sleep-wake patterns Sleep-wake patterns more aligned with circadian rhythm decrease daytime sleepiness Our computational package calculates the CSS to provide personalized sleep schedules
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17
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Hesse J, Martinelli J, Aboumanify O, Ballesta A, Relógio A. A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer. Comput Struct Biotechnol J 2021; 19:5170-5183. [PMID: 34630937 PMCID: PMC8477139 DOI: 10.1016/j.csbj.2021.08.051] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/21/2022] Open
Abstract
Scheduling anticancer drug administration over 24 h may critically impact treatment success in a patient-specific manner. Here, we address personalization of treatment timing using a novel mathematical model of irinotecan cellular pharmacokinetics and -dynamics linked to a representation of the core clock and predict treatment toxicity in a colorectal cancer (CRC) cellular model. The mathematical model is fitted to three different scenarios: mouse liver, where the drug metabolism mainly occurs, and two human colorectal cancer cell lines representing an in vitro experimental system for human colorectal cancer progression. Our model successfully recapitulates quantitative circadian datasets of mRNA and protein expression together with timing-dependent irinotecan cytotoxicity data. The model also discriminates time-dependent toxicity between the different cells, suggesting that treatment can be optimized according to their cellular clock. Our results show that the time-dependent degradation of the protein mediating irinotecan activation, as well as an oscillation in the death rate may play an important role in the circadian variations of drug toxicity. In the future, this model can be used to support personalized treatment scheduling by predicting optimal drug timing based on the patient's gene expression profile.
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Affiliation(s)
- Janina Hesse
- Institute for Systems Medicine, Department of Human Medicine, MSH Medical School Hamburg - University of Applied Sciences and Medical University, Hamburg 20457, Germany.,Institute for Theoretical Biology (ITB), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Julien Martinelli
- INSERM U900, Saint-Cloud, France, Institut Curie, Saint Cloud, France, Paris Saclay University, France, MINES ParisTech, CBIO - Centre for Computational Biology, PSL Research University, Paris, France.,UPR 'Chronotherapy, Cancers and Transplantation', Faculty of Medicine, Paris Saclay University, Campus CNRS, 7 rue Guy Moquet, 94800 Villejuif, France.,Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France
| | - Ouda Aboumanify
- Institute for Theoretical Biology (ITB), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany.,Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumor Immunology, Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin
| | - Annabelle Ballesta
- INSERM U900, Saint-Cloud, France, Institut Curie, Saint Cloud, France, Paris Saclay University, France, MINES ParisTech, CBIO - Centre for Computational Biology, PSL Research University, Paris, France.,UPR 'Chronotherapy, Cancers and Transplantation', Faculty of Medicine, Paris Saclay University, Campus CNRS, 7 rue Guy Moquet, 94800 Villejuif, France
| | - Angela Relógio
- Institute for Systems Medicine, Department of Human Medicine, MSH Medical School Hamburg - University of Applied Sciences and Medical University, Hamburg 20457, Germany.,Institute for Theoretical Biology (ITB), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany.,Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumor Immunology, Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin
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18
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Martinelli J, Dulong S, Li XM, Teboul M, Soliman S, Lévi F, Fages F, Ballesta A. Model learning to identify systemic regulators of the peripheral circadian clock. Bioinformatics 2021; 37:i401-i409. [PMID: 34252929 PMCID: PMC8557835 DOI: 10.1093/bioinformatics/btab297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Personalized medicine aims at providing patient-tailored therapeutics based on multi-type data toward improved treatment outcomes. Chronotherapy that consists in adapting drug administration to the patient's circadian rhythms may be improved by such approach. Recent clinical studies demonstrated large variability in patients' circadian coordination and optimal drug timing. Consequently, new eHealth platforms allow the monitoring of circadian biomarkers in individual patients through wearable technologies (rest-activity, body temperature), blood or salivary samples (melatonin, cortisol) and daily questionnaires (food intake, symptoms). A current clinical challenge involves designing a methodology predicting from circadian biomarkers the patient peripheral circadian clocks and associated optimal drug timing. The mammalian circadian timing system being largely conserved between mouse and humans yet with phase opposition, the study was developed using available mouse datasets. RESULTS We investigated at the molecular scale the influence of systemic regulators (e.g. temperature, hormones) on peripheral clocks, through a model learning approach involving systems biology models based on ordinary differential equations. Using as prior knowledge our existing circadian clock model, we derived an approximation for the action of systemic regulators on the expression of three core-clock genes: Bmal1, Per2 and Rev-Erbα. These time profiles were then fitted with a population of models, based on linear regression. Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. This agreed with biological knowledge on temperature-dependent control of Per2 transcription. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background. AVAILABILITY AND IMPLEMENTATION https://gitlab.inria.fr/julmarti/model-learning-mb21eccb. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Julien Martinelli
- INSERM UMR-S 900, Institut Curie, MINES ParisTech CBIO, PSL Research University, 92210 Saint-Cloud, France.,Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France
| | - Sandrine Dulong
- UPR "Chronotherapy, Cancers and Transplantation", Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France
| | - Xiao-Mei Li
- UPR "Chronotherapy, Cancers and Transplantation", Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France
| | - Michèle Teboul
- Côte d'Azur University, CNRS, INSERM, iBV, Nice 06000, France
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France
| | - Francis Lévi
- UPR "Chronotherapy, Cancers and Transplantation", Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France.,Hepato-Biliary Center, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif 94800, France
| | - François Fages
- Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France
| | - Annabelle Ballesta
- INSERM UMR-S 900, Institut Curie, MINES ParisTech CBIO, PSL Research University, 92210 Saint-Cloud, France
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19
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Violaris IG, Kalafatakis K, Zavala E, Tsoulos IG, Lampros T, Lightman SL, Tsipouras MG, Giannakeas N, Tzallas A, Russell GM. Modelling Hydrocortisone Pharmacokinetics on a Subcutaneous Pulsatile Infusion Replacement Strategy in Patients with Adrenocortical Insufficiency. Pharmaceutics 2021; 13:pharmaceutics13060769. [PMID: 34064165 PMCID: PMC8224376 DOI: 10.3390/pharmaceutics13060769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 11/16/2022] Open
Abstract
In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.
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Affiliation(s)
- Ioannis G. Violaris
- Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece; (I.G.V.); (M.G.T.)
| | - Konstantinos Kalafatakis
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
- Correspondence: or ; Tel.: +30-2107288264
| | - Eder Zavala
- Centre for Systems Modelling and Quantitative Biomedicine, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Ioannis G. Tsoulos
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Theodoros Lampros
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Stafford L. Lightman
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
| | - Markos G. Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece; (I.G.V.); (M.G.T.)
| | - Nikolaos Giannakeas
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Alexandros Tzallas
- Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, 47100 Arta, Greece; (I.G.T.); (T.L.); (N.G.); (A.T.)
| | - Georgina M. Russell
- Laboratories of Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol BS1 3NY, UK; (S.L.L.); (G.M.R.)
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20
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Zavala E, Voliotis M, Zerenner T, Tabak J, Walker JJ, Li XF, Terry JR, Lightman SL, O'Byrne K, Tsaneva-Atanasova K. Dynamic Hormone Control of Stress and Fertility. Front Physiol 2020; 11:598845. [PMID: 33329048 PMCID: PMC7718016 DOI: 10.3389/fphys.2020.598845] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 10/20/2020] [Indexed: 12/13/2022] Open
Abstract
Neuroendocrine axes display a remarkable diversity of dynamic signaling processes relaying information between the brain, endocrine glands, and peripheral target tissues. These dynamic processes include oscillations, elastic responses to perturbations, and plastic long term changes observed from the cellular to the systems level. While small transient dynamic changes can be considered physiological, larger and longer disruptions are common in pathological scenarios involving more than one neuroendocrine axes, suggesting that a robust control of hormone dynamics would require the coordination of multiple neuroendocrine clocks. The idea of apparently different axes being in fact exquisitely intertwined through neuroendocrine signals can be investigated in the regulation of stress and fertility. The stress response and the reproductive cycle are controlled by the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Hypothalamic-Pituitary-Gonadal (HPG) axis, respectively. Despite the evidence surrounding the effects of stress on fertility, as well as of the reproductive cycle on stress hormone dynamics, there is a limited understanding on how perturbations in one neuroendocrine axis propagate to the other. We hypothesize that the links between stress and fertility can be better understood by considering the HPA and HPG axes as coupled systems. In this manuscript, we investigate neuroendocrine rhythms associated to the stress response and reproduction by mathematically modeling the HPA and HPG axes as a network of interlocked oscillators. We postulate a network architecture based on physiological data and use the model to predict responses to stress perturbations under different hormonal contexts: normal physiological, gonadectomy, hormone replacement with estradiol or corticosterone (CORT), and high excess CORT (hiCORT) similar to hypercortisolism in humans. We validate our model predictions against experiments in rodents, and show how the dynamic responses of these endocrine axes are consistent with our postulated network architecture. Importantly, our model also predicts the conditions that ensure robustness of fertility to stress perturbations, and how chronodisruptions in glucocorticoid hormones can affect the reproductive axis' ability to withstand stress. This insight is key to understand how chronodisruption leads to disease, and to design interventions to restore normal rhythmicity and health.
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Affiliation(s)
- Eder Zavala
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Margaritis Voliotis
- EPSRC Centre for Predictive Modelling in Healthcare, Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Tanja Zerenner
- EPSRC Centre for Predictive Modelling in Healthcare, Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Joël Tabak
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom
| | - Jamie J. Walker
- EPSRC Centre for Predictive Modelling in Healthcare, Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Henry Wellcome Laboratory for Integrative Neuroscience and Endocrinology, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Xiao Feng Li
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Stafford L. Lightman
- Henry Wellcome Laboratory for Integrative Neuroscience and Endocrinology, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kevin O'Byrne
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Krasimira Tsaneva-Atanasova
- EPSRC Centre for Predictive Modelling in Healthcare, Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
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Hesse J, Malhan D, Yalҫin M, Aboumanify O, Basti A, Relógio A. An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling. Cancers (Basel) 2020; 12:cancers12113103. [PMID: 33114254 PMCID: PMC7690897 DOI: 10.3390/cancers12113103] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/15/2020] [Accepted: 10/20/2020] [Indexed: 02/07/2023] Open
Abstract
Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.
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Affiliation(s)
- Janina Hesse
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
| | - Deeksha Malhan
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
| | - Müge Yalҫin
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
| | - Ouda Aboumanify
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
| | - Alireza Basti
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
| | - Angela Relógio
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany; (J.H.); (D.M.); (M.Y.); (O.A.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
- Department of Human Medicine, Institute for Systems Medicine and Bioinformatics, MSH Medical School Hamburg—University of Applied Sciences and Medical University, 20457 Hamburg, Germany
- Correspondence: or
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