Cardiometabolic risk factors, such as smoking, age, hypertension, diabetes, obesity, hyperlipidemia etc., accelerate the occurrence of cardiovascular events and metabolic disease. On the one hand, early detection and possible modification of such factors may decelerate the progress or change the natural course of the disease. On the other hand, detailed characterization of the disease and its complications may facilitate the buildup of knowledge and lead to new ways for objective diagnosis and therapy assessment. Current and emerging technologies for such purposes, include physical (e.g. blood pressure, heart rate, blood oxygenation etc.) and electrochemical (e.g. glucose sensors) sensors, as well as, imaging techniques for the assessment of the cardiovascular system and related soft tissues/organs (e.g. skin, adipose tissue, skeletal muscle, liver, pancreas, kidneys etc.). Development of novel sensing and imaging techniques may further facilitate the convenient and objective assessment of cardiometabolic risk and relevant diseases by means of measurable and objective biomarkers with applications not only in the research but also in the clinical setting.
Over the last decade, the use of sensors revolutionized the way of physiological monitoring. Even if not offering direct visualizations of the tissue under examination, sensors offer longitudinal and low-cost monitoring of disease-induced tissue changes in a highly convenient manner. Furthermore, both traditional (e.g. ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine techniques) and modern (e.g. high-resolution ultrasound, novel magnetic resonance sequences, optical, optoacoustics etc.) imaging has greatly contributed to a better understanding of cardiovascular and metabolic disease. Furthermore, advanced molecular approaches have succeeded to capture specific pathophysiological aspects of cardiometabolic diseases, such as inflammation, tissue remodeling and regeneration in vivo. However, the wide dissemination for point-of-care use or translation of these approaches into the clinical theater most frequently has either failed or was not even attempted. Nowadays, combination of such approaches with other technologies, such as medical robotics, machine learning and advanced analytics may well lead to the formation of synthetic frameworks with high adaptability to multiple environments (e.g. laboratory, home, hospital). In this light, a non-negotiable yet realistic goal could therefore be to boost the wide dissemination and clinical translation of such approaches: a step affecting millions of current and future patient populations with cardiometabolic diseases.
Specific themes we would like contributors to address include:
1) Sensor- and imaging-based biomarkers of cardiometabolic diseases.
2) Novel clinical biomarkers of cardiovascular and metabolic disease.
3) Novel sensing (e.g. wearables, smartphone-based, printed, lab-on-a-chip etc) approaches.
4) Advanced molecular imaging techniques with cardiometabolic applications.
5) Novel molecular imaging probes with translational potential.
6) Modern technologies (e.g. artificial intelligence, biomedical robotics).
7) Clinical evaluation of medical sensors, imaging techniques and methods.
Cardiometabolic risk factors, such as smoking, age, hypertension, diabetes, obesity, hyperlipidemia etc., accelerate the occurrence of cardiovascular events and metabolic disease. On the one hand, early detection and possible modification of such factors may decelerate the progress or change the natural course of the disease. On the other hand, detailed characterization of the disease and its complications may facilitate the buildup of knowledge and lead to new ways for objective diagnosis and therapy assessment. Current and emerging technologies for such purposes, include physical (e.g. blood pressure, heart rate, blood oxygenation etc.) and electrochemical (e.g. glucose sensors) sensors, as well as, imaging techniques for the assessment of the cardiovascular system and related soft tissues/organs (e.g. skin, adipose tissue, skeletal muscle, liver, pancreas, kidneys etc.). Development of novel sensing and imaging techniques may further facilitate the convenient and objective assessment of cardiometabolic risk and relevant diseases by means of measurable and objective biomarkers with applications not only in the research but also in the clinical setting.
Over the last decade, the use of sensors revolutionized the way of physiological monitoring. Even if not offering direct visualizations of the tissue under examination, sensors offer longitudinal and low-cost monitoring of disease-induced tissue changes in a highly convenient manner. Furthermore, both traditional (e.g. ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine techniques) and modern (e.g. high-resolution ultrasound, novel magnetic resonance sequences, optical, optoacoustics etc.) imaging has greatly contributed to a better understanding of cardiovascular and metabolic disease. Furthermore, advanced molecular approaches have succeeded to capture specific pathophysiological aspects of cardiometabolic diseases, such as inflammation, tissue remodeling and regeneration in vivo. However, the wide dissemination for point-of-care use or translation of these approaches into the clinical theater most frequently has either failed or was not even attempted. Nowadays, combination of such approaches with other technologies, such as medical robotics, machine learning and advanced analytics may well lead to the formation of synthetic frameworks with high adaptability to multiple environments (e.g. laboratory, home, hospital). In this light, a non-negotiable yet realistic goal could therefore be to boost the wide dissemination and clinical translation of such approaches: a step affecting millions of current and future patient populations with cardiometabolic diseases.
Specific themes we would like contributors to address include:
1) Sensor- and imaging-based biomarkers of cardiometabolic diseases.
2) Novel clinical biomarkers of cardiovascular and metabolic disease.
3) Novel sensing (e.g. wearables, smartphone-based, printed, lab-on-a-chip etc) approaches.
4) Advanced molecular imaging techniques with cardiometabolic applications.
5) Novel molecular imaging probes with translational potential.
6) Modern technologies (e.g. artificial intelligence, biomedical robotics).
7) Clinical evaluation of medical sensors, imaging techniques and methods.
