Simulation study of autoregulation responses of peripheral circulation to systemic pulsatility

Frequency domain analysis and clinical outcomes of pulsatile and non-pulsatile blood flow energy during cardiopulmonary bypass

INTRODUCTION

The superiority of pulsatile perfusion during cardiopulmonary bypass remains controversial. We analyzed the frequency-domain characteristics and organ protection of pulsatile and nonpulsatile flow in adult patients with valvular disease.

METHODS

EEP and SHE were used to calculate blood flow energy in 60 patients. The Fast Fourier Transform was employed to analyze the power spectral density and power density ratio (Rvpd) of flow energy. Changes in endothelin-1, nitric oxide, interleukin-6,10, tumor necrotic factor, S100β, NSE, blood and urinary β2-microglobulin levels were investigated to assess the endothelial function, inflammatory reaction, kidney and brain injury during CPB.

RESULTS

EEP and SHE in PP group at each time point were 1.52-1.62 times and 2.03-2.22 times higher respectively compared with NP group. Power spectral density analysis demonstrated that the blood flow energy frequencies in each group were all within 40 Hz and the low frequency energy (0-5 Hz) was dominant in physiological perfusion (>90%). The energy ratio of 0-5 Hz at radial artery was significantly decreased compared with that of post arterial filter in PP (81% vs 64%) and NP (63% vs 37%) group. The power density ratio (Rvpd) was higher than that of NP in all frequency ranges at the radial artery (9.51 vs 4.68 vs 3.59) and arterial filter (3.87 vs 2.69 vs 2.38). The S100β, NSE Urinary and plasma β2-microglobulin level were significantly increased at 6 and 24 hours after surgery in two group, and significantly higher in group NP.

CONCLUSION

PP provided more energy than NP. The proportion of low frequency energy in the pulsatile or nonpulsatile flow is significantly reduced. The low-frequency energy is significantly attenuated during conduction to peripheral tissues in nonpulsatile flow. The surplus pulsatile energy influences the secretion of endothelial and inflammatory factors, and demonstrate better cerebral and kidney protective effect at the biological marker level.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.

Biomedical signal and image processing

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.