Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia due to inadequate insulin secretion, resistance, or both. The cardiovascular complications of DM are the leading cause of morbidity and mortality in diabetic patients. There are three major types of pathophysiologic cardiac remodeling including coronary artery atherosclerosis, cardiac autonomic neuropathy, and DM cardiomyopathy in patients with DM. DM cardiomyopathy is a distinct cardiomyopathy characterized by myocardial dysfunction in the absence of coronary artery disease, hypertension, and valvular heart disease. Cardiac fibrosis, defined as the excessive deposition of extracellular matrix (ECM) proteins, is a hallmark of DM cardiomyopathy. The pathophysiology of cardiac fibrosis in DM cardiomyopathy is complex and involves multiple cellular and molecular mechanisms. Cardiac fibrosis contributes to the development of heart failure with preserved ejection fraction (HFpEF), which increases mortality and the incidence of hospitalizations. As medical technology advances, the severity of cardiac fibrosis in DM cardiomyopathy can be evaluated by non-invasive imaging modalities such as echocardiography, heart computed tomography (CT), cardiac magnetic resonance imaging (MRI), and nuclear imaging. In this review article, we will discuss the pathophysiology of cardiac fibrosis in DM cardiomyopathy, non-invasive imaging modalities to evaluate the severity of cardiac fibrosis, and therapeutic strategies for DM cardiomyopathy.

Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis^® CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was −18 ± 22.0 mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was 6.4 ± 12.8 mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was 30.4 ± 3.6 mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography.

With the advancement of wearable technology, many physiological monitoring instruments are gradually being converted into wearable devices. However, as a consumer product, the blood pressure monitor is still a cuff-type device, which does perform a beat-by-beat continuous blood pressure measurement. Consequently, the cuffless blood pressure measurement device was developed and it is based on the pulse transit time (PTT), although its accuracy remains inadequate. According to the cardiac hemodynamic theorem, blood pressure relates to the arterial characteristics and the contours of the pulse wave include some characteristics of the artery. Therefore, the purpose of this study was to use the contour characteristics of the pulses measured by photoplethysmography (PPG) to estimate the blood pressure using a linear multi-dimension regression model. Ten subjects participated in the experiment, and the blood pressure levels of the subjects were elevated by exercise. The results showed that the mean and standard deviation (mean ± SD) of the root mean square error of the estimated systolic and diastolic pressures within the best five parameters were 6.9 ± 2.81 mmHg and 4.0 ± 0.65 mmHg, respectively. Compared to the results that used one parameter, the PTT, for estimating the systolic and diastolic pressures, 8.2 ± 2.1 mmHg and 4.5 ± 0.79 mmHg, respectively, our results were better.

Deep vein thrombosis (DVT) of lower limbs can easily arise from prolonged sitting or standing. Elders and pregnant women are most likely to have this disease. When the embolus of DVT comes to pass the lung, it will become a life-threatening disease. Thus, for DVT disease, early detection and the early treatment are needed. The goal of this study was to develop an examination system to be used at non-medical places to detect the DVT of lower limbs with light reflection rheography (LRR). Consisting of a wearable device and a mobile application (APP), the system is operated in a wireless manner to control the actions of sensors and display and store the LRR signals on the APP. Then, the recorded LRR signals are processed to find the parameters of DVT examination. Twenty subjects were recruited to perform experiments. The veins of lower limbs were occluded by pressuring the cuff up to 100 mmHg and 150 mmHg to simulate the slight and serious DVT scenarios, respectively. Six characteristic parameters were defined to classify whether there was positive or negative DVT using the receiver operating characteristic curves, including the slopes of emptying and refilling curves in the LRR signal, and the changes of venous pump volume. Under the slight DVT scenario (0 mmHg vs. 100 mmHg), the first three parameters, m₁₀, m₄₀, and m₅₀, had accuracies of 72%, 69%, and 69%, respectively. Under the serious DVT scenario (0 mmHg vs. 150 mmHg), m₁₀, m₄₀, and m₅₀ achieved accuracies of 73%, 76%, and 73%, respectively. The experimental results show that this proposed examination system may be practical as an auxiliary tool to screen DVT in homecare settings.

Gout is a common metabolic disorder characterized by hyperuricemia and chronic inflammation. Previous studies show that hyperuricemia accelerates the occurrence and worsening of cardiovascular disease due to LV remodeling. However, it is still unclear whether hyperuricemia is the sole contributor to organic heart remodeling in patients with gout. In addition, there is a paucity of data regarding the association between LV diastolic function and gout. The objective of this study was to investigate the effects of gout on LV diastolic function. A total of 173 patients were divided into tertiles based on the following serum uric acid (UA) levels: 1) serum UA ≤ 6.5 mg/dL (n = 54), 2) serum UA >6.5 to ≤8.5 mg/dL (n = 59), and 3) serum UA > 8.5 mg/dL (n = 60).Patients underwent a comprehensive Doppler-echocardiography examination to evaluate LV volume, systolic and diastolic function, and left atrial (LA) volume. LV diastolic parameters, including diastolic peak early transmitral flow velocity (E), late transmitral flow velocity (A), E/A, peak early diastolic mitral annular velocity (Em), late diastolic annular velocity (Am), Em/Am, E/Em, maximal LA volume index (LAVi) and prevalence of moderate to severe LV diastolic dysfunction were not significantly different between the three groups. Among the population being studied, 108 individuals received a gout diagnosis. Gout patients had greater LV end-systolic dimensions (27.08 ± 4.38 mm, p = 0.006), higher LV mass index (107.18 ± 29.51 g/m2, p < 0.001), higher E/Em (10.07 ± 2.91, p = 0.008), and larger maximal LAVi (16.96 ± 7.39 mL/m2, p < 0.001) than patients without gout. The prevalence of moderate to severe LV diastolic dysfunction was higher in patients with gout (23 %, p = 0.02). Gout, not hyperuricemia alone, is associated with LV diastolic dysfunction and LA volume enlargement.