Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process

The effects of magnesium administration on cardiac ventricular function, heart rate variability, and myocardial morphological changes in a chronic diabetes disease model in rats

Introduction: Diabetes mellitus (DM) is a leading cause of morbidity and mortality all over the world, and the main cause of the mortality is cardiovascular complications. Such diabetic cardiovascular complications include coronary heart disease, cardiac autonomic neuropathy and ventricular dysfunction. Furthermore, DM is associated with electrolyte disturbances such as those involving potassium, calcium and magnesium (Mg2+). Among these electrolyte disturbances hypomagnesemia is common in diabetes and is associated with increased cardiovascular risk. Recent evidence has shown that Mg2+ supplementation can prevent cardiac autonomic dysfunction and improve ventricular compliance in acute DM. However, the underlying mechanisms of Mg2+ action and Mg2+ effects in chronic DM are unknown. Therefore, the present study explored the effects of Mg2+ administration and its possible mechanisms of action in chronic streptozotocin (STZ) induced diabetic rats. Methods: Adult male Wistar rats were injected intraperitoneally (i.p) once with either STZ (50 mg/Kg body weight) or the STZ vehicle (citrate buffer). The rats were then injected i.p once daily with either magnesium sulphate (MgSO4; 270 mg/Kg body weight) or the MgSO4 vehicle (normal saline) for 28 consecutive days. Blood glucose and body weight were measured throughout the period of the study. On day 28 of the experiments, in-vivo heart rate variability (HRV) parameters were measured to assess cardiac autonomic function using tail pulse plethysmography. Orthostatic stress was induced by tilting the animals from flat position to 70° head-up position. Ex-vivo hemodynamic and electrocardiograph (ECG) measurements were performed on a Langendorff perfusion system. Histological studies of ventricular tissue were performed using haematoxylin-eosin and Masson’s trichrome staining. Western blot analyses of the cardiac autonomic presynaptic marker (synaptophysin) and of the mitochondrial marker of oxidative stress (ATP5A) were performed on right atrial tissue. Plasma Mg2+ concentration was measured using automated photometric assays. Results: STZ treatment significantly increased the blood glucose level and decreased the body weight, and these STZ effects were not prevented by Mg2+ treatment. Diabetes decreased the root mean square differences of successive normal-to-normal intervals (RMSSD) and increased the low frequency (LF) /high frequency (HF) power ratio, which are both indicative of abnormal HRV. These diabetes effects on HRV parameters were significantly prevented by Mg2+ treatments (P < 0.05, STZ+Mg vs. STZ). DM also reduced both the heart rate and orthostatic stress-induced tachycardia, and these effects were reversed by Mg2+ treatment (P < 0.05, STZ+Mg vs. STZ). DM also decreased the left ventricular (LV) developed pressure and the maximal rate of LV pressure increase (+dP/dt), and these diabetic effects were prevented by Mg2+ treatment (P < 0.05, STZ+Mg vs. STZ). DM also decreased the maximal rate of LV pressure decline (-dP/dt) and the rate pressure product, but these parameters were not improved by Mg2+ treatment. DM and Mg2+ treatment did not affect the ECG waveforms and the coronary flow rate in all groups. Histologically, there were no differences in ventricular cardiomyocyte width or in the extent of interstitial fibrosis in all groups. Western blot analysis qualitatively showed a decrease in the expression of synaptophysin in DM that was prevented by Mg2+ treatment. Neither DM nor Mg2+ treatment altered ATP5A expression. The plasma Mg2+ concentration was not altered by DM or Mg2+ treatment. Conclusion: This study showed that Mg2+ treatment prevented cardiac autonomic dysfunction and improved hemodynamic function impairment in chronic DM. Based on the expression of synaptophysin, the mechanism through which Mg2+ improved cardiac autonomic function could involve the prevention of synaptic degradation in diabetes. The effects of Mg2+ on hemodynamic impairment in diabetes seemed to be unrelated to the Mg2+ effects on the cardiac histological structure or on the changes in coronary perfusion. Moreover, the overall effects of Mg2+ in diabetes were independent of its effects on the blood glucose level or the alteration of plasma Mg2+ level. Thus, Mg2+ treatment may have long-lasting therapeutic effects on ventricular dysfunction and cardiac autonomic impairment in chronic diabetes, but further studies are needed to explore the precise underlying mechanisms

Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging