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The cardiovascular system is characterized by complex interactions between various control mechanisms and physiological processes. Different approaches are used to provide better diagnostics and physiological understanding, cardiac prosthesis and medical planning. The mathematical description and modelling of the human cardiovascular system plays nowadays an important role in the comprehension of the genesis and development of cardiovascular disorders by providing computer based simulation of dynamic processes in this system. This paper aims to give an overview on lumped parameter models that have been developed by many researchers all over the world, to simulate the blood flow in systemic arteries. Surveying various references we make a review of different approaches to arterial tree modelling and discuss on the applications of such models.

Andrea Romigi is a clinical epileptologist and sleep specialist. SUMMARYObjective: To compare heart rate variability (HRV) parameters in newly diagnosed and untreated temporal lobe epilepsy (TLE) between the interictal, preictal, ictal, and postictal states. Methods: HRV parameters were extracted from single-lead electrocardiography data collected during video-electroencephalography (EEG) recordings from 14 patients with newly diagnosed TLE in a resting, awake, and supine state. HRV parameters in the time and frequency domains included low frequency (LF), high frequency (HF), standard deviation of all consecutive R wave intervals (SDNN), and square root of the mean of the sum of the squares of differences between adjacent R wave intervals (RMSSD). Cardiovagal index (CVI), cardiosympathetic index (CSI), and approximate entropy (ApEn) were also studied. Results: Frequency domain analysis showed significantly higher preictal, ictal, and postictal LF/HF ratio compared to the interictal state. Similarly, the LF component increased progressively and was significantly higher during the ictal state compared to interictal and preictal states. RR interval values were lower in the ictal state compared to basal and preictal states and in the postictal state compared to the preictal state. Interictal RMSSD was significantly higher compared to all other states, and ictal SDNN was significantly higher compared to all other states. Ictal CSI was significantly higher compared to preictal and interictal states, whereas preictal CVI was lower than in basal and ictal states. In addition, ictal ApEn was significantly lower than interictal and preictal ApEn. Interictal CVI was lower in left TLE compared to right TLE. In addition, in left TLE, ictal CVI was higher than interictal CVI, whereas in right TLE, CVI was lower in the preictal state compared to all other states. Significance: Our data suggest an ictal sympathetic overdrive with partial recovery in the postictal state. Higher sympathetic tone and vagal tone imbalance may induce early autonomic dysfunction and increase cardiovascular risk in patients affected by TLE.

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Glioblastoma multiforme (GBM) is the most common and deadliest primary brain tumor, driving patients to death within 15 months after diagnosis (short term survivors, ST), with the exception of a small fraction of patients (long term survivors, LT) surviving longer than 36 months. Here we present deep sequencing data showing that peritumoral (P) areas differ from healthy white matter, but share with their respective frankly tumoral (C) samples, a number of mRNAs and microRNAs representative of extracellular matrix remodeling, TGFβ and signaling, of the involvement of cell types different from tumor cells but contributing to tumor growth, such as microglia or reactive astrocytes. Moreover, we provide evidence about RNAs differentially expressed in ST vs LT samples, suggesting the contribution of TGF-β signaling in this distinction too. We also show that the edited form of miR-376c-3p is reduced in C vs P samples and in ST tumors compared to LT ones. As a whole, our study provides new insights into the still puzzling distinction between ST and LT tumors, and sheds new light onto that “grey” zone represented by the area surrounding the tumor, which we show to be characterized by the expression of several molecules shared with the proper tumor mass.