A rapid and simple analytical approach is developed to screen the semiconducting properties of metal organic frameworks (MOFs) by modeling the band structure and predicting the density of state of isoreticular MOFs (IRMOFs). One can consider the periodic arrangement of metal nodes linked by organic subunits as a 1D periodic array crystal model, which can be aligned with any unit-cell axis included in the IRMOF's primitive cubic lattice. In such a structure, each valence electron of a metal atom feels the potential field of the entire periodic array. We allocate the 1D periodic array in a crystal unit cell to three IRMOFs-n (n = 1, 8, and 10) of the Zn 4 O(L) 3 IRMOF series and apply the model to their crystal lattices with unit-cell constants a = 25.66, 30.09, and 34.28 Å, respectively. By solving Schrodinger's equation with a Kronig−Penney periodic potential and fitting the computed energy spectra to IRMOFs' experimental spectroscopic data, we model electronic band structures and obtain densities of state. The band diagram of each IRMOF reveals the nature of its electronic structures and density of state, allowing one to identify its n-or p-type semiconducting behavior. This novel analytical approach serves as a predictive and rapid screening tool to search the MOF database to identify potential semiconducting MOFs.

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Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

This study focuses on the analysis of the bioelectrical activity of the left ventricle using a 2D Bueno-Orovio-Fenton-Cherry monodomain reaction diffusion model. ECGs signals are simulated for normal and ischemic conditions of varying severity. Changes in ischemia are examined in a single precordial lead as the size of the ischemic area increases in various locations. Analyzing this single lead ECG, we determine the ratio between ST deviation and T-wave amplitude and establish a threshold sufficient for monitoring acute ischemic event. This method may be potentially implemented to predict sudden cardiac death.

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Introduction: Although rotor modulation targeting atrial fibrillation (AF) drivers or substrates has been proposed as one of the effective ablation strategies for non-paroxysmal AF (Non-PAF), previous meta-analyses demonstrated that ablation added to pulmonary vein isolation (PVI) did not result in the expected outcome. This is because the optimal method of detection of rotors and ablation strategy remain unclear. Recently, rotor detection using LGE-MRI-based computer simulations has been shown to be effective for Non-PAF ablation in clinical practice. Our study aimed to establish the minimal ablation strategy that, while correctly finding the rotors, also avoids iatrogenic atrial tachyarrhythmias due to excessive ablation. Hypothesis: By prioritizing and classifying detected rotors, reentrant drivers (RDs) of AF rather than passive rotors (PRs) could be identified, thereby offering an optimal ablation strategy. Methods: Personalized computational modeling of AF ablation was performed in 10 Non-PAF patient models based on fibrosis data from LGE-MRI. In each bi-atrial model, all rotors induced outside of PVI were investigated, and a number of ablation strategies were examined sequentially to classify rotors and achieve minimal ablation (figure). A rotor that terminated following ablation of another rotor was defined as PR. A rotor that persisted and needed to be ablated to achieve non-inducibility of the substrate was defined as RD. Results: Seven patients had rotors outside of PVI, with 6 having both PRs and RDs. Overall, 35 rotors were induced, 13 in left atrium and 22 in the right; 17 were RDs and 18 PRs. In addition, the density of fibrosis in the sites of RDs was significantly higher than in those of PRs (p=0.031). Conclusion: The sequential computer simulation strategy to predict ablation targets using a personalized AF model is promising in detecting different types of rotors and establishing the optimal minimum-lesion ablation strategy.