A Hidden Markov Model for Seismocardiography

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services

Recent advances in computational design of potent aromatase inhibitors: open-eye on endocrine-resistant breast cancers

Introduction: The vast majority of breast cancers (BC) are estrogen receptor positive (ER+). The most effective treatments to fight this BC type rely on estrogen deprivation therapy, by inhibiting the aromatase enzyme, which performs estrogen biosynthesis, or on blocking the estrogens signaling path via modulating/degrading the estrogen\u2019s specific nuclear receptor (estrogen receptor-\u3b1, ER\u3b1). While being effective at early disease stage, patients treated with aromatase inhibitors (AIs) may acquire resistance and often relapse after prolonged therapies. Areas covered: In this compendium, after an overview of the historical development of the AIs currently in clinical use, and of the computational tools which were used to identify them, the authors focus on current advances in obtaining innovative inhibitors via molecular simulations. These inhibitors may help prevent or delay relapse to AIs. Expert opinion: BC remains the most diagnosed and the leading cause of death in women. In spite of the success of the adjuvant endocrine therapy, which has enormously prolonged woman\u2019s survival rate, the increasing emergence of the resistance phenomena calls for the development of novel approaches and drugs to fight it. The discovery of the last generation of AIs dates back to two decades ago, underlying a paucity of research efforts