Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments

Hybrid particles derived from alendronate and bioactive glass for treatment of osteoporotic bone defects

\u3cp\u3eOsteoporosis is the most widespread metabolic bone disease which represents a major public health burden. Consequently, novel biomaterials with a strong capacity to regenerate osteoporotic bone defects are urgently required. In view of the anti-osteoporotic and osteopromotive efficacy of alendronate and 45S5 bioactive glass, respectively, we investigated the feasibility to synthesize novel hybrid particles by exploiting the strong interactions between these two compounds. Herein, we demonstrate the facile preparation of a novel class of hybrid particles of tunable morphology, chemical composition and structure. These hybrid particles (i) release alendronate and various inorganic elements (Ca, Na, Si, and P) in a controlled manner, (ii) exhibit a strong anti-osteoclastic effect in vitro, and (iii) stimulate regeneration of osteoporotic bone in vivo. Consequently, this novel class of hybrid biomaterials opens up new avenues of research on the design of bone substitutes with specific activity to facilitate regeneration of bone defects in osteoporotic patients.\u3c/p\u3

Hybrid particles derived from alendronate and bioactive glass for treatment of osteoporotic bone defects

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