Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF).

Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms.

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality.

Nature of bilayer lipids affects membranes deformation and pore resealing during nanoparticle penetration.

Interactions of nanoparticles (NPs) with lipid membranes have enormous biological implications especially for gene delivery applications. In this work, using all-atom steered- and molecular dynamics simulations, we investigated deformation of lipid membranes and pore closure during a NP penetration process. Three membrane bilayer models built from 2-oleoyl-1-palmitoyl-sn-glycero-3-phosphocholine (POPC), dipalmitoylphosphatidylcholine (DPPC) and dilauroylphosphatidylcholine (DLPC), and a NP formed by 2 short interfering RNA (siRNA) and 6 polyethylenimine (PEI) molecules were used.

Membrane lipids destabilize short interfering ribonucleic acid (siRNA)/polyethylenimine nanoparticles.

Cell entry of polymeric nanoparticles (NPs) bearing polynucleotides is an important stage for successful gene delivery. In this work, we addressed the influence of cell membrane lipids on the integrity and configurational changes of NPs composed of short interfering ribonucleic acid (siRNA) and polyethylenimine. We focused on NPs derived from two different PEIs, unmodified low molecular weight PEI and linoleic acid (LA)-substituted PEI, and their interactions with two membrane lipids (zwitterionic 2-oleoyl-1-palmitoyl-sn-glycero-3-phosphocholine (POPC) and anionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine (POPS)).

Steered molecular dynamics simulations reveal a self-protecting configuration of nanoparticles during membrane penetration.

Cell entry of polynucleotide-based therapeutic agents can be facilitated by nanoparticle (NP) mediated delivery. In this work, using steered molecular dynamics simulations, we simulated the membrane penetration process of a NP formed by 2 short interfering RNA (siRNA) and 6 polyethylenimine (PEI) molecules. To the best of our knowledge, this is the first set of simulations that explore the direct penetration of an siRNA/PEI NP through a membrane at an all-atom scale.

Carbon nanotube-encapsulated drug penetration through the cell membrane: an investigation based on steered molecular dynamics simulation.

Understanding the penetration mechanisms of carbon nanotube (CNTs)-encapsulated drugs through the phospholipid bilayer cell membrane is an important issue for the development of intracellular drug delivery systems. In the present work, steered molecular dynamics (SMD) simulation was used to explore the possibility of penetration of a polar drug, paclitaxel (PTX), encapsulated inside the CNT, through a dipalmitoylphosphatidylcholine bilayer membrane. The interactions between PTX and CNT and between PTX and the confined water molecules inside the CNT had a significant effect on the penetration process of PTX.