ACURATE neo2 Transcatheter aortic valve implantation without balloon aortic valvuloplasty - direct ACURATE neo2.

Background: ACURATE neo2 (Neo2) implantation is performed after systematic Balloon Aortic Valvuloplasty (BAV) in most patients. No reports exist about the feasibility and safety of direct Neo2 transcatheter aortic valve implantation (TAVI) in comparison to the standard practice.

Aim: We aimed to identify the patients' baseline anatomical characteristics, procedural, and early post-procedural outcomes in patients treated using Neo2 with and without BAV.

Predictors of Conduction Disturbances Requiring New Permanent Pacemaker Implantation following Transcatheter Aortic Valve Implantation Using the Evolut Series.

(1) Background: Conduction disturbance requiring a new permanent pacemaker (PPM) after transcatheter aortic valve implantation (TAVI) has traditionally been a common complication. New implantation techniques with self-expanding platforms have reportedly reduced the incidence of PPM. We sought to investigate the predictors of PPM at 30 days after TAVI using Evolut R/PRO/PRO+; (2) Methods: Consecutive patients who underwent TAVI with the Evolut platform between October 2019 and August 2022 at University Hospital Galway, Ireland, were included.

Core Lab Adjudication of the ACURATE Hemodynamic Performance Using Computed-Tomography-Corrected Left Ventricular Outflow Tract Area.

(1) Background: Hemodynamic assessment of prosthetic heart valves using conventional 2D transthoracic Echocardiography-Doppler (2D-TTE) has limitations. Of those, left ventricular outflow tract (LVOT) area measurement is one of the major limitations of the continuity equation, which assumes a circular LVOT. (2) Methods: This study comprised 258 patients with severe aortic stenosis (AS), who were treated with the ACURATE .

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients.

Accurate prediction of kinase-substrate networks using knowledge graphs.

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy.

Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.

Polypharmacy is the use of drug combinations and is commonly used for treating complex and terminal diseases. Despite its effectiveness in many cases, it poses high risks of adverse side effects. Polypharmacy side-effects occur due to unwanted interactions of combined drugs, and they can cause severe complications to patients which results in increasing the risks of morbidity and leading to new mortalities.

Predicting The Effects of Chemical-Protein Interactions On Proteins Using Tensor Factorisation.

Understanding the different effects of chemical substances on human proteins is fundamental for designing new drugs. It is also important for elucidating the different mechanisms of action of drugs that can cause side-effects. In this context, computational methods for predicting chemical-protein interactions can provide valuable insights on the relation between therapeutic chemical substances and proteins.

Biological applications of knowledge graph embedding models.

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e.

Discovering protein drug targets using knowledge graph embeddings.

Motivation: Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges.