Biology subjects

Lebedev, D.

Publications and source records attributed to Lebedev, D..

3 recordsLinked to original sources

bioRxiv2021

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSUp to 30%-50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge. ObjectiveThe main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology. Materials and MethodsRetrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from active poles of leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only. ResultsThe best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification. ConclusionOur results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

physiology↗

bioRxiv2019

Cold and distant: structural features of the nucleoprotein complex of a cold-adapted influenza A virus strain

Two influenza A nucleoprotein variants (wt: G102R; and mutant: G102R and E292G) were studied with regard to macro-molecular interactions in oligomeric form (24-mers). The E292G mutation has been previously shown to provide cold adaptation. Molecular dynamics simulations of these complexes and trajectory analysis showed that the most significant difference between the obtained models was distance differences between nucleoprotein complex strands. Influenza virus nucleoprotein complexes were isolated from strains bearing the corresponding NP amino acid substitutions. The isolated complexes were characterized by transmission electron microscopy and differential scanning fluorimetry (DSF). Presence of the E292G substitution was shown by DSF to affect nucleoprotein complex melting temperature. In the filament interface peptide model, it was shown that the peptide corresponding in primary structure to the wild-type NP (SGYDFEREGYS, wild type peptide) is prone to temperature-dependent self-association, unlike the peptide carrying the substitution corresponding to E292G (SGYDFGREGYS, mutant peptide). It was also shown that the SGYDFEREGYS peptide (wt) is capable of interacting with a recombinant full-size monomeric nucleoprotein (with primary structure corresponding to wild type); this interactions equilibrium dissociation constant is five orders of magnitude lower than for the SGYDFGREGYS peptide. Using small-angle neutron scattering (SANS), the supramolecular structures of isolated complexes of these proteins was studied at temperatures of 15, 32, and 37{degrees}C. SANS data show that the structures of the studied complexes (mutant or normal proteins with RNA) at elevated temperature differ from the rod-like particle model and react differently to temperature changes. The data suggest that the mechanism behind cold adaptation with E292G is associated with a weakening of the interaction between strands of the ribonucleoprotein complex and, as a result, the appearance of inter-chain interface flexibility necessary for complex function at low temperature.

biophysics↗

bioRxiv2019

Effect of Alpha-Lactalbumin and Lactoferrin Oleic Acid Complexes on Chromatin Structural Organization

This work focuses on the study of multimeric alpha-lactalbumin oleic acid and lactoferrin oleic acid complexes. The purpose of the research is to study possible mechanisms involved in their pro-apoptotic activities, as seen in some tumor cell cultures. Complexes featuring oleic acid (OA) with human alpha-lactalbumin (hAl) or with bovine alpha-lactalbumin (bAl), and human lactoferrin (hLf) were investigated using small-angle neutron scattering (SANS). It was shown that while alpha-lactalbumin protein complexes were formed on the surface of polydisperse OA micelles, the lactoferrin complexes comprised a monodisperse system of nanoscale particles. Both hAl and hLf complexes appeared to interact with the chromatin of isolated nuclei affecting chromatin structural organization. The possible roles of these processes in the specific anti-tumor activity of these complexes are discussed.

biophysics↗