The non-invasive evaluation of heart function in patients with an acute myocardial infarction: The role of impedance cardiography

Background: The purpose of this study was to analyze hemodynamic changes in patients treated withpercutaneous coronary intervention (PCI) at an early stage of acute myocardial infarction (AMI) and at 1-month follow-up.Methods: Patients with AMI (n = 27) who underwent PCI were analyzed using impedance cardiography(ICG). ICG data were collected continuously (beat by beat) during the whole PCI procedure and thereafter at every 60 s for the next 24 h. Blood pressure was taken every 10 min and stored for analysis. Additionally the following parameters were measured: cardiac index (CI), stroke volume index (SVi), left cardiac work index (LCWi), contractility index (CTi), ventricular ejection time (VET), systemic vascular resistance index (SVRi), thoracic fluid content index (TFCi) and heart rate (HR).Results: In the first 24 h after PCI all the contractility parameters including CI, SVi, LCWi, CTi and VET significantly decreased, whereas HR, SVRi and TFCi increased compared to baseline. All of the parameters examined got normalized at 1 month. The CI, SVi, LCWi, CTi, SVRi did not significantly differ from baseline, however the HR and VET were significantly lower compared to first day after PCIConclusions: Cardiac performance deteriorates early after PCI and normalizes after 1 month in patients with an AMI. ICG is useful for hemodynamic monitoring of AMI patients during and after invasive therapy

The immunopeptidome from a genomic perspective:Establishing the noncanonical landscape of MHC class I–associated peptides

G.B., D.B., K.W., A.P., R.F., T.R.H., S.K., and J.A.A. received support from Fundacja na rzecz Nauki Polskiej (FNP) (grant ID: MAB/3/2017). D.R.G. received support from Genome Canada & Genome BC (grant ID: 264PRO). D.J.H. received support from NuCana plc (grant ID: SMD0-ZIUN05). H.A. received support from Swedish Cancer Foundation (grant ID: 211709). H.G. received support from United Kingdom Research and Innovation (UKRI) (grant ID: EP/S02431X/1). C.P. received support from Fundação para a Ciência e a Tecnologia (FCT) through LASIGE Research Unit (grant ID: UIDB/00408/2020 and UIDP/00408/2020). A.L. F.M.Z., C.P., A.R., A.P., and J.A.A. received support from European Union’s Horizon 2020 research and innovation programme (grant ID: 101017453). C.B. received support from Agence Nationale de la Recherche (ANR) through GRAL LabEX (grant ID: ANR-10-LABX-49-01) and CBH-EUR-GS 32 (grant ID: ANR-17-EURE0003). S.N.S. received support from Cancer Research UK (CRUK) and the Chief Scientist's Office of Scotland (CSO): Experimental Cancer Medicine Centre (ECMC) (grant ID: ECMCQQR-2022/100017). A.L. received support from Chief Scientist's Office of Scotland (CSO) NRS Career Researcher Fellowship. R.O.N. received support from CRUK Cambridge Centre Thoracic Cancer Programme (grant ID: CTRQQR-2021\100012).Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development.Publisher PDFPeer reviewe

Basic Hand Gestures Classification Based on Surface Electromyography

This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method

A SVM-MCS Classifier for PCG Signals Processed by the LPC Algorithm

<p>A supervised classifier for phonocardiography signals processed by the Linear Predictive Coding algorithm. It is build upon combining a Support Vector Machine classifier and a Modified Cuckoo Search optimization algorithm. It has shown very promising results in classification of many types of heart tones at once.</p> <p> </p

A system for heart sounds classification

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.open access</p

Spectrum comparison of selected heart sounds and LPC filters.

<p>Presented curves demonstrate the effectiveness of the modified LPC algorithm in estimating different heart sounds.</p

Result of the heart tone segmentation algorithm.

<p>The waveform presented contains normal S1, S2 and S3 heart sounds, which are segmented by a variable size time window for further analysis.</p

Spectrum matching error for different filter orders.

<p>A comparison of matching errors in replicating a S3 heart tone spectrum. The presented curves indicate errors for three filters estimated by a Linear Predictive Coding algorithm with a transfer function of the 5th (red dotted line), 18th (green dotted line) and 24th (blue line) order. The 24th order filter obtained significantly lower error.</p

Percentage of correctly classified signals for different heart sounds from a leave-one-out test.

<p>ANN – Artificial Neural Network, SVM-poly – Support Vector Machine with polynomial kernel function, SVM-rbf – Support Vector Machine with radial basis kernel function, SVM-quad – Support Vector Machine with quadratic kernel function, SVM-MCS – Support Vector Machine with Modified Cuckoo Search optimizer, var. – variance</p><p>Percentage of correctly classified signals for different heart sounds from a leave-one-out test.</p