Proteomics: a subcellular look at spermatozoa

<p>Abstract</p> <p>Background</p> <p>Male-factor infertility presents a vexing problem for many reproductively active couples. Many studies have focused on abnormal sperm parameters. Recent advances in proteomic techniques, especially in mass spectrometry, have aided in the study of sperm and more specifically, sperm proteins. The aim of this study was to review the current literature on the various proteomic techniques, and their usefulness in diagnosing sperm dysfunction and potential applications in the clinical setting.</p> <p>Methods</p> <p>Review of PubMed database. Key words: spermatozoa, proteomics, protein, proteome, 2D-PAGE, mass spectrometry.</p> <p>Results</p> <p>Recently employed proteomic methods, such as two-dimensional polyacrylamide gel electrophoresis, mass spectrometry, and differential in gel electrophoresis, have identified numerous sperm-specific proteins. They also have provided a further understanding of protein function involved in sperm processes and for the differentiation between normal and abnormal states. In addition, studies on the sperm proteome have demonstrated the importance of post-translational modifications, and their ability to bring about physiological changes in sperm function. No longer do researchers believe that in order for them to elucidate the biochemical functions of genes, mere knowledge of the human genome sequence is sufficient. Moreover, a greater understanding of the physiological function of every protein in the tissue-specific proteome is essential in order to unravel the biological display of the human genome.</p> <p>Conclusion</p> <p>Recent advances in proteomic techniques have provided insight into sperm function and dysfunction. Several multidimensional separation techniques can be utilized to identify and characterize spermatozoa. Future developments in bioinformatics can further assist researchers in understanding the vast amount of data collected in proteomic studies. Moreover, such advances in proteomics may help to decipher metabolites which can act as biomarkers in the detection of sperm impairments and to potentially develop treatment for infertile couples.</p> <p>Further comprehensive studies on sperm-specific proteome, mechanisms of protein function and its proteolytic regulation, biomarkers and functional pathways, such as oxidative-stress induced mechanisms, will provide better insight into physiological functions of the spermatozoa. Large-scale proteomic studies using purified protein assays will eventually lead to the development of novel biomarkers that may allow for detection of disease states, genetic abnormalities, and risk factors for male infertility. Ultimately, these biomarkers will allow for a better diagnosis of sperm dysfunction and aid in drug development.</p

Wide complex tachycardia differentiation: A reappraisal of the state-of-the-art

The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long-term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever-expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in-depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically

The Advent of Sperm Proteomics has Arrived

Abstract: Sperm proteomics is the identification and functional study of sperm proteins. It is based on the separation of proteins to generate a sample suitable for mass spectrometry and subsequent protein identification. Various proteomic approaches can be employed to study sperm proteins. Currently it has led to the identification and cataloging of thousands of sperm proteins. Ultimately, the goal is to apply sperm proteomics not only as a research method, but also as a clinical and diagnostic tool in the field of male infertility. This manuscript aims to review proteomics and the approaches used to analyze sperm proteins as well as put its application in context with some of the current findings

Differentiating wide complex tachycardias: A historical perspective

One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base - one that spans several decades

Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals

BACKGROUND: Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS: A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one all-inclusive model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model\u27s performance. RESULTS: The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION: Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

ECG Interpretation: Clinical Relevance, Challenges, and Advances

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation

ECG Interpretation: Clinical Relevance, Challenges, and Advances

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation