Efficacy and safety of a bolus of half-dose r-SAK prior to primary PCI in ST-elevation myocardial infarction: Rationale and design of the OPTIMA-6 trial

Split intein-mediated backbone cyclization enhances the stability and activity of staphylokinase, a potent fibrin-selective plasminogen activator

Staphylokinase (Sak), a small 15 kDa globular protein that is secreted by certain strains of Staphylococcus aureus, shows a potent fibrin-selective thrombolytic activity. Earlier work has shown that Sak could potentially become a low-cost alternative to currently used thrombolytic agents, such as tissue plasminogen activator (tPA). In attempts to improve its potential for clinical applications, numerous modifications of Sak have already been investigated. Here, we have characterized a novel Sak modification, cyclized Sak (cyc-Sak), which was prepared through split-intein mediated protein backbone cyclization. We have characterized the structure, stability and the activity of cyc-Sak using biophysical techniques, limited proteolysis studies and plasminogen (PG)-activation assays. Our results show that cyc-Sak possesses an identical structure, enhanced stability, resistance to proteolysis by exoproteases and improved PG-activation properties compared to its linear counterpart. It can be over-expressed with high yield in the cytoplasm of Escherichia coli and is easily purified in a two-step process. The intein-mediated cyclization occurs spontaneously in vivo during protein expression and does not necessitate further modification steps after purification of the protein. Furthermore, covalent Sak cyclization could be readily combined with other Sak modifications previously proposed, to generate an effective thrombolytic agent with lower immunogenicity and improved stability and activity.

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT's limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.