Plastin 3 in health and disease: a matter of balance.

For a long time, PLS3 (plastin 3, also known as T-plastin or fimbrin) has been considered a rather inconspicuous protein, involved in F-actin-binding and -bundling. However, in recent years, a plethora of discoveries have turned PLS3 into a highly interesting protein involved in many cellular processes, signaling pathways, and diseases. PLS3 is localized on the X-chromosome, but shows sex-specific, inter-individual and tissue-specific expression variability pointing towards skewed X-inactivation. PLS3 is expressed in all solid tissues but usually not in hematopoietic cells. When escaping X-inactivation, PLS3 triggers a plethora of different types of cancers. Elevated PLS3 levels are considered a prognostic biomarker for cancer and refractory response to therapies. When it is knocked out or mutated in humans and mice, it causes osteoporosis with bone fractures; it is the only protein involved in actin dynamics responsible for osteoporosis. Instead, when PLS3 is upregulated, it acts as a highly protective SMN-independent modifier in spinal muscular atrophy (SMA). Here, it seems to counteract reduced F-actin levels by restoring impaired endocytosis and disturbed calcium homeostasis caused by reduced SMN levels. In contrast, an upregulation of PLS3 on wild-type level might cause osteoarthritis. This emphasizes that the amount of PLS3 in our cells must be precisely balanced; both too much and too little can be detrimental. Actin-dynamics, regulated by PLS3 among others, are crucial in a lot of cellular processes including endocytosis, cell migration, axonal growth, neurotransmission, translation, and others. Also, PLS3 levels influence the infection with different bacteria, mycosis, and other pathogens.

Detection of myocardial ischemia due to clinically asymptomatic coronary artery stenosis at rest using supervised artificial intelligence-enabled vectorcardiography - A five-fold cross validation of accuracy.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death and disability. Conventional non-invasive diagnostic modalities for the detection of stable CAD at rest are subject to significant limitations: low sensitivity, and personal expertise. We aimed to develop a reliable and time-cost efficient screening tool for the detection of coronary ischemia using machine learning. METHODS: We developed a supervised artificial intelligence algorithm combined with a five lead vectorcardiography (VCG) approach (i.e. Cardisiography, CSG) for the diagnosis of CAD. Using vectorcardiography, the excitation process of the heart can be described as a three-dimensional signal. A diagnosis can be received, by first, calculating specific physical parameters from the signal, and subsequently, analyzing them with a machine learning algorithm containing neuronal networks. In this multi-center analysis, the primary evaluated outcome was the accuracy of the CSG Diagnosis System, validated by a five-fold nested cross-validation in comparison to angiographic findings as the gold standard. Individuals with 1, 2, or 3- vessel disease were defined as being affected. RESULTS: Of the 595 patients, 62·0% (n = 369) had 1, 2 or 3- vessel disease identified by coronary angiography. CSG identified a CAD at rest with a sensitivity of 90·2 ± 4·2% for female patients (male: 97·2 ± 3·1%), specificity of 74·4 ± 9·8% (male: 76·1 ± 8·5%), and overall accuracy of 82·5 ± 6·4% (male: 90·7 ± 3·3%). CONCLUSION: These findings demonstrate that supervised artificial intelligence-enabled vectorcardiography can overcome limitations of conventional non-invasive diagnostic modalities for the detection of coronary ischemia at rest and is capable as a highly valid screening tool.