Objective—: Phosphatidylserine (PS) externalization by platelets upon activation is a key event in hemostasis and thrombosis. It is currently believed that strong stimulation of platelets forms 2 subpopulations, only 1 of which expresses PS. Methods and Results—: Here, we demonstrate that physiological stimulation leads to the formation of not 1 but 2 types of PS-expressing activated platelets, with dramatically different properties. One subpopulation sustained increased calcium level after activation, whereas another returned to the basal low-calcium state. High-calcium PS-positive platelets had smaller size, high surface density of fibrin(ogen), no active integrin α _IIb β ₃ , depolarized mitochondrial membranes, gradually lost cytoplasmic membrane integrity, and were poorly aggregated. In contrast, the low-calcium PS-positive platelets had normal size, retained mitochondrial membrane potential and cytoplasmic membrane integrity, and combined retention of fibrin(ogen) with active α _IIb β ₃ and high proaggregatory function. Formation of low-calcium PS-positive platelets was promoted by platelet concentration increase or shaking and was decreased by integrin α _IIb β ₃ antagonists, platelet dilution, or in platelets from kindlin-3–deficient and Glanzmann thrombasthenia patients. Conclusion—: Identification of a novel PS-expressing platelet subpopulation with low calcium regulated by integrin α _IIb β ₃ can be important for understanding the mechanisms of PS exposure and thrombus formation.

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic–peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

We describe a novel microarray-based approach for simultaneous identification and quantification of human immunodeficiency virus type 1 (HIV-1) and hepatitis B and C viruses (HBV and HCV) in donor ...

Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.