COVID-19 and Thrombosis: Clinical Aspects.

In coronavirus disease 2019 (COVID-19), thrombus formation is related to the pathogenesis of acute respiratory distress syndrome (ARDS) and the progression of clinical symptoms. Severe damage to vascular endothelial cells and the associated cytokine storm after SARS-CoV-2 infection cause thrombogenesis and contribute to the development of more severe and unique thromboses compared to other infectious diseases. Thromboses occur more often in critically ill patients. In addition to pulmonary thromboembolism (PE) and deep vein thrombosis, acute myocardial infarction, peripheral arterial thrombosis, and aortic thrombosis have also been reported. In PE, thrombi develop in both pulmonary arteries and alveolar capillaries. These, together with intraalveolar fibrin deposition, interfere with effective gaseous exchange in the lungs and exacerbate the clinical symptoms of ARDS in patients with COVID-19. Pharmacological thromboprophylaxis is recommended for all hospitalized patients to prevent both thrombosis and aggravation of ARDS, and other organ failures. Although the pediatric population is mostly asymptomatic or develops mild disease after SARS-CoV-2 infection, a new inflammatory disorder affecting the cardiovascular system, multisystem inflammatory syndrome in children (MIS-C), has been reported. Similar to Kawasaki disease, acute myocarditis, coronary vasculitis, and aneurysms are typically seen in MISC, although these two are now considered distinct entities. A similar acute myocarditis is also observed in young male adults, in which a hyperinflammatory state after SARS-CoV-2 infection seems to be involved. Several side effects following vaccination against COVID-19 have been reported, including vaccine-induced immune thrombotic thrombocytopenia and acute myocarditis. Although these could be serious and life-threatening, the cases are very rare, thus, the benefits of immunization still outweigh the risks.

[Thrombosis with Thrombocytopenia Syndrome after ChAdOx1 nCoV-19 vaccination].

A 48-year-old Japanese man who had no previous medical history received his first dose of the ChAdOx1 nCoV-19 vaccine. Ten days after the vaccine administration, he developed a headache. Laboratory results indicated throm-bocytopenia and DIC. A head CT revealed microbleeding in the left parietal lobe. Contrast-enhanced CT showed thrombus in the left transverse sinus and left sigmoid sinus. A brain MRI demonstrated venous hemorrhagic infarction and subarachnoid hemorrhages in the left parietal lobe, and whole-body enhanced CT also revealed portal vein embolism and renal infarction. He was diagnosed with thrombosis with thrombocytopenia syndrome, and was treated according to the guideline. He has been recovering with the treatments. This is the first reported case of TTS associated with the ChAdOx1 nCoV-19 vaccine in Japan.

Time of Day of Vaccination Does Not Associate With SARS-CoV-2 Antibody Titer Following First Dose of mRNA COVID-19 Vaccine.

The immune system exhibits circadian rhythms, and its response to viral infection is influenced by the circadian clock system. Previous studies have reported associations between the time of day of vaccination against COVID-19 and production of anti-SARS-CoV-2 antibody titer. We examined the effect of vaccination time of day on anti-SARS-CoV-2 antibody titer after the first dose of vaccination with the mRNA-1273 (Moderna) COVID-19 vaccine in an adult population. A total of 332 Japanese adults participated in the present study. All participants were not infected with SARS-CoV-2 and had already received the first dose of mRNA-1273 2 to 4 weeks prior to participating in the study. The participants were asked to provide basic demographic characteristics (age, sex, medical history, allergy, medication, and mean sleep duration), the number of days after the first dose of vaccination, and the time of day of vaccination. Blood was collected from the participants, and SARS-CoV-2 antibody titers were measured. Ordinary least square regression was used for assessing the relationship between basic demographic characteristics, number of days after vaccination, time of day of vaccination, and the log10-transformed normalized antibody titer. The least square mean of antibody titers was not associated with the vaccination time and sleep durations. The least square means of antibody titers was associated with age; the antibody titers decreased in people aged 50 to 59 years and 60 to 64 years. The present findings demonstrate that the vaccination time with mRNA-1273 was not associated with the SARS-CoV-2 antibody titer in an adult population, suggesting that these results do not support restricting vaccination to a particular time of day. The present findings may be useful in optimizing SARS-CoV-2 vaccination strategies.

[Novel disease entities due to antiplatelet factor 4 antibodies: HIT, aHIT, and VITT].

Antiplatelet factor 4 (PF4) antibodies, also known as anti-PF4/heparin complex antibodies, are measured to diagnose heparin-induced thrombocytopenia (HIT). In HIT, anti-PF4 antibodies induced by heparin exposure cause thrombocytopenia and thrombosis. However, in recent years, autoimmune HIT (aHIT) that develops without heparin exposure has been getting attention. In 2021, anti-PF4 antibodies were reported to cause the fatal vaccine-induced immune thrombotic thrombocytopenia (VITT) that developed after adenoviral vector vaccination for COVID-19. HIT, aHIT, and VITT are considered to be caused by anti-PF4 antibodies, and their pathological conditions are similar. However, they have different levels of severity, and the detection sensitivity of their antibodies varies depending on the assay. Herein, we review three pathologies, namely, HIT, aHIT, and VITT, associated with anti-PF4 antibodies.

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.