Tethered platelet capture provides a mechanism for restricting circulating platelet activation to the wound site.

Background: Puncture wounding is a longstanding challenge to human health for which understanding is limited, in part, by a lack of detailed morphological data on how the circulating platelet capture to the vessel matrix leads to sustained, self-limiting platelet accumulation. Objectives: The objective of this study was to produce a paradigm for self-limiting thrombus growth in a mouse jugular vein model. Methods: Data mining of advanced electron microscopy images was performed from authors' laboratories. Results: Wide-area transmission electron mcrographs revealed initial platelet capture to the exposed adventitia resulted in localized patches of degranulated, procoagulant-like platelets. Platelet activation to a procoagulant state was sensitive to dabigatran, a direct-acting PAR receptor inhibitor, but not to cangrelor, a P2Y12 receptor inhibitor. Subsequent thrombus growth was sensitive to both cangrelor and dabigatran and sustained by the capture of discoid platelet strings first to collagen-anchored platelets and later to loosely adherent peripheral platelets. Spatial examination indicated that staged platelet activation resulted in a discoid platelet tethering zone that was pushed progressively outward as platelets converted from one activation state to another. As thrombus growth slowed, discoid platelet recruitment became rare and loosely adherent intravascular platelets failed to convert to tightly adherent platelets. Conclusions: In summary, the data support a model that we term Capture and Activate, in which the initial high platelet activation is directly linked to the exposed adventitia, all subsequent tethering of discoid platelets is to loosely adherent platelets that convert to tightly adherent platelets, and self-limiting, intravascular platelet activation over time is the result of decreased signaling intensity.

Massively Parallel Selection of NanoCluster Beacons.

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.

Venous puncture wound hemostasis results in a vaulted thrombus structured by locally nucleated platelet aggregates.

Primary hemostasis results in a platelet-rich thrombus that has long been assumed to form a solid plug. Unexpectedly, our 3-dimensional (3D) electron microscopy of mouse jugular vein puncture wounds revealed that the resulting thrombi were structured about localized, nucleated platelet aggregates, pedestals and columns, that produced a vaulted thrombus capped by extravascular platelet adherence. Pedestal and column surfaces were lined by procoagulant platelets. Furthermore, early steps in thrombus assembly were sensitive to P2Y12 inhibition and late steps to thrombin inhibition. Based on these results, we propose a Cap and Build, puncture wound paradigm that should have translational implications for bleeding control and hemostasis.

Convolutional neural networks to automate the screening of malaria in low-resource countries.

Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.