Deep Learning Approach for Quantification of Fluorescently Labeled Blood Cells in Danio rerio (Zebrafish).

Neutrophils are a type of white blood cell essential for the function of the innate immune system. To elucidate mechanisms of neutrophil biology, many studies are performed in vertebrate animal model systems. In Danio rerio (zebrafish), in vivo imaging of neutrophils is possible due to transgenic strains that possess fluorescently labeled leukocytes. However, due to the relative abundance of neutrophils, the counting process is laborious and subjective. In this article, we propose the use of a custom trained "you only look once" (YOLO) machine learning algorithm to automate the identification and counting of fluorescently labeled neutrophils in zebrafish. Using this model, we found the correlation coefficient between human counting and the model equals r = 0.8207 with an 8.65% percent error, while variation among human counters was 5% to 12%. Importantly, the model was able to correctly validate results of a previously published article that quantitated neutrophils manually. While the accuracy can be further improved, this model notably achieves these results in mere minutes compared with hours via standard manual counting protocols and can be performed by anyone with basic programming knowledge. It further supports the use of deep learning models for high throughput analysis of fluorescently labeled blood cells in the zebrafish model system.

Adding to platelet safety and life: Platelet additive solutions.

BACKGROUND: Platelet additive solutions (PAS) are crystalloid nutrient media used in place of plasma for platelet storage. They replace 60%-70% of plasma in platelet components, so the amount of storage plasma can be decreased. Platelets in PAS have lower risk for allergic transfusion reactions with equivalent clinical efficacy for controlling bleeding. AIM: The aim of this study is to evaluate the clinical and laboratory efficacy of PAS-platelets. MATERIALS AND METHODS: A total of 1674 single donor platelet (SDP) were collected in PAS in the month of June to September 2016 by different apheresis systems. The quality control tests were done on 356 units in 4 months. Total number of SDP were processed with Amicus device (n = 232), Trima Accel (n = 84), and MCS+ (n = 40). The parameters analyzed were antibody titer of anti-A and anti-B, volume, platelet count, pH, bacterial contamination, and reporting of adverse transfusion reaction. Antibody titers were checked by tube technique, and platelet counts were checked by hematology analyzer Sysmex poch 100i. The swirling was checked manually, and pH was checked with pH strips. RESULTS: Out of 356, 164 units were O group, 113 units were B group, 68 units were of A group, and the remaining 11 units were of AB Group. Anti-A and anti-B titer was significantly reduced in PAS-SDP and found 1:32 or less for all the units. All the units found negative for bacterial contamination. No transfusion reaction was reported of the units transfused. All other quality parameters for platelets also found satisfactory after implementing the additive solution. CONCLUSION: The ABO antibody titers were significantly reduced after addition of PAS. This facilitates the ABO incompatible SDP transfusion and helps in inventory management. The risk of allergic transfusion reaction decreases after reducing the amount of plasma from SDP units. Using PAS-SDP certainly improve the inventory management for platelets with no compromise on clinical and laboratory efficacy.