The blood-type-mismatch problem, in addition to shortage of blood donation, in blood transfusion has prompted the researchers to develop universal blood that does not require blood typing. In this work, the “cell-in-shell” (i.e., artificial spore) approach is utilized to shield the immune-provoking epitopes on the surface of red blood cells (RBCs). Individual RBCs are successfully coated with supramolecular metal-organic coordination complex of ferric ion (Fe^III) and tannic acid (TA). The use of isotonic saline (0.85% NaCl) is found to be critical in the formation of stable, reasonably thick (20 nm) shells on RBCs without any aggregation and hemolysis. The formed “RBC-in-shell” structures maintain their original shapes, and effectively attenuate the antibody-mediated agglutination. Moreover, the oxygen-carrying capability of RBCs is not deteriorated after shell formation. This work suggests a simple but fast method for generating immune-camouflaged RBCs, which would contribute to the development of universal blood.

Assessing the impact of variants of unknown significance on splicing has become a critical issue and a bottleneck, especially with the widespread implementation of whole-genome or exome sequencing. Although multiple in silico tools are available, the interpretation and application of these tools are difficult and practical guidelines are still lacking. A streamlined decision-making process can facilitate the downstream RNA analysis in a more efficient manner. Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. The change in the predicted score incurred by the variant of the nearest wild-type splice site was analyzed, and for type II, III, and IV splice variants, the change in the prediction score of de novo or cryptic splice site was also analyzed. SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. For de novo and cryptic splice sites, SpliceAI outperformed all other tools and showed a sensitivity of 95.7 at an optimal cut-off of 0.02 score change. Our results show that deep learning algorithms, especially those of SpliceAI, are validated at a significantly higher rate than other in silico tools for clinically relevant NF1 variants. This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.

Objective: To describe interactions among cytokines and to identify subgroups of systemic lupus erythematosus (SLE) patients based on cytokine levels using principal component analysis and cluster analysis. Methods: Levels of 12 cytokines were measured using sensitive multiplex bead assays and associations with SLE features including disease activity and renal involvement were assessed. Results: In a group of 203 SLE patients, strong correlations were observed between interleukin (IL)6 and interferon (IFN)γ levels (r = 0.624), IL17 and IFNγ levels (r = 0.768), and macrophage inflammatory protein (MIP)1α and MIP1β levels (r = 0.675). Cluster analysis revealed two distinct patient groups characterized by high levels of IL8, MIP1α, and MIP1β (group 1) or of IL2, IL6, IL10, IL12, IFNγ, and tumor necrosis factor α (group 2). Active disease was more common in group 1 (49/88, 55.7%) than in group 2 (40/115, 34.8%). More patients in group 2 had renal involvement (42/115, 36.5%) than in group 1 (22/88, 25%). Conclusions: Assessment of cytokine profiles can identify distinct SLE patient subgroups and aid in understanding clinical heterogeneity and immunological phenotypes.

Jaewoong Lee, M.D., Joonhong Park, Ph.D., Hayoung Choi, B.S., Jiyeon Kim, B.S., Ahlm Kwon, B.S., Woori Jang, Ph.D., Hyojin Chae, Ph.D., Myungshin Kim, Ph.D., Yonggoo Kim, Ph.D., Jae Wook Lee, Ph.D., Nack-Gyun Chung, Ph.D., and Bin Cho, Ph.D.. Ann Lab Med 2017;37:108-16. https://doi.org/10.3343/alm.2017.37.2.108