Dengue is an increasingly important cause of morbidity and mortality in the tropics, but vaccine development has been impeded by a poor understanding of disease pathogenesis and, in particular, of immunologic enhancement. In a large case-control study of Vietnamese patients with dengue hemorrhagic fever (DHF), variation at the HLA-A locus was significantly associated with susceptibility to DHF (P=.02), and specific HLA-A susceptibility and resistance alleles were identified. HLA-A-specific epitopes were predicted from binding motifs, and ELISPOT analyses of patients with DHF revealed high frequencies of circulating CD8 T lymphocytes that recognized both serotype-specific and -cross-reactive dengue virus epitopes. Thus, strong CD8 T cell responses are induced by natural dengue virus infection, and HLA class I genetic variation is a risk factor for DHF. These genetic and immunologic data support both protective and pathogenic roles for dengue virus-specific CD8 T cell responses in severe disease. The potentially pathogenic role of serotype-cross-reactive CD8 T cells poses yet another obstacle to successful dengue vaccine development.

Objective This study evaluated the presence and the levels of antibodies reactive to SARS-CoV-2 S1 and S2 subunits (S1 + S2), and nucleocapsid protein. Study design The levels of SARS-CoV-2 S1 + S2-and nucleocapsid-reactive SIgM/IgM, IgG and SIgA/IgA were measured in human milk samples from 41 women during the COVID-19 pandemic (2020-HM) and from 16 women 2 years prior to the outbreak (2018-HM). Results SARS-CoV-2 S1 + S2-reactive SIgA/IgA, SIgM/IgM and IgG were detected in 97.6%, 68.3% and 58.5% in human milk whereas nucleocapsid-reactive antibodies were detected in 56.4%, 87.2% and 46.2%, respectively. S1 + S2-reactive IgG was higher in milk from women that had symptoms of viral respiratory infection(s) during the last year than in milk from women without symptom. S1 + S2-and nucleocapsid-reactive IgG were higher in the 2020-HM group compared to the 2018-HM group. Conclusions The presence of SARS-CoV-2-reactive antibodies in human milk could provide passive immunity to breastfed infants and protect them against COVID-19 diseases.

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The original version of this article contained an error in the spelling of the author D.M. Do, which was incorrectly given as M. Dung. This has now been corrected in both the PDF and HTML versions of the article.

Background Graph databases enable efficient storage of heterogeneous, highly-interlinked data, such as clinical data. Subsequently, researchers can extract relevant features from these datasets and apply machine learning for diagnosis, biomarker discovery, or understanding pathogenesis. Methods To facilitate machine learning and save time for extracting data from the graph database, we developed and optimized Decision Tree Plug-in (DTP) containing 24 procedures to generate and evaluate decision trees directly in the graph database Neo4j on homogeneous and unconnected nodes. Results Creation of the decision tree for three clinical datasets directly in the graph database from the nodes required between 0.059 and 0.099 s, while calculating the decision tree with the same algorithm in Java from CSV files took 0.085–0.112 s. Furthermore, our approach was faster than the standard decision tree implementations in R (0.62 s) and equal to Python (0.08 s), also using CSV files as input for small datasets. In addition, we have explored the strengths of DTP by evaluating a large dataset (approx. 250,000 instances) to predict patients with diabetes and compared the performance against algorithms generated by state-of-the-art packages in R and Python. By doing so, we have been able to show competitive results on the performance of Neo4j, in terms of quality of predictions as well as time efficiency. Furthermore, we could show that high body-mass index and high blood pressure are the main risk factors for diabetes. Conclusion Overall, our work shows that integrating machine learning into graph databases saves time for additional processes as well as external memory, and could be applied to a variety of use cases, including clinical applications. This provides user with the advantages of high scalability, visualization and complex querying.