Blindness as an uncommon complication of Streptococcus pneumoniae systemic infection

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

A SYBR Green based multiplex Real-Time PCR assay for rapid detection and differentiation of ocular bacterial pathogens

PURPOSE

Ocular bacterial pathogenesis is a serious sight threatening infection due to several bacterial species like Staphylococcus aureus, Streptococcus pneumoniae and Pseudomonas aeruginosa which are predominant. It is necessary to expedite diagnosis of pathogens for early treatment. Hence, a SYBR Green based multiplex Real-Time PCR assay coupled with melting curve analysis has been developed for rapid detection and differentiation of Staphylococcus aureus, Streptococcus pneumoniae and Pseudomonas aeruginosa in a single reaction.

METHODS

The assay was designed for simultaneous detection and differentiation of pathogens based on their distinct melting curve. The analytical specificity, sensitivity and reproducibility of the assay were examined using various reference strains. Clinical validation was carried out with 100 ocular samples collected from patients suffering from ocular infections.

RESULT

Each reaction tested for the targets individually generated three non overlapping melting curves with well alienated peaks corresponding to each gene. Among 100 ocular samples tested, 40 samples diagnosed with positive results in RT-PCR. Thus assay showed 100% specificity with high sensitivity and reproducibility.

CONCLUSION

The developed assay consistently established as a rapid and accurate diagnosis of ocular bacterial pathogens compared to the conventional laboratory techniques. Such precise method would aid greatly in clinical management of devastating ocular infections.