Corrigendum: Robust rank aggregation and least absolute shrinkage and selection operator analysis of novel gene signatures in dilated cardiomyopathy.

[This corrects the article DOI: 10.3389/fcvm.2021.747803.].

Carbonized Silk Nanofibers in Biodegradable, Flexible Temperature Sensors for Extracellular Environments.

Temperature is one of the key parameters for activity of cells. The trade-off between sensitivity and biocompatibility of cell temperature measurement is a challenge for temperature sensor development. Herein, a highly sensitive, biocompatible, and degradable temperature sensor was proposed to detect the living cell extracellular environments. Biocompatible silk materials were applied as sensing and packing layers, which endow the device with biocompatibility, biodegradability, and flexibility. The silk-based temperature sensor presented a sensitivity of 1.75%/°C and a working range of 35-63 °C with the capability to measure the extracellular environments. At the bending state, this sensor worked at promising response of cells at different temperatures. The applications of this developed silk material-based temperature sensor include biological electronic devices for cell manipulation, cell culture, and cellular metabolism.

An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy.

Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary. Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS). Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen-containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients. Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.