Biomarkers based on the molecular mechanism of sepsis are important for timely diagnosis and treatment. A large panel of small non-coding microRNAs was reported to modulate the immune response in sepsis but have not been tested in clinical practice. Large-scale identification of microRNA networks in sepsis might reveal a new biological mechanism that can be also targeted by gene therapy. Therefore, the main objective of this study is to perform a comparison of the miRNA network between septic patients and healthy controls. We used the previously measured levels of expression of 16 different circulating human and viral microRNAs in plasma from 99 septic patients and 53 healthy controls. We used three different computational methods to find correlations between the expressions of microRNAs and to build microRNA networks for the two categories, septic patients and healthy controls. We found that the microRNA network of the septic patients is significantly less connected when compared to miRNA network of the healthy controls (21 edges vs 52 edges, P < 0.0001). We hypothesize that several microRNAs (miR-16, miR-29a, miR-146, miR-155, and miR-182) are being sponged in sepsis explaining the loss of connection in the septic patient miRNA network. This was specific for sepsis, as it did not occur in other conditions characterized by an increased inflammatory response such as in post-surgery patients. Using several target prediction instruments, we predicted potential common sponges for the miRNA network in sepsis from several signaling pathways. Understanding the dynamics of miRNA network in sepsis is useful to explain the molecular pathophysiology of sepsis and for designing therapeutic strategies that target essential components of the immune response pathways.

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The novel homogeneity measure Hd identifies carcinomas with very low aggressiveness within the class of complexity C1 or carcinomas with very high aggressiveness in the class C7.

The generation and organization of the invasion front shape of neoplasms is an intriguing problem. The intimate mechanism is not yet understood, but the prevailing theory is that it represents an example of morphogenesis. Morphogenesis requires the presence of specific molecules, known as morphogens (activators and inhibitors), which can diffuse and elicit dose-dependent responses in their target cells. Due to their ability to modulate most of the coding transcriptome, their well-established role in embryogenesis, and their capacity to rapidly move between neighboring and distant cells, we propose microRNAs as inhibitors that could shape the cancer invasion front. In order to explain the genesis of the tumor border, we use Alan Turing’s reaction diffusion model, refined by Meinhardt and Gierer. This assumes the existence of an activator called a, and an inhibitor called h, which we hypothesize could be a freely moving microRNA. We used the fractal dimension as a measure of tumor border irregularity. We observed that the change in fractal dimension associates with variations in the diffusion coefficient of the activator (Da) or the inhibitor (Dh). We determined that the fractal dimension remains constant (i.e., the irregularity of the tumor border does not change) across a Dh interval, which becomes narrower as Da rises. We therefore conclude that a change in fractal dimension occurs when the balance between Da and Dh is disrupted. Biologically, this could be explained by a faulty distribution of the inhibitor caused by an abnormal density of the intercellular connection network. From a translational perspective, if experimentally confirmed, our observations can be used for a better diagnosis of cancer aggressiveness.