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Sepsis is a leading cause of death in critically ill patients, primarily due to multiple organ failures. It is associated with a systemic inflammatory response that plays a role in the pathogenesis of the disease. Intestinal barrier dysfunction and bacterial translocation (BT) play pivotal roles in the pathogenesis of sepsis and associated organ failure. In this review, we describe recent advances in understanding the mechanisms by which the gut microbiome and BT contribute to the pathogenesis of sepsis. We also discuss several potential treatment modalities that target the microbiome as therapeutic tools for patients with sepsis.

ABSTRACTThe ongoing severe acute respiratory syndrome pandemic caused by the novel coronavirus 2 (SARS-CoV-2) is associated with high morbidity and mortality rates, and it has created a pressing global need for effective antiviral therapies against it. COVID-19 disease pathogenesis is characterized by an initial virus-mediated phase, followed by inappropriate hyperactivation of the immune system leading to organ damage. Targeting of the SARS-CoV-2 viral receptors is being explored as a therapeutic option for these patients. In this paper, we summarize several potential receptors associated with the infectivity of SARS-CoV-2 and discuss their association with the immune-mediated inflammatory response. The potential for the development of resistance towards antiviral drugs is also presented. An algorithm-based platform to improve the efficacy of and overcome resistance to viral receptor blockers through the introduction of personalized variability is described. This method is designed to ensure sustained antiviral effectiveness when using SARS-CoV-2 receptor blockers.

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, and each decoder specializes in decoding words from a specific region of the channel words’ distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75 dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high signal-to-noise ratios (SNRs).