Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

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In the new era of the Internet of Things (IoT), all information related to the environment, things and humans is connected to networks. Humans, too, can be considered an integral part of the IoT ecosystem. The growing human-centricity of IoT applications raises the need greater dynamicity, heterogeneity, and scalability in future IoT systems. Recently, the IoT and cloud computing have both evolved as emerging technologies and have already become part of our daily life. The complementary features of the IoT and cloud are forming a new IT paradigm to meet current and future requirements. Due to the increased demand for and volume of IoT data, it has become a critical challenge to transfer data from the edge of the network to computing data centers due to the limitations of network bandwidth and higher latency delay. The emergence of the new paradigm of computing in the cloud computing architecture has made it necessary to overcome the inherent limitations of cloud computing, such as location awareness, scalability, energy efficiency, mobility, bandwidth bottlenecks, and latency delay. To address these issues, this paper proposes an efficient hybrid cloud architecture framework coupled with Li-Fi communication for a human-centric IoT network. It also introduces the architecture of the local cloud to reduce the latency delay and bandwidth cost and to improve efficiency, security, reliability and availability. Finally, the paper discusses the communication modulation schemes in the Li-Fi technique and presents scenarios involving the application of the proposed model in the real world.

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