5/10/2023 0 Comments Stroke path gimp 2.8.22![]() We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Taken together, these data indicated that L971 could down-regulate both JAK/STAT and NFκB signalling activities and has the potential to treat inflammatory diseases such as sepsis shock. Finally, L971 anti-inflammatory character was further verified in LPS-induced sepsis shock mouse model in vivo. The bioinformatic studies confirmed the anti-inflammatory effects of L971. Gene expression profiles upon L971 treatment were determined using high-throughput RNA sequencing, and significant differentially up-regulated and down-regulated genes were identified by DESeq analysis. ![]() L971 could inhibit the constitutive and stimuli-dependent activation of STAT1, STAT3 and IκBα and could significantly down-regulate the proinflammatory gene expression in mouse peritoneal macrophages stimulated by LPS. To find prodrugs that can treat inflammation, we performed a preliminary high-throughput screening of 18 840 small molecular compounds and identified scaffold compound L971 which significantly inhibited JAK/STAT and NFκB driven luciferase activities. JAK/STAT and NFκB signalling pathways play essential roles in regulating inflammatory responses, which are important pathogenic factors of various serious immune-related diseases, and function individually or synergistically. However, we hypothesize that some component of the host response is protective in both SS and SLC. Host response to infection plays a key role in pathogenesis of SS and SLC. Transcriptome-based systems biology approach segregates cancer into two groups (SLC and CA) based on similarity with SS. Additionally, pathway up-regulation was observed to be associated with survival in the SLC group of cancers. Machine learning classifier successfully segregated the two cancer groups with high accuracy (> 98%). The SLC group mainly consisted of malignancies of the gastrointestinal tract (head and neck, oesophagus, stomach, liver and biliary system) often associated with infection. In general, there was up-regulation in SS and one group of cancer (termed Sepsis-Like Cancer, or SLC), but not in other cancers (termed Cancer Alone, or CA). However, clustering segregated cancer types into two categories based on the direction of transcriptomic change. A robust classifier of cancer groups was developed based on machine learning.Ī total of 66 pathways were observed to be enriched in both SS and cancer. Clinical significance of the pathways was tested by survival analysis. Biological significance of the selected pathways was explored by network analysis. Thereafter, hierarchical clustering was applied to identify relative segregation of 17 cancer types into two groups vis-a-vis SS. Gene Set Enrichment Analysis was performed to detect the pathways enriched in SS and cancer. This has motivated a more comprehensive comparison of the transcriptomes of SS and cancer. Earlier analysis had revealed a cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. This mutual dependence for susceptibility suggests shared biology between the two disease categories. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. Sepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other.
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