The roles of interleukin-17A in risk stratification and prognosis of patients with sepsis-associated acute kidney injury

Background The aim of this study was to evaluate the roles of interleukin (IL)-17A in risk stratification and prognosis of patients with sepsis-associated acute kidney injury (SAKI). Methods We enrolled 146 sepsis patients (84 non-SAKI and 62 SAKI patients) admitted to the emergency department from November 2020 to November 2021. Patients with SAKI were differentiated based on the severity of acute kidney injury. All clinical parameters were evaluated upon admission before administering antibiotic treatment. Inflammatory cytokines were assessed using flow cytometry and the Pylon 3D automated immunoassay system (ET Healthcare). In addition, a receiver operating characteristic (ROC) curve was utilized to determine the prognostic values of IL-17A in SAKI. Results The levels of creatinine, IL-2, IL-4, IL-6, IL-17A, tumor necrosis factor alpha, C-reactive protein, and procalcitonin (PCT) were significantly higher in the SAKI group than in the non-SAKI group (p < 0.05). The level of IL-17A revealed significant differences among stages 1, 2, and 3 in SAKI patients (p < 0.05). The mean levels of PCT, IL-4, and IL-17A were significantly higher in the non-survival group than in the survival group in SAKI patients (p < 0.05). In addition, the area under the ROC curve of IL-17A was 0.811. Moreover, the IL-17A cutoff for differentiating survivors from non-survivors was 4.7 pg/mL, of which the sensitivity and specificity were 77.4% and 71.0%, respectively. Conclusion Elevated levels of IL-17A could predict that SAKI patients are significantly prone to worsening kidney injury with higher mortality. The usefulness of IL-17A in treating SAKI requires further research

Rac1 Targeting Suppresses Human Non-Small Cell Lung Adenocarcinoma Cancer Stem Cell Activity

The cancer stem cell (CSC) theory predicts that a small fraction of cancer cells possess unique self-renewal activity and mediate tumor initiation and propagation. However, the molecular mechanisms involved in CSC regulation remains unclear, impinging on effective targeting of CSCs in cancer therapy. Here we have investigated the hypothesis that Rac1, a Rho GTPase implicated in cancer cell proliferation and invasion, is critical for tumor initiation and metastasis of human non-small cell lung adenocarcinoma (NSCLA). Rac1 knockdown by shRNA suppressed the tumorigenic activities of human NSCLA cell lines and primary patient NSCLA specimens, including effects on invasion, proliferation, anchorage-independent growth, sphere formation and lung colonization. Isolated side population (SP) cells representing putative CSCs from human NSCLA cells contained elevated levels of Rac1-GTP, enhanced in vitro migration, invasion, increased in vivo tumor initiating and lung colonizing activities in xenografted mice. However, CSC activity was also detected within the non-SP population, suggesting the importance of therapeutic targeting of all cells within a tumor. Further, pharmacological or shRNA targeting of Rac1 inhibited the tumorigenic activities of both SP and non-SP NSCLA cells. These studies indicate that Rac1 represents a useful target in NSCLA, and its blockade may have therapeutic value in suppressing CSC proliferation and metastasis

Design and implementation of an Agricultural IoT based on LoRa

In order to build a large-scale Agriculture IoT, a sensor network based on LoRa is designed instead traditional Zigbee network. The data collected by the sensors is transmitted to remote server for storage through GPRS. The environment data is displayed on the browser for users to use. Because the agricultural production environment is complex, collected data is greatly influenced by noise and can not be analyzed directly. To solve this problem, the time series analysis method is used to model the raw data and a prediction model is obtained, which can fill or replace the missing data, abnormal data and so on, and can effectively predict the future data. It provides a good data source for the analysis of subsequent data. The experiment shows that the system can satisfy the design requirements and can operate efficiently and steadily

Design and implementation of an Agricultural IoT based on LoRa

In order to build a large-scale Agriculture IoT, a sensor network based on LoRa is designed instead traditional Zigbee network. The data collected by the sensors is transmitted to remote server for storage through GPRS. The environment data is displayed on the browser for users to use. Because the agricultural production environment is complex, collected data is greatly influenced by noise and can not be analyzed directly. To solve this problem, the time series analysis method is used to model the raw data and a prediction model is obtained, which can fill or replace the missing data, abnormal data and so on, and can effectively predict the future data. It provides a good data source for the analysis of subsequent data. The experiment shows that the system can satisfy the design requirements and can operate efficiently and steadily