Background The role of platelet-derived extracellular vesicles (PEVs) in the development of sepsis was investigated in this study. Methods After collection of blood samples from sepsis patients and normal volunteers, the extracellular vesicles (EVs) were separated, followed by the isolation of PEVs from the blood of rats. Next, a sepsis rat model was constructed by cecal ligation and puncture (CLP), and rats received tail vein injection of PEVs to explore the role of PEVs in sepsis. Subsequently, nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM) were adopted to determine the diameter of EVs and observe the morphology of PEVs, respectively; flow cytometry to detect the percentage of CD41-and CD61-positive EVs in isolated EVs; and ELISA to assess neutrophil extracellular trap (NET) formation, endothelial function injury-related markers in clinical samples or rat blood and serum inflammatory factor level. Results Compared with normal volunteers, the percentage of CD41- and CD61-positive EVs and the number of EVs were significantly elevated in sepsis patients. Moreover, sepsis patients also presented notably increased histone H3, myeloperoxidase (MPO), angiopoietin-2 and endocan levels in the blood, and such increase was positively correlated with the number of EVs. Also, animal experiments demonstrated that PEVs significantly promoted NET formation, mainly manifested as up-regulation of histone H3, high mobility group protein B1 (HMGB1), and MPO; promoted endothelial dysfunction (up-regulation of angiopoietin-2, endocan, and syndecan-1); and stimulated inflammatory response (up-regulation of interleukin (IL) -1β, IL-6, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein (MCP) -1) in the blood of sepsis rats. Conclusion PEVs aggravate endothelial function injury and inflammatory response in sepsis by promoting NET formation.
The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance ( P < 0.05 ). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance ( P < 0.05 ). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.
Aims The effect of changes in serum sodium levels on the survival of patients with heart failure (HF) is unclear. We aimed to analyse the impact of serum sodium level trajectories on survival in intensive care unit (ICU) patients with HF. Methods A total of 4760 patients diagnosed with HF between 2001 and 2012 from the Medical Information Mart for Intensive Care III (MIMIC‐III) database were extracted. Of these patients, 1132 patients who died within 48 h of ICU admission were excluded, and 3628 patients were included in this retrospective cohort study. Sodium levels were measured at baseline, 6, 12, 18, 24, 30, 36, 42, and 48 h. Patients were divided into hyponatremia, normal, and hypernatremia groups based on baseline sodium levels, and trajectory modelling was performed for each group separately. Group‐based trajectory model (GBTM) method was utilized to identify serum sodium levels trajectories. Results The number of patients with hyponatremia (<135 mmol/L), normal sodium levels (135–145 mmol/L), and hypernatremia (>145 mmol/L) at baseline were 594 (16.37%), 2,738 (75.47%), and 296 (8.16%), respectively. A total of seven trajectory groups were identified, including hyponatremia‐slow rise group [initial levels (IL), 128.48 ± 5.42 mmol/L; end levels (EL), 131.23 ± 3.83 mmol/L], hyponatremia‐rapid rise to normal group (IL, 132.13 ± 2.18 mmol/L; EL, 137.46 ± 3.68 mmol/L), normal‐slow decline group (IL, 137.65 ± 2.15 mmol/L; EL, 134.50 ± 2.54 mmol/L), normal‐steady‐state group (IL, 139.20 ± 2.26 mmol/L; EL, 139.04 ± 2.58 mmol/L), normal‐slow rise group (IL, 140.94 ± 2.37 mmol/L; EL, 143.43 ± 2.89 mmol/L), hypernatremia‐rapid decline to normal group (IL, 146.31 ± 1.98 mmol/L; EL, 140.71 ± 3.61 mmol/L), and hypernatremia‐slow decline group (IL, 148.89 ± 5.54 mmol/L; EL, 146.28 ± 3.90 mmol/L). The results showed that hyponatremia‐slow rise group [hazard ratio (HR) = 1.35; 95% confidence interval (CI), 1.01–1.80, P = 0.040], hyponatremia‐rapid rise to normal group (HR = 1.37; 95% CI, 1.11–1.71, P = 0.004), hypernatremia‐rapid decline to normal group (HR = 1.46; 95% CI, 1.08–1.97, P = 0.014), and hypernatremia‐slow decline group (HR = 1.49; 95% CI, 1.07–2.07, P = 0.018) trajectories were associated with an increased risk of 1‐year mortality in HF patients compared with normal‐steady‐state group. After adjustment for all confounders, hyponatremia‐rapid rise to normal group (HR = 1.26, 95% CI; 1.01–1.57, P = 0.038) and hypernatremia‐rapid decline to normal group (HR = 1.36; 95% CI, 1.01–1.84, P = 0.047) trajectories were still related to an increased risk of 1‐year mortality in patients with HF. Conclusions Serum sodium level trajectories were associated with mortality in patients with HF. Association between serum sodium level trajectorie...
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