ABSTRACT
Background: Neonatal sepsis is a lethal syndrome that necessitates prompt treatment to avoid disease complications. As a result, biomarkers that may either differentiate sepsis early or predict the outcome of sepsis are essential. Aim: The goal of this research was to find out the clinical weight of using miR181b-5p and miR21-5p expression levels as diagnostic and prognostic new genetic markers for neonatal sepsis. Method: A total of 60 neonates with sepsis and 60 healthy neonates were involved in this study. Laboratory tests include complete blood count (CBC), random blood sugar (RBS), arterial blood gases (ABG), and serum C-reactive protein (CRP). Neonates with sepsis were assessed by the Score for Neonatal Acute Physiology II (SNAP II). The serum fold changes of the target miRNAs were determined using qRT-PCR and the 2-ΔΔCt equation. Results: The relative serum level of miR181b-5p was [ median (IQR) = 0.2509 (0.0009-4.11)] and for miR21-5p was [median (IQR) = 0.07 (0.007-7.16)] which were significantly downregulated in patients with neonatal sepsis compared to controls (p < 0.001 each). There was a strong significant positive correlation between miR181b-5p and miR21-5p with r = 0.718 and p < 0.001. MiR181b-5p and miR21-5p were significantly negatively correlated with total leucocytic count (TLC), lymphocytic count, and CRP. While they were both positively correlated to the SNAP II score. Obvious association between higher expressions of target genes and higher SNAP II score groups. After a following-up period, twenty-two (36.7%) neonates died, while 38 (63.3%) of the babies became better and were released from the hospital. We reported that miR-181-5p, miR21-5p, SNAP II score and CRP were significantly higher in non-survivors than survivors. Only miR181b-5p, miR21-5p, and SNAP II were predictive factors of septic mortality. Conclusion: MiR181b-5p and miR21-5p are diagnostic and prognostic biomarkers of neonatal sepsis.
ABSTRACT
The automation strategy of today's smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed.
ABSTRACT
Background: Behçet's disease (BD) is a chronic autoimmune disease. The early diagnosis of BD is very important to avoid serious and/or fatal complications such as eye damage, severe neurological involvement, and large vessel occlusion. New, sensitive biomarkers would aid in rapid diagnosis, the monitoring of disease activity, and the response to treatment. Methods: This study's aim is to identify two immune system-related BD biomarkers. We measured long non-coding RNAs (lncRNAs) NEAT1 (nuclear-enriched abundant transcript 1), and lnc-DC (lncRNA in dendritic cells) in serum by real-time polymerase chain reaction (RT-PCR) in 52 BD patients and 52 controls. We analyzed the association between NEAT1 and lnc-DC and the clinical parameters of BD. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic performance of the studied genes. Results: Compared to controls, the significant upregulation of NEAT1 {median [interquartile range (IQR)] = 1.68 (0.38-7.7), p < 0.0001} and downregulation of lnc-DC [median (IQR) = 0.2 (0.12-1.39), p = 0.03] were detected in the sera collected from BD patients. Higher serum expression levels of NEAT1 and lnc-DC were significantly associated with the following clinical presentations: cutaneous lesions, vascular manifestations, articular manifestations, neurological manifestations, and higher disease activity score. Also, high NEAT1 levels were significantly associated with a negative pathergy test, while higher lnc-DC was significantly associated with a positive family history. ROC curves showed that NEAT1 and lnc-DC levels in serum could be used as predictors of BD with high specificity and fair sensitivity. NEAT1 had an area under the curve (AUC) of 0.692 (95% CI: 0.591-0.794, p = 0.001), and lnc-DC had an AUC of 0.615 (95% CI: 0.508-0.723, p = 0.043). Conclusion: Serum lncRNAs NEAT1 and lnc-DC are biomarkers for BD.
