Entrapment neuropathy results in different microRNA expression patterns from denervation injury in rats

<p>Abstract</p> <p>Background</p> <p>To compare the microRNA (miRNA) expression profiles in neurons and innervated muscles after sciatic nerve entrapment using a non-constrictive silastic tube, subsequent surgical decompression, and denervation injury.</p> <p>Methods</p> <p>The experimental L4-L6 spinal segments, dorsal root ganglia (DRGs), and soleus muscles from each experimental group (sham control, denervation, entrapment, and decompression) were analyzed using an Agilent rat miRNA array to detect dysregulated miRNAs. In addition, muscle-specific miRNAs (miR-1, -133a, and -206) and selectively upregulated miRNAs were subsequently quantified using real-time reverse transcription-polymerase chain reaction (real-time RT-PCR).</p> <p>Results</p> <p>In the soleus muscles, 37 of the 47 miRNAs (13.4% of the 350 unique miRNAs tested) that were significantly downregulated after 6 months of entrapment neuropathy were also among the 40 miRNAs (11.4% of the 350 unique miRNAs tested) that were downregulated after 3 months of decompression. No miRNA was upregulated in both groups. In contrast, only 3 miRNAs were upregulated and 3 miRNAs were downregulated in the denervated muscle after 6 months. In the DRGs, 6 miRNAs in the entrapment group (miR-9, miR-320, miR-324-3p, miR-672, miR-466b, and miR-144) and 3 miRNAs in the decompression group (miR-9, miR-320, and miR-324-3p) were significantly downregulated. No miRNA was upregulated in both groups. We detected 1 downregulated miRNA (miR-144) and 1 upregulated miRNA (miR-21) after sciatic nerve denervation. We were able to separate the muscle or DRG samples into denervation or entrapment neuropathy by performing unsupervised hierarchal clustering analysis. Regarding the muscle-specific miRNAs, real-time RT-PCR analysis revealed an ~50% decrease in miR-1 and miR-133a expression levels at 3 and 6 months after entrapment, whereas miR-1 and miR-133a levels were unchanged and were decreased after decompression at 1 and 3 months. In contrast, there were no statistical differences in the expression of miR-206 during nerve entrapment and after decompression. The expression of muscle-specific miRNAs in entrapment neuropathy is different from our previous observations in sciatic nerve denervation injury.</p> <p>Conclusions</p> <p>This study revealed the different involvement of miRNAs in neurons and innervated muscles after entrapment neuropathy and denervation injury, and implied that epigenetic regulation is different in these two conditions.</p

MicroRNA profiling in ischemic injury of the gracilis muscle in rats

<p>Abstract</p> <p>Background</p> <p>To profile the expression of microRNAs (miRNAs) and their potential target genes in the gracilis muscles following ischemic injury in rats by monitoring miRNA and mRNA expression on a genome-wide basis.</p> <p>Methods</p> <p>Following 4 h of ischemia and subsequent reperfusion for 4 h of the gracilis muscles, the specimens were analyzed with an Agilent rat miRNA array to detect the expressed miRNAs in the experimental muscles compared to those from the sham-operated controls. Their expressions were subsequently quantified by real-time reverse transcription polymerase chain reaction (real-time RT-PCR) to determine their expression pattern after different durations of ischemia and reperfusion. In addition, the expression of the mRNA in the muscle specimens after 4 h of ischemia and reperfusion for 1, 3, 7, and 14 d were detected with the Agilent Whole Rat Genome 4 × 44 k oligo microarray. A combined approach using a computational prediction algorithm that included miRanda, PicTar, TargetScanS, MirTarget2, RNAhybrid, and the whole genome microarray experiment was performed by monitoring the mRNA:miRNA association to identify potential target genes.</p> <p>Results</p> <p>Three miRNAs (miR-21, miR-200c, and miR-205) of 350 tested rat miRNAs were found to have an increased expression in the miRNA array. Real-time RT-PCR demonstrated that, with 2-fold increase after 4 h of ischemia, a maximum 24-fold increase at 7 d, and a 7.5-fold increase at 14 d after reperfusion, only the miR-21, but not the miR-200c or miR-205 was upregulated throughout the experimental time. In monitoring the target genes of miR-21 in the expression array at 1, 3, 7, 14 d after reperfusion, with persistent expression throughout the experiment, we detected the same 4 persistently downregulated target genes (<it>Nqo1</it>, <it>Pdpn</it>, <it>CXCL3</it>, and <it>Rad23b</it>) with the prediction algorithms miRanda and RNAhybrid, but no target gene was revealed with PicTar, TargetScanS, and MirTarget2.</p> <p>Conclusions</p> <p>This study revealed 3 upregulated miRNAs in the gracilis muscle following ischemic injury and identified 4 potential target genes of miR-21 by examining miRNAs and mRNAs expression patterns in a time-course fashion using a combined approach with prediction algorithms and a whole genome expression array experiment.</p

Identification of Pancreatic Injury in Patients with Elevated Amylase or Lipase Level Using a Decision Tree Classifier: A Cross-Sectional Retrospective Analysis in a Level I Trauma Center

Background: In trauma patients, pancreatic injury is rare; however, if undiagnosed, it is associated with high morbidity and mortality rates. Few predictive models are available for the identification of pancreatic injury in trauma patients with elevated serum pancreatic enzymes. In this study, we aimed to construct a model for predicting pancreatic injury using a decision tree (DT) algorithm, along with data obtained from a population-based trauma registry in a Level I trauma center. Methods: A total of 991 patients with elevated serum levels of amylase (&gt;137 U/L) or lipase (&gt;51 U/L), including 46 patients with pancreatic injury and 865 without pancreatic injury between January 2009 and December 2016, were allocated in a ratio of 7:3 to training (n = 642) or test (n = 269) sets. Using the data on patient and injury characteristics as well as laboratory data, the DT algorithm with Classification and Regression Tree (CART) analysis was performed based on the Gini impurity index, using the rpart function in the rpart package in R. Results: Among the trauma patients with elevated amylase or lipase levels, three groups of patients were identified as having a high risk of pancreatic injury, using the DT model. These included (1) 69% of the patients with lipase level ≥306 U/L; (2) 79% of the patients with lipase level between 154 U/L and 305 U/L and shock index (SI) ≥ 0.72; and (3) 80% of the patients with lipase level &lt;154 U/L with abdomen injury, glucose level &lt;158 mg/dL, amylase level &lt;90 U/L, and neutrophil percentage ≥76%; they had all sustained pancreatic injury. With all variables in the model, the DT achieved an accuracy of 97.9% (sensitivity of 91.4% and specificity of 98.3%) for the training set. In the test set, the DT achieved an accuracy of 93.3%, sensitivity of 72.7%, and specificity of 94.2%. Conclusions: We established a DT model using lipase, SI, and additional conditions (injury to the abdomen, glucose level &lt;158 mg/dL, amylase level &lt;90 U/L, and neutrophils ≥76%) as important nodes to predict three groups of patients with a high risk of pancreatic injury. The proposed decision-making algorithm may help in identifying pancreatic injury among trauma patients with elevated serum amylase or lipase levels