RESUMO
BACKGROUND: Painful diagnostic and therapeutic procedures performed for children are routine actions. Opioids and nonsteroidal anti-inflammatory drugs such as acetaminophens are among medications that can be used for this purpose. This study aimed to compare the onset and duration of action of intrabuccal (IB, submucosal) space and intramuscular (IM) injection of ketamine in pediatrics. MATERIALS AND METHODS: This clinical trial study was carried out on 126 children of 1-15 years old referred to the emergency room of Al-Zahra and Kashani Hospitals in Isfahan and divided into two 63 populated groups of IM and IB. For one group randomly, 3 mg/kg IB ketamine was administered, and for another group, ketamine was injected intramuscularly at the dose of 5 mg/kg. The drug effect, surgeon satisfaction, and complications were evaluated. Data were analyzed using SPSS software. RESULTS: The mean of time between injection and onset of drug effect in IM group was 5.71 min, whereas in IB group, it was 4.14 min (P < 0.0001). The mean of the duration of drug effect in IM group was 45.54 min, whereas in IB group, it was 24.63 min (P < 0.0001). Complications in IM group were significantly more reported than IB group (33.3% versus 11.1%, respectively, P = 003). The median of surgeon satisfaction in IM group was 3 and in IB group was 4 which was statistically significant (P = 0.007). CONCLUSIONS: IB method is preferred over IM method, and hence, it is recommended to use.
RESUMO
Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine-learning problem. However, due to the dimensionality and variability of expression data, existing methods based on feature space analysis or individual anomalously expressed genes are insufficient. We present a novel approach, CSAX, that identifies pathways in an individual sample in which the normal expression relationships are disrupted. To evaluate our approach, we have compiled and released a compendium of public expression data sets, reformulated to create a test bed for anomaly detection. We demonstrate the accuracy of CSAX on the data sets in our compendium, compare it to other leading methods, and show that CSAX aids in both identifying anomalies and explaining their underlying biology. We describe an approach to characterizing the difficulty of specific expression anomaly detection tasks. We then illustrate CSAX's value in two developmental case studies. Confirming prior hypotheses, CSAX highlights disruption of platelet activation pathways in a neonate with retinopathy of prematurity and identifies, for the first time, dysregulated oxidative stress response in second trimester amniotic fluid of fetuses with obese mothers. Our approach provides an important step toward identification of individual disease patterns in the era of precision medicine.
