The effect of phenylephrine on the onset time of rocuronium

BACKGROUND: Several studies have demonstrated that ephedrine shortens the onset time of muscle relaxants, and it does so probably by increasing the cardiac output. However, elevation of the systemic blood pressure through α adrenergic stimulation via ephedrine may affect the onset of muscle relaxants during the induction of anesthesia. We investigated the effect of phenylephrine, which is a selective α-1 agonist, on the onset time of rocuronium and the intubating conditions in adults after the administration of propofol. METHODS: Sixty-four patients were randomly assigned to two groups. Phenylephrine (0.9 µg/kg) (P group) or the same volume of saline (S group) was injected before rocuronium (0.6 mg/kg) administration. Anesthesia was induced with fentanyl 2 µg/kg and propofol 2 mg/kg. The onset time was defined as the time from the end of rocuronium injection to the time when a single twitch height gets to 0% or the minimum level. A well-trained anesthesiologist who was 'blinded' to the treatment groups evaluated the intubating conditions. The mean arterial pressure and heart rate were recorded before induction, before intubation, immediately after intubation and 1 minute and 2 minutes after intubation. RESULTS: The onset time was 84 ± 18 sec in the P-group and 72 ± 14 sec in the S-group. There was no difference of the intubating conditions, the mean arterial pressure and the heart rate between the two groups. CONCLUSIONS: A small dose of phenylephrine, which has a limited effect on blood pressure, delayed the onset time of rocuronium after the administration of propofol, and the vasoconstriction effect of phenylephrine may affect the prolongation of the rocuronium onset time at the induction of anesthesia with using propofol.ope

Advancements in the treatment of pediatric acute leukemia and brain tumor - continuous efforts for 100% cure

Treatment outcomes of pediatric cancers have improved greatly with the development of improved treatment protocols, new drugs, and better supportive measures, resulting in overall survival rates greater than 70%. Survival rates are highest in acute lymphoblastic leukemia, reaching more than 90%, owing to risk-based treatment through multicenter clinical trials and protocols developed to prevent central nervous system relapse and testicular relapse in boys. New drugs including clofarabine and nelarabine are currently being evaluated in clinical trials, and other targeted agents are continuously being developed. Chimeric antigen receptor-modified T cells are now attracting interest for the treatment of recurrent or refractory disease. Stem cell transplantation is still the most effective treatment for pediatric acute myeloid leukemia (AML). However, in order to reduce treatment-related death after stem cell transplantation, there is need for improved treatments. New drugs and targeted agents are also needed for improved outcome of AML. Surgery and radiation therapy have been the mainstay for brain tumor treatment. However, chemotherapy is becoming more important for patients who are not eligible for radiotherapy owing to age. Stem cell transplant as a means of high dose chemotherapy and stem cell rescue is a new treatment modality and is often repeated for improved survival. Drugs such as temozolomide are new chemotherapeutic options. In order to achieve 100% cure in children with pediatric cancer, every possible treatment modality and effort should be considered

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. Results Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics

The Prevalence of Hepatitis C Virus Infection in Korea: Pooled Analysis

This study evaluated the prevalence of hepatitis C virus (HCV) infections in Korea. Pooled estimates of the anti-HCV positivity were calculated using the data published in 15 reports on the general population and health check-up examinees. The overall pooled estimate of the prevalence of HCV among middle-aged adults (40 yr old and above) was 1.68% (95% confidence interval: 1.51-1.86%) during the year of 1990-2000 among the general population. Most of the published data indicated that the prevalence of anti-HCV increased with age. The anti-HCV positivity was significantly higher in females than in males. Because the risk of HCV exposure in blood recipients has decreased remarkably, the spread of HCV through means other than a transfusion must be prevented

Blockade of Airway Inflammation by Kaempferol via Disturbing Tyk-STAT Signaling in Airway Epithelial Cells and in Asthmatic Mice

Asthma is characterized by bronchial inflammation causing increased airway hyperresponsiveness and eosinophilia. The interaction between airway epithelium and inflammatory mediators plays a key role in the asthmatic pathogenesis. The in vitro study elucidated inhibitory effects of kaempferol, a flavonoid found in apples and many berries, on inflammation in human airway epithelial BEAS-2B cells. Nontoxic kaempferol at ≤20 μM suppressed the LPS-induced IL-8 production through the TLR4 activation, inhibiting eotaxin-1 induction. The in vivo study explored the demoting effects of kaempferol on asthmatic inflammation in BALB/c mice sensitized with ovalbumin (OVA). Mouse macrophage inflammatory protein-2 production and CXCR2 expression were upregulated in OVA-challenged mice, which was attenuated by oral administration of ≥10 mg/kg kaempferol. Kaempferol allayed the airway tissue levels of eotaxin-1 and eotaxin receptor CCR3 enhanced by OVA challenge. This study further explored the blockade of Tyk-STAT signaling by kaempferol in both LPS-stimulated BEAS-2B cells and OVA-challenged mice. LPS activated Tyk2 responsible for eotaxin-1 induction, while kaempferol dose-dependently inhibited LPS- or IL-8-inflamed Tyk2 activation. Similar inhibition of Tyk2 activation by kaempferol was observed in OVA-induced mice. Additionally, LPS stimulated the activation of STAT1/3 signaling concomitant with downregulated expression of Tyk-inhibiting SOCS3. In contrast, kaempferol encumbered STAT1/3 signaling with restoration of SOCS3 expression. Consistently, oral administration of kaempferol blocked STAT3 transactivation elevated by OVA challenge. These results demonstrate that kaempferol alleviated airway inflammation through modulating Tyk2-STAT1/3 signaling responsive to IL-8 in endotoxin-exposed airway epithelium and in asthmatic mice. Therefore, kaempferol may be a therapeutic agent targeting asthmatic diseases

Identification of protein functions using a machine-learning approach based on sequence-derived properties

<p>Abstract</p> <p>Background</p> <p>Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.</p> <p>Results</p> <p>A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.</p> <p>Conclusion</p> <p>We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new <it>PNPRD </it>features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.</p