SUMMARYSince quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach under standard conditions, and the proposed estimator reduces to the classical one if there is no crossing. The performance of the constrained estimator has shown significant improvement by adding smoothing and stability across the quantile levels.

In this study, multilayer perceptron artificial neural networks are used to predict orthodontic treatment plans, including the determination of extraction-nonextraction, extraction patterns, and anchorage patterns. The neural network can output the feasibilities of several applicable treatment plans, offering orthodontists flexibility in making decisions. The neural network models show an accuracy of 94.0% for extraction-nonextraction prediction, with an area under the curve (AUC) of 0.982, a sensitivity of 94.6%, and a specificity of 93.8%. The accuracies of the extraction patterns and anchorage patterns are 84.2% and 92.8%, respectively. The most important features for prediction of the neural networks are “crowding, upper arch” “ANB” and “curve of Spee”. For handling discrete input features with missing data, the average value method has a better complement performance than the k-nearest neighbors (k-NN) method; for handling continuous features with missing data, k-NN performs better than the other methods most of the time. These results indicate that the proposed method based on artificial neural networks can provide good guidance for orthodontic treatment planning for less-experienced orthodontists.

BackgroundHigh-flow nasal cannula oxygen (HFNC) is a relatively new therapy used in adults with respiratory failure. Whether it is superior to conventional oxygen therapy (COT) or to noninvasive mechanical ventilation (NIV) remains unclear. The aim of the present study was to investigate whether HFNC was superior to either COT or NIV in adult acute respiratory failure patients.MethodsA review of the literature was conducted from the electronic databases from inception up to 20 October 2016. Only randomized clinical trials comparing HFNC with COT or HFNC with NIV were included. The intubation rate was the primary outcome; secondary outcomes included the mechanical ventilation rate, the rate of escalation of respiratory support and mortality.ResultsEleven studies that enrolled 3459 patients (HFNC, n = 1681) were included. There were eight studies comparing HFNC with COT, two comparing HFNC with NIV, and one comparing all three. HFNC was associated with a significant reduction in intubation rate (OR 0.52, 95% CI 0.34 to 0.79, P = 0.002), mechanical ventilation rate (OR 0.56, 95% CI 0.33 to 0.97, P = 0.04) and the rate of escalation of respiratory support (OR 0.45, 95% CI 0.31 to 0.67, P < 0.0001) when compared to COT. There was no difference in mortality between HFNC and COT utilization (OR 1.01, 95% CI 0.67 to 1.53, P = 0.96). When HFNC was compared to NIV, there was no difference in the intubation rate (OR 0.96; 95% CI 0.66 to 1.39, P = 0.84), the rate of escalation of respiratory support (OR 1.00, 95% CI 0.77 to 1.28, P = 0.97) or mortality (OR 0.85, 95% CI 0.43 to 1.68, P = 0.65).ConclusionsCompared to COT, HFNC reduced the rate of intubation, mechanical ventilation and the escalation of respiratory support. When compared to NIV, HFNC showed no better outcomes. Large-scale randomized controlled trials are necessary to prove our findings.Trial registrationPROSPERO International prospective register of systematic reviews on May 25, 2016 registration no. CRD42016039581.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1760-8) contains supplementary material, which is available to authorized users.

The aim of this meta-analysis is to examine the effects of dexmedetomidine on serum inflammatory markers when administered perioperatively. We searched multiple electronic databases for relevant research papers, and carried out meta-analyses of weighted mean differences and interpreted in the light of statistical heterogeneity (I2). Fifteen RCTs recruiting 641 patients were included. Dexmedetomidine treatment significantly decreased interleukin-6 (IL-6), IL-8 and tumor necrosis factor-alpha (TNF-α) levels with mean differences [95% CI] in the changes from baseline between dexmedetomidine treated and controls of −25.14 [−35.29, −15.00]; P < 0.00001 (for IL-6), −5.69 [−10.77, −0.60]; P < 0.04 (for IL-8), and −20.30 [−30.93, −9.67]; P < 0.0002 (for TNF-α) immediately after surgery; and −41.55 [−57.41, −25.70]; P < 0.00001 (IL-6), −6.46 [−10.83, −2.08]; P < 0.005 (IL-8), and −14.67 [−22.61, −6.73]; P < 0.0003 (TNF-α) on postoperative day 1 (random effects). IL-10 levels were found to increase significantly a day after surgery (8.33 [3.31, 13.36]; P = 0.001). Subgroup analyses did not reveal significant differences. In conclusion, perioperative adjunctive use of dexmedetomidine substantially decreases serum IL-6, IL-8 and TNF-α levels.

A growing corpus of evidence implicates the involvement of the commensal microbiota and immune cytokines in the initiation and progression of systemic lupus erythematosus (SLE). Glucocorticoids have been widely used in the treatment of SLE patients, however, glucocorticoid treatment carries a higher risk of other diseases. Using the 16S rRNA technique, we investigated the differences between the gut microbiota associated with the immune cytokines of SLE and relevant glucocorticoid treatment in a female cohort of 20 healthy control subjects (HC), 17 subjects with SLE (SLE-G), and 20 SLE patients having undergone glucocorticoid treatment (SLE+G). We observed that the diversity and structure of the microbial community in SLE+G patients were significantly changed compared to that of SLE-G patients, whereas the gut microbial community of the SLE+G group showed a similarity with the HC group, which implicate that the shift in the gut microbiome could represent a return to homeostasis. Furthermore, the up-regulations of immune cytokines in SLE-G were identified as closely related to gut dysbiosis, which indicates that the overrepresented genera in SLE patients may play roles in regulating expression level of these immune cytokines. This associated analysis of gut microbiota, glucocorticoid therapy, and immune factors might provide novel and insightful clues revealing the pathogenesis of SLE patients.