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Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.
This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).
Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.
The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.
The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.
A field study to optimize the nitrogen (N) top-dressing mode of panicle fertilization and improve rice yields was conducted in mid-eastern China. Japonica cultivar Yunongjing-6 was grown and panicle N fertilizer was applied at the beginning of the inverted fourth leaf stage using three different modes: manual broadcast application (BA) on the soil surface as a control treatment, deep application during ditching at a depth of 15 cm (DD) and manual BA on the soil surface during deep ditching (BAD). The activity of soil enzymes, including invertase, urease, phosphatase and catalase, was increased significantly at the jointing, booting and grain-filling stages with the DD and BAD treatments compared with the traditional BA mode. The DD and BAD treatments also increased basal internode and neck-panicle internode bleeding intensity. The DD treatment gave the highest crop yield, increasing the yield by 0.63 and 0.31 t/hm2 in 2011 and 2012, respectively, compared with BA. The results suggest that ditching during panicle N fertilizer application after sun-drying of the fields increases rice yields, most likely by improving the activity of soil enzymes and enhancing the physiological activity of roots and grain weight.
Underground Nuclear Astrophysics in China (JUNA) will take the advantage of the ultra-low background in Jinping underground lab. High current accelerator with an ECR source and detectors were commissioned. JUNA plans to study directly a number of nuclear reactions important to hydrostatic stellar evolution at their relevant stellar energies. At the first period, JUNA aims at the direct measurements of 25Mg(p,γ)26 Al, 19F(p,α) 16 O, 13C(α, n) 16O and 12C(α,γ) 16O near the Gamow window. The current progress of JUNA will be given.
Despite extensive literature on contributing factors to the high commodityprices and volatility in the recent years, few have examined these causalfactors together in one analysis. We quantify empirically the relativeimportance of three factors: global demand, speculation, and energyprices/policy in explaining corn price volatility. A structural vectorauto-regression model is developed and variance decomposition is applied tomeasure the contribution of each factor in explaining corn price variation.We find that speculation is important, but only in the short run. However,in the long run, energy is the most important followed by global demand.
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