The Ni/SAPO-11 bifunctional catalyst for hydroisomerization of n-hexane was prepared via a novel synthesis method. It involved grinding of nickel source with amorphous precursors used for SAPO-11 followed by crystallization at 473 K for 24 h, thus avoiding the use of extra solvents in the synthesis. The highly dispersed nickel species and acid sites in the Ni/SAPO-11 bifunctional catalyst were instantaneouslyformed. The Ni/SAPO-11 catalyst contains framework nickel, nickel monoxide (NiO) and nickel aluminate spinel. The nickel monoxide with a size of 2-4 nm provides (de)hydrogenation function after reduction, while the framework nickel supplies more acid sites leading to an enhancedisomerization activity.The Ni/SAPO-11 catalyst shows a great synergeticeffect between the metallic nickel and acid sites with a high metal-to-acid sites ratio (C Ni /C A ) and close proximity. A single metallic nickel site is able to balance ca. 5 acid sites (C Ni /C A ≈0.19) over the Ni/SAPO-11 catalyst in n-hexane hydroisomerization.The high dispersion of nickel over the catalyst provides relatively excessive metal sites (C Ni /C A >0.19), leaving the rate limiting reaction to occur on the acid sites. The Ni/SAPO-11 catalyst exhibits comparable n-hexane conversion (71.2%) and iso-hexane yield (66.7%) to the classical Pt/SAPO-11 catalyst.With enhancing acidity, the Ni/SAPO-11 catalyst exhibits one of the highest iso-hexane yields reported in the n-hexane hydroisomerization, which render the new material as a promising candidate for the hydroisomerization catalysts.
Dual antiplatelet therapy (DAPT) with clopidogrel plus aspirin within 48 h of acute minor strokes and transient ischemic attacks (TIAs) has been indicated to effectively reduce the rate of recurrent strokes. However, the efficacy of clopidogrel has been shown to be affected by cytochrome P450 2C19 (CYP2C19) polymorphisms. Patients carrying loss-of-function alleles (LoFAs) at a low risk of recurrence (ESRS < 3) cannot benefit from clopidogrel plus aspirin at all and may have an increased bleeding risk. In order to optimize antiplatelet therapy for these patients and avoid the waste of medical resources, it is important to identify the subgroups that genuinely benefit from DAPT with clopidogrel plus aspirin through CYP2C19 genotyping. This study sought to assess the cost-effectiveness of CYP2C19 genotyping to guide drug therapy for acute minor strokes or high-risk TIAs in China. A decision tree and Markov model were constructed to evaluate the cost-effectiveness of CYP2C19 genotyping. We used a healthcare payer perspective, and the primary outcomes included quality-adjusted life years (QALYs), costs and the incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to evaluate the robustness of the results. CYP2C19 genotyping resulted in a lifetime gain of 0.031 QALYs at an additional cost of CNY 420.13 (US$ 59.85), yielding an ICER of CNY 13,552.74 (US$ 1930.59) per QALY gained. Probabilistic sensitivity analysis showed that genetic testing was more cost-effective in 95.7% of the simulations at the willingness-to-pay threshold of CNY 72,100 (GDP per capita, US$ 10,300) per QALY. Therefore, CYP2C19 genotyping to guide antiplatelet therapy for acute minor strokes and high-risk TIAs is highly cost-effective in China.
Objectives: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. Methods: This study proposed a variable engineering process that automatically generates secondorder variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables. Results: Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups. Conclusion:The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.
Purpose This study aimed to establish an optimal model to predict vancomycin trough concentrations by using machine learning. Patients and Methods We enrolled 407 pediatric patients (age < 18 years) who received vancomycin intravenously and underwent therapeutic drug monitoring from June 2013 to April 2020 at Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. The median (interquartile range) age and weight of the patients were 2 (0.63–5) years and 12 (7.8–19) kg. Vancomycin trough concentrations were considered as the target variable, and eight different algorithms were used for predictive performance comparison. The whole dataset (407 cases) was divided into training group and testing group at the ratio of 80%: 20%, which were 325 and 82 cases, respectively. Results Ultimately, five algorithms (XGBoost, GBRT, Bagging, ExtraTree and decision tree) with high R 2 (0.657, 0.514, 0.468, 0.425 and 0.450, respectively) were selected and further ensembled to establish the final model and achieve an optimal result. For missing data, through filling the missing values and model ensemble, we obtained R 2 =0.614, MAE=3.32, MSE=24.39, RMSE=4.94 and a prediction accuracy of 51.22% (predicted trough concentration within ±30% of the actual trough concentration). In comparison with the pharmacokinetic models ( R 2 =0.3), the machine learning model works better in model fitting and has better prediction accuracy. Conclusion Therefore, the ensemble model is useful for the vancomycin concentration prediction, especially in the population of children with great individual variation. As machine learning methods evolve, the clinical value of the ensemble model will be demonstrated in the clinical practice.
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