Biodiesel is a fuel composed of monoalkyl esters of long-chain fatty acids derived from renewable biomass sources. In this study, biomass waste pecan nutshell (PS) was attempted to be converted into microbial oil. For effective utilization of PS, sequential pretreatment with ethylene glycol-HSO-water (78:2:20, wt:wt:wt) at 130 °C for 30 min and aqueous ammonia (25 wt%) at 50 °C for 24 h was used to enhance its enzymatic saccharification. Significant linear correlation was obtained about delignification-saccharification (R = 0.9507). SEM and FTIR results indicated that combination pretreatment could effectively remove lignin and xylan in PS for promoting its enzymatic saccharification. After 72 h, the reducing sugars from the hydrolysis of 50 g/L pretreated PS by combination pretreatment could be obtained at 73.6% yield. Using the recovered PS hydrolysates containing 20 g/L glucose as carbon source, microbial lipids produced from the PS hydrolysates by Rhodococcus opacus ACCC41043. Four fatty acids including palmitic acid (C16:0; 23.1%), palmitoleic acid (C16:1; 22.4%), stearic acid (C18:0; 15.3%), and oleic acid (C18:1; 23.9%) were distributed in total fatty acids. In conclusion, this strategy has potential application in the future.
Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Interindividual variability in response to warfarin therapy results in difficulties in the initial dose decision. Traditional pharmacogenetics-based dosing algorithms can only explain ~ 50% of the variation in the maintenance dose. WHAT QUESTION DID THIS STUDY ADDRESS? This study identified a set of novel metabolic predictors of warfarin therapy. By integrating the baseline metabolic biomarkers, genotypes, and clinical information, patients with extreme responses to warfarin can be selected, and an effective dose range at the initial 7-day warfarin therapy can be effectively predicted for an individual by machine learning.
The current quality control methods relying mainly on chromogenic reaction can hardly ensure the quality and safety of the biochemical drug with complex chemical composition. Therefore, a chromatographic fingerprint method was developed for the quality evaluation of a multicomponent biochemical drug, transfer factor injection. High-performance liquid chromatography fingerprint was measured by using a C 18 column (250 × 4.6 mm, 5 µm) with a mobile phase composed of 0.1% trifluoroacetic acid-water and 0.085% trifluoroacetic acid-acetonitrile under gradient elution. The developed method was validated and was subsequently applied to 57 batches of commercial products which were sampled by National Drug Assessment Program.High-resolution mass spectrometry analysis was performed on characteristic peaks of fingerprints, and a series of amino acids, nucleosides, and deoxynucleosides were identified. In the fingerprint assessments, principal component analysis and Hotelling T 2 analysis yielded the best results. The results generally indicated that there was a significant difference among products of batch-to-batch or from different manufacturers. Abnormal samples and its discriminatory components were also explored. In summary, the established fingerprinting method with multivariate statistical analysis could offer an efficient, reliable, and practical approach for quality consistency evaluation of transfer factor injection, providing a reference for the quality control of other multicomponent biochemical drugs.
K E Y W O R D Shigh-performance liquid chromatography fingerprinting, multivariate statistical analysis, multicomponent biochemical drugs, transfer factor injection, quality consistency evaluation 2042
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