Artificial neural network-genetic algorithm-based optimization of aerobic composting process parameters of Ganoderma lucidum residue.

The rapid development of traditional Chinese medicine enterprises has put forward higher requirements for the resource utilization of traditional Chinese medicine residues (TCMR). Aerobic composting of TCMR to prepare bio-organic fertilizer is an effective resource utilization method. In this study, a back-propagation artificial neural network (BPNN) model using composting factors as inputs (C/N, initial moisture content, type of inoculant, composting days) and the humic acid content as the output was constructed based on the orthogonal test data. BPNN-GA (a genetic algorithm) was used for extreme value optimization, and the optimal composting process parameter combination was obtained and verified. The results show that the combination of orthogonal testing and BPNN can effectively establish the relationship between the composting process parameters and humic acid content. The R2 value was 0. 9064. The optimized parameter combination is as follows: C/N,37.42; moisture content,69.76%; bacteria,no; and composting time,50 d.

Distribution of traditional Chinese medicine syndromes in type 2 diabetes mellitus with chronic heart failure: A clinical study.

INTRODUCTION: The incidence of type 2 diabetes has been increasing year by year in recent years. Type 2 diabetes is an important risk factor in the occurrence and development of heart failure, and it is the second potential risk factor after coronary artery disease. At present, there is no unified etiology, pathogenesis, and syndrome differentiation criteria for type 2 diabetes with chronic heart failure, and it is susceptible to subjective factors. Therefore, standardized, objective, and standardized research is needed to provide reference and guidance for clinical diagnosis and treatment. In this study, the theory of syndrome differentiation is used to initially explore the distribution of traditional Chinese medicine syndromes in patients with type 2 diabetes and chronic heart failure through case data collection, syndrome extraction, and clinical data analysis. METHODS/DESIGN: In this study, we will collect at least 500 cases of type 2 diabetes with chronic heart failure that meet the standard outpatient and hospitalization, and fill out the case information collection form. Then we will collect a number of clinical diagnosis and treatment information, and judge the syndrome based on the sum of the contribution of each syndrome to the relevant syndrome. We will use Microsoft Excel to establish a database, enter the relevant diagnosis and treatment, and syndrome information of the case information collection table, and verify and correct in time to ensure the accuracy of the data. DISCUSSION: This study will provide reference and guidance for the clinical diagnosis and treatment of type 2 diabetes with chronic heart failure. TRIAL REGISTRATION: ClinicalTrials.gov, ChiCTR2000033010, Registered on May 18, 2020.

Impact of Heavy Metal Pollution on Ammonia Oxidizers in Soils in the Vicinity of a Tailings Dam, Baotou, China.

Soil heavy metal pollution has received increasing attention due to their toxicity to soil microorganisms. We have analyzed the effects of heavy metal pollution on ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in soils in the vicinity of a tailings dam of Baotou region, China. Results showed that AOB were dominated with Nitrosomonas-like clusters, while AOA was dominated by group1.1b (Nitrososphaera cluster). Single Cd and Cr contents, as well as compound heavy metal pollution levels, had a significant negative impact on soil potential nitrification rate and both diversities of AOA and AOB. No clear relationship was found between any single heavy metal and abundance of AOA or AOB. But compound pollution could significantly decrease AOA abundance. The results indicated that heavy metal pollution had an obviously deleterious effect on the abundance, diversity, activity and composition of ammonia oxidizers in natural soils.

The orphan nuclear receptor Nur77 regulates LKB1 localization and activates AMPK.

Liver kinase B1 (LKB1) has important roles in governing energy homeostasis by regulating the activity of the energy sensor kinase AMP-activated protein kinase (AMPK). The regulation of LKB1 function, however, is still poorly understood. Here we demonstrate that the orphan nuclear receptor Nur77 binds and sequesters LKB1 in the nucleus, thereby attenuating AMPK activation. This Nur77 function is antagonized by the chemical compound ethyl 2-[2,3,4-trimethoxy-6-(1-octanoyl)phenyl]acetate (TMPA), which interacts with Nur77 with high affinity and at specific sites. TMPA binding of Nur77 results in the release and shuttling of LKB1 to the cytoplasm to phosphorylate AMPKα. Moreover, TMPA effectively reduces blood glucose and alleviates insulin resistance in type II db/db and high-fat diet- and streptozotocin-induced diabetic mice but not in diabetic littermates with the Nur77 gene knocked out. This study attains a mechanistic understanding of the regulation of LKB1-AMPK axis and implicates Nur77 as a new and amenable target for the design and development of therapeutics to treat metabolic diseases.