Development of LC–MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat

Safety Risk Assessment Using a BP Neural Network of High Cutting Slope Construction in High-Speed Railway

High-speed railway construction is extending to mountainous areas, and the harsh environment and complex climate pose various risks to the slope construction. This seriously threatens human lives and causes huge economic losses. The existing research results on the construction safety risks of high cutting slope construction in HSRs are limited, and a complete set of safety risk assessment processes and methods has not yet been formed. Therefore, in this study, we aimed to develop a safety risk assessment model, including factor identification and classification and assessment data processing, to help project managers evaluate safety risks in high cutting slope construction. In this study, comprehensive identification of high cutting slope construction safety risks was carried out from three dimensions, risk technical specification, literature analysis, and case statistical analysis, and a list of risk-influencing factors was formed. Based on the historical data, a high side slope risk evaluation model was established using a BP neural network algorithm. The model was applied to the risk evaluation of HF high cutting slopes. The results show that the risk evaluation level is II; the main risks are earthwork excavation method, scaffolding equipment, slope height, slope rate, groundwater, personnel safety awareness, and construction safety risk management system. Finally, a case study was used to verify the proposed model, and control measures for safety risks were proposed. Our findings will help conduct effective safety management, add to the knowledge of construction safety risk management in terms of implementation, and offer lessons and references for future construction safety management of HSR.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

A cumulative-risk assessment method based on an artificial neural network model for the water environment

To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10^-6. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.

Pharmacokinetic Interaction Study of Ketamine and Rhynchophylline in Rat Plasma by Ultra-Performance Liquid Chromatography Tandem Mass Spectrometry

Eighteen Sprague-Dawley rats were randomly divided into three groups: ketamine group, rhynchophylline group, and ketamine combined with rhynchophylline group (n = 6). The rats of two groups received a single intraperitoneal administration of 30 mg/kg ketamine and 30 mg/kg rhynchophylline, respectively, and the third group received combined intraperitoneal administration of 30 mg/kg ketamine and 30 mg/kg rhynchophylline together. After blood sampling at different time points and processing, the concentrations of ketamine and rhynchophylline in rat plasma were determined by the established ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method. Chromatographic separation was achieved using a UPLC BEH C18 column (2.1 mm × 50 mm, 1.7 μm) with carbamazepine as an internal standard (IS). The initial mobile phase consisted of acetonitrile and water (containing 0.1% formic acid) with gradient elution. Multiple reaction monitoring (MRM) modes of m/z 238.1 → 179.1 for ketamine, m/z 385.3 → 159.8 for rhynchophylline, and m/z 237.3 → 194.3 for carbamazepine (IS) were utilized to conduct quantitative analysis. Calibration curve of ketamine and rhynchophylline in rat plasma demonstrated good linearity in the range of 1-1000 ng/mL (r > 0.995), and the lower limit of quantification (LLOQ) was 1 ng/mL. Moreover, the intra- and interday precision relative standard deviation (RSD) of ketamine and rhynchophylline were less than 11% and 14%, respectively. This sensitive, rapid, and selective UPLC-MS/MS method was successfully applied to pharmacokinetic interaction study of ketamine and rhynchophylline after intraperitoneal administration. The results showed that there may be a reciprocal inhibition between ketamine and rhynchophylline.

Effective Separation and Simultaneous Determination of Corynoxeine and Its Metabolites in Rats by High-Performance Liquid Chromatography with Tandem Mass Spectrometry and Application to Pharmacokinetics and In Vivo Distribution in Main Organs

An effective separation and simultaneous determination of corynoxeine and its metabolites using high-performance liquid chromatography with tandem mass spectrometry was developed and validated. The method was applied to pharmacokinetics and in vivo distribution investigations in rats after oral (0.105 mmol kg(-1)) and intravenous (0.0105 mmol kg(-1)) doses of corynoxeine. Its brain uptake index was of 3.08 × 10(-11) mol g(-1) at 3 h and 3.75 × 10(-11) mol g(-1) at 74 min after oral and intravenous doses, respectively.

Association of blood glucose and lipid levels with complete blood count indices to establish a regression model

Hyperglycemia and hyperlipidemia, which are usually diagnosed by analysis of blood glucose (GLU) and lipid levels, are two of the most common diseases in modern society. The purpose of the current study was to investigate the potential association between blood GLU and lipid levels with complete blood count (CBC) indices in overweight and healthy individuals and establish a regression model. There were 456 healthy and 421 overweight participants in the study. Data were collected on triglyceride (TG), total cholesterol (CHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood GLU and CBC. The distribution characteristics and differences between healthy and overweight subjects were analyzed. Subsequently, the associations between TG, CHO, HDL, LDL and GLU with CBC were analyzed using correlation analysis and multiple linear regression (MLR). Significant differences were identified between the healthy and overweight individuals in TG, CHO, HDL, LDL, GLU and in the majority of the CBC indices. The correlation analysis indicated that there were strong correlations between TG, LDL, HDL, CHO and GLU with CBC indices in the healthy and overweight subjects. The MLR demonstrated that the regression models of TG, LDL, HDL and CHO, but no GLU, were statistically significant in the two groups (P<0.001). The HDL regression model exhibited the best regression parameters; the multiple correlation coefficients (R) were 0.351 and 0.308 in the healthy and overweight subjects, respectively. In the overweight and healthy subjects, there were strong correlations between TG, LDL, HDL and CHO with CBC indices, with HDL being the most relevant to the CBC indices. The CBC demonstrated statistical significance in the diagnosis of hyperlipidemia.

Site-Specific Drug-Releasing Polypeptide Nanocarriers Based on Dual-pH Response for Enhanced Therapeutic Efficacy against Drug-Resistant Tumors

To enhance effective drug accumulation in drug-resistant tumors, a site-specific drug-releasing polypeptide system (PEG-Phis/Pasp-DOX/CA4) was exploited in response to tumor extracellular and intracellular pH. This system could firstly release the embedded tumor vascular inhibitor (CA4) to transiently 'normalize' vasculature and facilitate drug internalization to tumors efficiently, and then initiate the secondary pH-response to set the conjugated active anticancer drug (DOX) free in tumor cells. The encapsulated system (PEG-Phis/DOX/CA4), both CA4 and DOX embedding in the nanoparticles, was used as a control. Comparing with PEG-Phis/DOX/CA4, PEG-Phis/Pasp-DOX/CA4 exhibited enhanced cytotoxicity against DOX-sensitive and DOX-resistant cells (MCF-7 and MCF-7/ADR). Moreover, PEG-Phis/Pasp-DOX/CA4 resulted in enhanced therapeutic efficacy in drug-resistant tumors with reduced toxicity. These results suggested that this site-specific drug-releasing system could be exploited as a promising treatment for cancers with repeated administration.

Pharmacokinetic interaction study of combining imatinib with dasatinib in rats by UPLC-MS/MS

This study examined whether oral administration of dasatinib to the rats with imatinib led to any pharmacokinetic interactions. Twenty-four rats were divided randomly into three groups, imatinib group (imatinib 25 mg/kg, n = 8), dasatinib group (dasatinib 15 mg/kg, n = 8) and co-administration group (dasatinib 15 mg/kg and imatinib 25 mg/kg, n = 8). The concentration of imatinib and dasatinib in rat plasma was determined by a sensitive and simple UPLC-MS/MS method. There was statistical pharmacokinetics difference for imatinib in the imatinib group and co-administration group, when co-oral administration imatinib with dasatinib, MRT(0-t) increased (p < 0.01). There was statistical pharmacokinetics difference for dasatinib in the dasatinib group and co-administration group, when co-oral administration dasatinib with imatinib, Cmax and AUC increased (p < 0.01), CL and V decreased (p < 0.01). These data indicate dasatinib could slightly influence the pharmacokinetic profile of imatinib in rats, and imatinib could influence the pharmacokinetic profile of dasatinib in rats, which might cause drug-drug interactions when using imatinib with dasatinib.

Clearance rate and BP-ANN model in paraquat poisoned patients treated with hemoperfusion

In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05–1.0 μg/mL) and High-PQ group (1.0–10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R = 0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model.

Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations.