UPLC-Q-TOF-MS/MS combined with machine learning methods for screening quality indicators of Hypericum perforatum L.

Hypericum perforatum L. (HPL), also known as St. John's wort, is one of the extensively researched domestically and internationally as a medicinal plant. In this study, non-targeted metabolomics combined with machine learning methods were used to identify reasonable quality indicators for the holistic quality control of HPL. First, the high-resolution MS data from different samples of HPL were collected, and visualized the chemical compounds through the MS molecular network. A total of 122 compounds were identified. Then, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established for comparing the differences in metabolite expression between flower, leaf, and branches. A total of 46 differential metabolites were screened out. Subsequently, analyzing the pharmacological activities of these differential metabolites based on protein-protein interaction (PPI) network. A total of 25 compounds associated with 473 gene targets were retrieved. Among them, 13 highly active compounds were selected as potential quality markers, and five compounds were ultimately selected as quality control markers for HPL. Finally, three different classifiers (support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN)) were used to validate whether the selected quality control markers are qualified. When the feature count is set to 122 and 46, the RF model demonstrates optimal performance. As the number of variables decreases, the performance of the RF model degrades. The KNN model and the SVM model also exhibit a decrease in performance but still manage to satisfy the intended requirements. The strategy can be applied to the quality control of HPL and can provide a reference for the quality control of other herbal medicines.

Network pharmacology and experimental verification study on the mechanism of Hedyotis diffusa Willd in treating colorectal cancer.

This study aimed to evaluate the pharmacological mechanism of Hedyotis diffusa Willd against CRC (colorectal cancer) using network pharmacological analysis combined with experimental validation. The active components and potential targets of Hedyotis diffusa Willd were screened from the tax compliance management program public database using network pharmacology. The core anti-CRC targets were screened using a protein-protein interaction (PPI) network. The mRNA and protein expression of core target genes in normal colon and CRC tissues and their relationship with overall CRC survival were evaluated using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Functional and pathway enrichment analyses of the potential targets were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The first six core targets with stable binding were molecular-docked with the active components quercetin and ß-sitosterol. Finally, the results of network pharmacology were verified using in vitro experiments. In total, 149 potential targets were identified by searching for seven types of active components and the intersection of all potential and CRC targets. PPI network analysis showed that ten target genes, including tumor protein p53 (TP53) and recombinant cyclin D1 (CCND1), were pivotal genes. GO enrichment analysis involved 2043 biological processes, 52 cellular components, and 191 molecular functions. KEGG enrichment analysis indicated that the anticancer effects of H. alba were mediated by tumor necrosis factor, interleukin-17, and nuclear factor-κB (NF-κB) signaling pathways. Validation of key targets showed that the validation results for most core genes were consistent with those in this study. Molecular docking revealed that the ten core target proteins could be well combined with quercetin and ß-sitosterol and the structure remained stable after binding. The results of the in vitro experiment showed that ß-sitosterol inhibited proliferation and induced apoptosis in SW620 cells. This study identified a potential target plant for CRC through network pharmacology and in vitro validation.

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine.

In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.

Deep Learning Models for SO2 Distribution in a 30 MW Boiler via Computational Fluid Dynamics Simulation Data.

The distribution of SO2 in a boiler is an important factor affecting tube corrosion in a furnace. To investigate the correlation between SO2 distribution and numerous variables (e.g., temperature, O2 distribution, etc.), a hybrid deep learning model is developed via the computational fluid dynamics (CFD) simulation data. First, the combustion process under typical working conditions is simulated to output the training data set. Then, a LASSO algorithm is adopted to select input variables with a high correlation with SO2 distribution. Finally, a deep belief network combined with a restricted belief machine and a fully connected layer is developed to describe the nonlinear relationship. The proposed model is the first work to use a deep learning algorithm to obtain the correlation between SO2 distribution and other products of combustion. The results show that O2 concentration has the highest influence on SO2 distribution.