Network pharmacology and metabolomics elucidate the underlying mechanisms of Venenum Bufonis in the treatment of colorectal cancer.

ETHNOPHARMACOLOGICAL RELEVANCE: The present study aims to evaluate the efficacy of Venenum Bufonis (VBF), a traditional Chinese medicine derived from the dried secretions of the Chinese toad, in treating colorectal cancer (CRC). The comprehensive roles of VBF in CRC through systems biology and metabolomics approaches have been rarely investigated. AIMS OF THE STUDY: The study sought to uncover the potential underlying mechanisms of VBF's anti-cancer effects by investigating the impact of VBF on cellular metabolic balance. MATERIALS AND METHODS: An integrative approach combining biological network analysis, molecular docking and multi-dose metabolomics was used to predict the effects and mechanisms of VBF in CRC treatment. The prediction was verified by cell viability assay, EdU assay and flow cytometry. RESULTS: The results of the study indicate that VBF presents anti-CRC effects and impacts cellular metabolic balance through its impact on cell cycle-regulating proteins, such as MTOR, CDK1, and TOP2A. The results of the multi-dose metabolomics analysis suggest a dose-dependent reduction of metabolites related to DNA synthesis after VBF treatment, while the EdU and flow cytometry results indicate that VBF inhibits cell proliferation and arrests the cell cycle at the S and G2/M phases. CONCLUSIONS: These findings suggest that VBF disrupts purine and pyrimidine pathways in CRC cancer cells, leading to cell cycle arrest. This proposed workflow integrating molecular docking, multi-dose metabolomics, and biological validation, which contented EdU assay, cell cycle assay, provides a valuable framework for future similar studies.

Feasibility of an NIR spectral calibration transfer algorithm based on optimized feature variables to predict tobacco samples in different states.

The prediction accuracy of calibration models for near-infrared (NIR) spectroscopy typically relies on the morphology and homogeneity of the samples. To achieve non-homogeneous tobacco samples for non-destructive and rapid analysis, a method that can predict tobacco filament samples using reliable models based on the corresponding tobacco powder is proposed here. First, as it is necessary to establish a simple and robust calibrated model with excellent performance, based on full-wavelength PLSR (Full-PLSR), the key feature variables were screened by three methods, namely competitive adaptive reweighted sampling (CARS), variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV), and variable combination population analysis-genetic algorithm (VCPA-GA). The partial least squares regression (PLSR) models for predicting the total sugar content in tobacco were established based on three optimal wavelength sets and named CARS-PLSR, VCPA-IRIV-PLSR and VCPA-GA-PLSR, respectively. Subsequently, they were combined with different calibration transfer algorithms, including calibration transfer based on canonical correlation analysis (CTCCA), slope/bias correction (S/B) and non-supervised parameter-free framework for calibration enhancement (NS-PFCE), to evaluate the best prediction model for the tobacco filament samples. Compared with the previous two transfer algorithms, NS-PFCE performed the best under various wavelength conditions. The prediction results indicated that the most successful approach for predicting the tobacco filament samples was achieved by VCPA-IRIV-PLSR when coupled with the NS-PFCE method, which obtained the highest determination coefficient (Rp2 = 0.9340) and the lowest root mean square error of the prediction set (RMSEP = 0.8425). VCPA-IRIV simplifies the calibration model and improves the efficiency of model transfer (31 variables). Furthermore, it pledges the prediction accuracy of the tobacco filament samples when combined with NS-PFCE. In summary, calibration transfer based on optimized feature variables can eliminate prediction errors caused by sample morphological differences and proves to be a more beneficial method for online application in the tobacco industry.

Synthesis of Tropine-Based Functionalized Acidic Ionic Liquids and Catalysis of Esterification.

Some traditional acidic ionic liquids (AILs) have shown great catalytic potential in esterification; meanwhile, the design and application of more new AILs are expected at present.Tropine-based functionalized acidic ionic liquids (FAILs) were synthesized to realize esterification catalysis for the first time; with aspirin synthesis as the template reaction, key influences on the substrate conversion and product yield of the synthesis, such as IL type, ratio of salicylic acid to acetic anhydride, temperature, reaction time and amount of IL, were investigated. The new tropine-based FAILs exhibited excellent performance in catalytic synthesis of aspirin with 88.7% yield and 90.8% selectivity. Multiple recovery and re-usage of N-(3-propanesulfonic acid) tropine is the cation, and p-toluenesulfonic acid is the anion. ([Trps][OTs]) shows satisfactory results. When [Trps][OTs] was used to catalyze different esterification reactions, it also showed good results. The above studies proved that ionic liquid [Trps][OTs] could serve as an ideal green solvent for esterification reaction, which serves as a suitable substitute for current catalysts.

Across different instruments about tobacco quantitative analysis model of NIR spectroscopy based on transfer learning.

With the development of near-infrared (NIR) spectroscopy, various calibration transfer algorithms have been proposed, but such algorithms are often based on the same distribution of samples. In machine learning, calibration transfer between types of samples can be achieved using transfer learning and does not need many samples. This paper proposed an instance transfer learning algorithm based on boosted weighted extreme learning machine (weighted ELM) to construct NIR quantitative analysis models based on different instruments for tobacco in practical production. The support vector machine (SVM), weighted ELM, and weighted ELM-AdaBoost models were compared after the spectral data were preprocessed by standard normal variate (SNV) and principal component analysis (PCA), and then the weighted ELM-TrAdaBoost model was built using data from the other domain to realize the transfer from different source domains to the target domain. The coefficient of determination of prediction (R 2) of the weighted ELM-TrAdaBoost model of four target components (nicotine, Cl, K, and total nitrogen) reached 0.9426, 0.8147, 0.7548, and 0.6980. The results demonstrated the superiority of ensemble learning and the source domain samples for model construction, improving the models' generalization ability and prediction performance. This is not a bad approach when modeling with small sample sizes and has the advantage of fast learning.

Exploring the Absorption Mechanisms of Imidazolium-Based Ionic Liquids to Epigallocatechin Gallate.

Imidazolium-based ionic liquids are wildly used in natural product adsorption and purification. In this work, one typical polymeric ionic liquid (PIL) was synthesized by using L-proline as the anion, which exhibited excellent adsorption capacity toward tea polyphenol epigallocatechin gallate (EGCG). The adsorption conditions were optimized with the response surface method (RSM). Under the optimum conditions, the adsorption capacity of the PIL for EGCG can reach as high as 552 mg/g. Dynamics and isothermal research shows that the adsorption process of EGCG by the PIL particularly meets the quasi-second-order kinetic equation and monolayer adsorption mechanism. According to thermodynamic parameter analysis, the adsorption process is endothermic and spontaneous. The results of theoretical calculation by molecular docking also demonstrated the interaction mechanisms between EGCG and the ionic liquid. Considering the wide application of imidazolium-based ionic liquids in component adsorption and purification, the present study can not only be extended to other similar experimental mechanism validation, but also be representative for guiding the synthesis of PIL and optimization of adsorption conditions.

Difenoconazole induces oxidative DNA damage and mitochondria mediated apoptosis in SH-SY5Y cells.

Difenoconazole is one of the most typical triazole fungicides. Difenoconazole is widely used in the field of agricultural production, and its health and safety problems need to be further studied. The main purpose of this paper is to verify the neurotoxicity of Difenoconazole at the cellular level. In this study, SH-SY5Y cell line of human neuroblastoma was used to evaluate its potentially toxic effects and molecular mechanism in vitro. The research indicated that Difenoconazole could reduce cell viability and inhibit cell proliferation, induce DNA damage and accelerate programmed cell death. Further studies showed that Difenoconazole induced DNA double-strand breaks, intracellular generation of ROS, cleaved PARP, mitochondrial membrane potential collapse, induced Cyt c release, and Bax/Bcl-2 ratio increase in SH-SY5Y cells. In conclusion, the cytotoxicity of Difenoconazole revealed its toxic effect on SH-SY5Y cells, and the IC50 value was 55.41 µM after 24 h exposure. Meanwhile, the genetic toxicity of Difenoconazole has revealed that it can induce DNA damage and apoptosis of SH-SY5Y cells. Through this study, the toxic effects of Difenoconazole on SH-SY5Y cells are further understood, which provides a more scientific basis for its safe use and risk control.

[Research progress of continuous manufacturing and process knowledge system of traditional Chinese medicine].

In the past few years, continuous manufacturing(CM) has been put forward by the FDA. Pharmaceutical enterprises are encouraged to promote the implementation of CM, which has become a hot research direction of pharmaceutical technology. In February 2019, the FDA issued a draft guideline for the implementation of CM, which greatly promoted the development of CM and provided reference for continuous manufacturing of traditional Chinese medicine(TCM). The production process of TCM is a complex system. With the innovation of production equipment and the promotion of automation and informatization of TCM production, the exis-ting policies, regulations and traditional production control capacity are difficult to meet the market demand for high-quality TCM pro-ducts. In this paper, we reviewed the new technologies and methods of quality control in accordance with the characteristics of TCM production by referring to modern manufacturing technology, information technology and quality control technology. Based on the "QbD" theory and "PAT" technology, process knowledge system(PKS), an advanced control strategy, was proposed to provide a reference for the implementation of CM in TCM production.

[Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine].

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics.

The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.

Rapid monitoring approaches for concentration process of lanqin oral solution by near-infrared spectroscopy and chemometric models.

Qualitative and quantitative detection methods based on near-infrared spectroscopy (NIRs) have been proposed in the process analysis of traditional Chinese medicine in recent years. In this study, rapid monitoring methods were developed for quality control of concentration process of lanqin oral solution (LOS). Partial least squares regression (PLSR) method was adopted to construct quantitative models for epigoitrin, geniposide, baicalin, berberine hydrochloride and density. Simultaneously, the genetic algorithm joint extreme learning machine (GA-ELM) was first applied in qualitative analysis of NIRs to distinguish end point of concentration process. Results of PLSR models were satisfactory with the relative standard error of calibration valued at 3.80%, 3.75%, 3.79%, 11.5% and 1.22% for epigoitrin, geniposide, baicalin, berberine hydrochloride and density respectively, and the residual predictive deviation values were higher than 3. For qualitative analysis, the GA-ELM model obtained 100% prediction accuracy. The PLSR quantitative models and the end point discrimination model constructed by GA-ELM correspond with the requirements of practical applications. The results indicate that NIRs in combination with chemometrics has great potential in improving the efficiency in production.

Ivermectin confers its cytotoxic effects by inducing AMPK/mTOR-mediated autophagy and DNA damage.

Ivermectin (IVM), a broad-spectrum antiparasitic drug, is widely used in agriculture and animal husbandry. Due to widespread use and little metabolism in animals, the toxicity of IVM has received increasing attention. The accumulation of IVM in animal tissues and the excretion of urine and feces in the environment is the major source of potential toxicity. Human consumption of meat or milk contaminated with livestock can result in exposure to high levels of IVM exposure. The aim of this study was to reveal the cytotoxic mechanism of IVM in model cell HeLa in vitro, in order to provide a theoretical basis for the safe and rational use of IVM. Here we observed the γH2AX and 8-oxodG foci to detect the DNA damage in HeLa cells. As expected, we found that IVM can induce oxidative double-stranded damage in HeLa cells, indicating that IVM has potential genotoxicity to human health. In addition, we observed the formation of LC3-B in HeLa cells, the accumulation of Beclin1, the degradation of p62 and the activation of the AMPK/mTOR signal transduction pathway. This suggests that IVM confers cytotoxicity through autophagy mediated by the AMPK/mTOR signaling pathway. We conclude that IVM produces genotoxicity and cytotoxicity by inducing DNA damage and AMPK/mTOR-mediated autophagy, thereby posing a potential risk to human health.

Evaluation of the cytotoxic effects of glyphosate herbicides in human liver, lung, and nerve.

Glyphosate-based herbicides are broad-spectrum pesticides widely used in the world, which is considered a highly safe pesticide due to their target specificity, but recently, there has been an ongoing controversy regarding their carcinogenicity and possible side effects of glyphosate on human health. Commercial glyphosate-based herbicides (GBHs) consist of declared active ingredient (glyphosate salts) and a number of formulants such as ethoxylated formulants (4130®, 3780®, and A-178®). The aim of our study is to investigate whether the toxicity of GBHs is related to formulants. The effects of GBHs on human health were studied at the cellular level based on their toxicity to liver, lungs and nerve tissue. The inhibitory toxicity to cell viability by GBHs was examined with cell-based systems using three human cell lines: HepG2, A549, and SH-SY5Y. Data obtained showed that all tested ethoxylated formulants and their mixtures with declared active ingredient glyphosate isopropylamine salt (GP) have significant inhibitory effect on cell proliferation, while the declared active ingredient has no significant toxicity. Our study demonstrates that the toxic effect of GBH is primarily due to the use of formulants. This result suggests that GP is relatively safe and a new approach for the assessment of toxicity should be made.

Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes.

Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.