Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling

Ultrasound-assisted optimization extraction and biological activities analysis of flavonoids from Sanghuangporus sanghuang

The fungus Sanghuangporus sanghuang possesses notable medicinal and edible characteristics, displaying a diverse array of biological functionalities. This research endeavor seeks to investigate the procedure of extracting flavonoids from S. sanghuang, and the qualitative and quantitative analysis of flavonoids extraction from S. sanghuang using ultra-performance liquid chromatography (UPLC), and assess its antioxidant capacity and potential antiproliferative properties. The ultrasonic-assisted extraction resulted in a 2.34-fold increase compared to the hot water extraction method. Response surface methodology (RSM) was employed to enhance the extraction process of flavonoids from S. sanghuang. The results indicated that the optimal extraction rate of S. sanghuang flavonoids were achieved at 16.16 ± 0.12 %. This was attained at an ultrasound temperature of 50°C using 80 % ethanol concentration and an ultrasound extraction time of 60 min. The S. sanghuang extract was analyzed using UPLC, resulting in the identification of twenty-six distinct compounds. The flavonoids derived from S. sanghuang have demonstrated the ability to effectively scavenge DPPH, superoxide anions (O2-·), and hydroxyl free radicals (·OH), in addition to exhibiting ferric reducing power. Furthermore, it exhibited inhibitory effects on α-glucosidase. The Pearson correlation analysis revealed a statistically significant positive correlation between the antioxidant capacities, encompassing DPPH, O2-·, ·OH, ferric reducing power, and the inhibited α-glucosidase capability. It has been determined that the activity of α-glucosidase can be inhibited by S. sanghuang flavonoids, and this inhibition can be predicted using a model developed with the MATLAB program. In the current investigation, the study successfully demonstrated the inhibitory effects of S. sanghuang flavonoids on cell proliferation and migration in glioma cells. This was achieved through the analysis of CCK-8 assay and wound healing assay, with statistical significance observed (p < 0.05).

Mathematical modeling and application of IL-1β/TNF signaling pathway in regulating chondrocyte apoptosis

Introduction: Mathematical model can be used to model complex biological processes, and have shown potential in describing apoptosis in chondrocytes. Method: In order to investigate the regulatory mechanisms of TNF signaling pathway in regulating chondrocyte apoptosis, a fractional-order differential equation model is proposed to describe the dynamic behavior and mutual interaction of apoptosis-related genes under the activation of TNF signaling pathway. Compared with the traditional molecular biology techniques, the proposed mathematical modeling has advantages to providing a more comprehensive understanding of the regulatory mechanisms of TNF signaling pathway in chondrocyte apoptosis. Result: In this paper, differentially expressed genes induced by IL-1β in human chondrocyte apoptosis are screened using high-throughput sequencing. It is found that they were significantly enriched in the TNF signaling pathway. Therefore, a mathematical model of the TNF signaling pathway is built. Using real-time PCR experiments, mRNA data is measured and used to identify the model parameters, as well as the correlation coefficient. Finally, the sensitivity of the model parameters is discussed by using numerical simulation methods, which can be used to predict the effects of different interventions and explore the optimal intervention strategies for regulating chondrocyte apoptosis. Discussion: Therefore, fractional-order differential equation modeling plays an important role in understanding the regulatory mechanisms of TNF signaling pathway in chondrocyte apoptosis and its potential clinical applications.

Recent advances in GPR35 pharmacology; 5-HIAA serotonin metabolite becomes a ligand

GPR35, an orphan receptor, has been waiting for its ligand since its cloning in 1998. Many endogenous and exogenous molecules have been suggested to act as agonists of GPR35 including kynurenic acid, zaprinast, lysophosphatidic acid, and CXCL17. However, complex and controversial responses to ligands among species have become a huge hurdle in the development of therapeutics in addition to the orphan state. Recently, a serotonin metabolite, 5-hydroxyindoleacetic acid (5-HIAA), is reported to be a high potency ligand for GPR35 by investigating the increased expression of GPR35 in neutrophils. In addition, a transgenic knock-in mouse line is developed, in which GPR35 was replaced with a human ortholog, making it possible not only to overcome the different selectivity of agonists among species but also to conduct therapeutic experiments on human GPR35 in mouse models. In the present article, I review the recent advances and prospective therapeutic directions in GPR35 research. Especially, I'd like to draw attention of readers to the finding of 5-HIAA as a ligand of GPR35 and lead to apply the 5-HIAA and human GPR35 knock-in mice to their research fields in a variety of pathophysiological conditions.

Modeling the COVID-19 Epidemic With Multi-Population and Control Strategies in the United States

As of January 19, 2021, the cumulative number of people infected with coronavirus disease-2019 (COVID-19) in the United States has reached 24,433,486, and the number is still rising. The outbreak of the COVID-19 epidemic has not only affected the development of the global economy but also seriously threatened the lives and health of human beings around the world. According to the transmission characteristics of COVID-19 in the population, this study established a theoretical differential equation mathematical model, estimated model parameters through epidemiological data, obtained accurate mathematical models, and adopted global sensitivity analysis methods to screen sensitive parameters that significantly affect the development of the epidemic. Based on the established precise mathematical model, we calculate the basic reproductive number of the epidemic, evaluate the transmission capacity of the COVID-19 epidemic, and predict the development trend of the epidemic. By analyzing the sensitivity of parameters and finding sensitive parameters, we can provide effective control strategies for epidemic prevention and control. After appropriate modifications, the model can also be used for mathematical modeling of epidemics in other countries or other infectious diseases.

Forecasting sensitive targets of the kynurenine pathway in pancreatic adenocarcinoma using mathematical modeling

In this study, a new mathematical model was established and validated to forecast and define sensitive targets in the kynurenine pathway (Kynp) in pancreatic adenocarcinoma (PDAC). Using the Panc-1 cell line, genetic profiles of Kynp molecules were tested. qPCR data were implemented in the algorithm programming (fmincon and lsqnonlin function) to estimate 35 parameters of Kynp variables by Matlab 2017b. All tested parameters were defined as non-negative and bounded. Then, based on experimental data, the function of the fmincon equation was employed to estimate the approximate range of each parameter. These calculations were confirmed by qPCR and Western blot. The correlation coefficient (R) between model simulation and experimental data (72 hours, in intervals of 6 hours) of every variable was >0.988. The analysis of reliability and predictive accuracy depending on qPCR and Western blot data showed high predictive accuracy of the model; R was >0.988. Using the model calculations, kynurenine (x3, a6), GPR35 (x4, a8), NF-kβp105 (x7, a16), and NF-kβp65 (x8, a18) were recognized as sensitive targets in the Kynp. These predicted targets were confirmed by testing gene and protein expression responses. Therefore, this study provides new interdisciplinary evidence for Kynp-sensitive targets in the treatment of PDAC.