Search Results (3)

The therapeutic potential of delta9-tetrahydrocannabinol (THC), a primary cannabinoid in the cannabis plant, has led to its development into oral medical products for treating various conditions. However, THC, being a psychoactive substance, can lead to addiction if taken in inappropriate amounts. Thus, studying the pharmacokinetics of THC is crucial for understanding how the drug behaves in the body after administration. This study aims to develop a multi-compartmental model to investigate the pharmacokinetics of medical THC and its metabolites after oral administration. Using the law of mass action, the model was converted into ordinary differential equations (ODEs) to describe the rate of concentration changes of THC and its metabolites in each compartment. The nonstandard finite difference (NSFD) method was then applied to construct numerical solution schemes, which were implemented in MATLAB along with estimated pharmacokinetic rate constants. The results demonstrate that the simulation curves depicting the plasma concentration–time profiles of THC and 11-hydroxy-THC (THC-OH) closely resemble actual data samples, indicating the model’s accuracy. Moreover, the model predicts the pharmacokinetics of THC and its metabolites in various tissues. Consequently, this model serves as a valuable tool for enhancing our understanding of the pharmacokinetics of THC and its metabolites, guiding dosage adjustments, and determining administration durations for oral medical THC. Full article

Two non-standard predictor-corrector type finite difference methods for a SIR epidemic model are proposed. The methods have useful and significant features, such as positivity, basic stability, boundedness and preservation of the conservation laws. The proposed schemes are compared with classical fourth order Runge–Kutta and non-standard difference methods (NSFD). The stability analysis is studied and numerical simulations are provided. Full article

This study analyzes the relationship between the degrees of resemblance and distances between dialects based on several dialectological atlases. This analysis investigates various correspondence data with respect to total valid data in setting reference places and comparison places. The degree of correspondence (DC) can be calculated by quantifying the degree of resemblance. I adopt a great-circular distance for the distance between the source and a comparison place. It is possible to graph the data with distances and DCs along the X and Y axes, respectively. The analysis yields five main results. (1) DC has an inverse relationship with distance in most places, here called the main sequence. However, there are exceptional places called peculiar groups. (2) One of the peculiar groups was caused by in-migration. (3) Another peculiar group is found on islands having very narrow land areas divided by the sea. (4) The main sequence can be classified into two types of linguistic classes. The grammatical data show a stepping slope instead of a gentle slope in the lexical data. (5) The main sequence shows a precise linear relationship over a narrow area. Full article