No abstract

Mathematical modeling of mass transfer and absorption in the small intestine has been a challenging task. Systematic review and analysis of existing efforts indicate the need to pursue a reliable predictive model that is physically sound and computationally efficient. With the consideration of 3D intestinal inner wall structure, this work rigorously derives an absorption model that can be used as a source term in a 1D distributed model, conventionally called the diffusion–convection–reaction model. Moreover, computational fluid dynamics simulations are carried out to generate in silico experimental data for quantification of the mass‐transfer coefficient in the absorption model. This model facilitates a better understanding of the intricate influence of intestinal morphology and motility on mass transfer and absorption in the intestine. Rat duodenum featuring a villous structure and pendular movement is selected as an example to demonstrate the capability of this approach.

OBJECTIVE:The aim of this study was to investigate the correlation between the Trial of Org 10172 in acute stroke treatment classification and the National Institutes of Health Stroke Scale score of acute cerebral infarction as well as acute cerebral infarction's risk factors. METHODS: The clinical data of 3,996 patients with acute cerebral infarction hospitalized in Hebei Renqiu Kangjixintu Hospital from January 2014 to November 2018 were analyzed retrospectively. According to Trial of Org 10172 in acute stroke treatment, they were divided into five groups: arteriosclerosis, cardio cerebral embolism, arterial occlusion, other causes, and unknown causes. Through questionnaire design, routine physical examination, and physical and chemical analysis of fasting venous blood samples, the risk factors were evaluated, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale classification was analyzed using multivariate logistic regression. In addition, the relationship between National Institutes of Health Stroke Scale score and risk factors in different groups was compared, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale score was analyzed. RESULTS: Multivariate logistic regression analysis showed that diabetes, atrial fibrillation or stroke history, age, and education level were related to Trial of Org 10172 in acute stroke treatment classification. In the National Institutes of Health Stroke Scale comparison, the scores of the cardio cerebral embolism group were significantly higher than those of the other four groups, and patients with diabetes, atrial fibrillation, or stroke history had a high share, especially atrial fibrillation (33.06%). CONCLUSIONS: The nerve function defect is more serious after acute cerebral infarction with cardiogenic cerebral embolism, indicating a poor prognosis.