Microfluidic chip technology integrates the sample preparation, reaction, separation and detection on a chip. It consists a network of microchannels, which controls the whole system through fluid. With the advantages of portability, high throughput, and the ability to simulate the microenvironment in vivo, it has a broad application prospect in the research of disease diagnosis, pathogenesis and drug screening. Pulmonary inflammatory disease is a common disease usually caused by bacterial, viral and fungal infections. Early pneumonia is often difficult to diagnose due to lack of obvious respiratory symptoms or the symptoms are mostly atypical, but the disease progresses rapidly. Recently, microfluidic chip technology has been increasingly used to the study of pulmonary inflammatory diseases. In particular, it has been used to develop a "lung-on-a-chip" model, which can reproduce the key structure, function and mechanical properties of human alveolar capillary interface (i.e., the basic functional unit of a living lung), and well simulate the alveoli in vitro. Compared with the cell and animal models, this multifunctional micro experimental platform has great advantages. This article summarizes the advances of using microfluidic chips for the research and diagnosis of pulmonary inflammatory diseases, with the aim to provide new ideas for researchers in this area.
Animals ; Drug Evaluation, Preclinical ; Humans ; Lung ; Microfluidic Analytical Techniques ; Microfluidics

Objective: To examine the integrated model of self-management behavior in middle-aged stroke patients based on health belief model and planned behavior theory. Methods: A self-designed general information questionnaire, self-management behavior cognition and attitude questionnaire and self-management behavior scale, were used to investigate 654 cases of first middle-aged stroke patients in the neurology department of 3 grade a hospitals in Qingdao. Results: The standardization factor load of each theoretical dimension in the theoretical integration model was 0.51-0.91 (0.72±0.11). Behavioral intention had a direct positive predictive effect on self-management behavior, with an effect value of 0.49 (t = 7.41, P < 0.01). Perceived behavior control not only had a direct positive prediction effect on self-management behavior, but also indirectly influenced self-management behavior through behavioral intention. The effect values are 0.12 (t = 2.75, P < 0.05) and 0.04 (t = 2.18, P < 0.05), respectively. Behavioral attitude exerts influence on self-management behavior through behavioral intention, and the effect value is 0.40 (t = 14.71, P < 0.01). Subjective norms not only influenced self-management behavior through behavioral intention, but also indirectly influenced behavioral intention and self-management behavior through behavioral attitude, with the effect value of 0.25. The effects of perceived benefits and perceived severity on self-management behaviors were 0.21 and 0.04, respectively. The theoretical integration model can explain 62% variance variation of self-management behavior. Conclusion The theoretical integration model of self-management behavior of middle-aged stroke patients based on the health belief model and planned behavior theory can better explain the behavior. This model can be applied to stroke patients in an attempt to provide theoretical basis for the formulation of personalized nursing measures for stroke patients.