ObjectiveTo observe the effects of human IL-10 gene transfection on the mRNA and protein expressions of IL-1β and TNF-α in the penumbra area following focal cerebral ischemia-reperfusion injury in rats and to investigate its neuroprotective mechanism. MethodsRats were divided into four groups: normal control group, ischemic control group, empty plasmid group and human IL-10 gene transfected group. The mRNA and protein expressions of IL-1β and TNF-α in the penumbra area were detected by fluorescence real-time quantitative PCR and ELISA respectively. ResultsIn normal control group, ischemic control group, empty plasmid group and human IL-10 gene transfected group, the levels of protein expression of TNF-α in penumbra area were(0.66±0. 04) ,(1.16±0.26),(1. 155±0. 26)ng/g and(0. 84±0. 05)ng/g, and the levels of protein expression of IL-1βin penumbra area were(0.37±0.05), (1.25±0.39), (1.21±0.57) ng/g and(0.62+0.05)ng/g, respectively. Compared with normal control group, the levels of protein expression of TNF-α and 1L-1β were significantly higher in other three groups(all P＜0. 01), and lower in human IL-10 gene transfected group than in ischemic control group and empty plasmid group(all P＜0. 01). In normal control group, ischemic control group, empty plasmid group and human IL-10 gene transfectedgroup, the levels of mRNA expression of TNF-α in penumbra area were 1.00 ±0.53,9.42±1.83,9.69±1.96 and 3.53±1.09, and the levels of mRNA expression of IL-1β in penumbra area were 1.00 ±0.51,27. 81±4.84,23.96 ± 4.90 and 13.55± 4.45, respectively. Compared with normal control group, the levels of mRNA expression of TNF-α and IL-1β were significantly higher in other three groups(all P＜0. 01), and lower in human IL-10 gene transfected group than in ischemic control group and empty plasmid group(all P＜0. 01). ConclusionsThe human IL-10 gene transfection may play an protective effect on cerebral ischemia through inhibiting mRNA and protein expression of IL-1β and TNF-α in the penumbra area following focal cerebral ischemia-reperfusion in rats.

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
Humans ; Natural Language Processing ; Surveys and Questionnaires