ABSTRACT
Objective:To evaluate the skin development and repair process of X-ray radiation damage in rat with non-invasive two-photon excitation fluorescence (TPEF) imaging technology in vivo. Methods:Totally 24 SD rats were randomly divided into four groups including X-ray irradiated group (25, 35 and 45 Gy) and non-irradiation control group. At different times after irradiation, the degree of skin injury was evaluated, and the pathological changes of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and collagen fiber fluorescence signals in epidermal cells were detected in vivo by TPEF imaging technology. Results:At 10 d post-irradiation, the skin of irradiation groups showed erythema and desquamation. At 15-20 d post-irradiation, the skin of radiation groups developed progressive exudation, edema and ulcers with increasing radiation dose. On day 25, the skin began to repair in the 25 Gy group, however, the skin of other groups still had exudation and ulcers. On day 10, NAD(P)H fluorescence signal in epidermal cells of irradiation groups decreased and the fluorescence signal of collagen fibers in papillary layer and reticular layer of irradiation groups reduced, which were significantly lower than that of normal control group ( t=24.145, 28.303, 26.989, 6.654, 7.510, 7.997, P<0.05). On day 30, fluorescence signal of NAD(P)H and collagen fibers in epidermal cells and dermis began to repair, the cell from stratum granulosum, stratum spinosum, and stratum basale in the 25 Gy group showed fluorescence signal, the other groups did not show. The fluorescence signal of collagen fibers in the 25 Gy group were gradually increased in papillary layer and reticular layer, however, they were significantly lower than normal control group ( t=115.133, 17.431, P<0.05), the skin of 45 Gy group did not show fluorescence signal of collagen fibers. Conclusions:The damage and repair process of epidermal cells and dermal collagen fiber can be detected noninvasively by TPEF imaging technology after X-ray irradiation in vivo.
ABSTRACT
BACKGROUND:Early detection and accurate staging diagnosis of heart failure are the basis of good clinical therapy efficacy. Due to lack of simple and effective staging model for the diagnosis of heart failure, it is difficult to diagnose heart failure in clinics, leading to poor control of heart failure. OBJECTIVE:To establish the disease staging model based on Adaboost and SVM for heart failure, and improve the accuracy of diagnosis and staging of heart failure. METHODS:A total of 194 cases were roled into this study, including heart failure patients and healthy physical examination persons. According to the stage standards formulated by American Colege of Cardiology and American Heart Association, specific clinical feature parameters closely related to heart failure were colected and selected. Based on clinical diagnosis results and using Adaboost model and SVM model, we trained the models for heart failure diagnosis and staging, thus obtaining diagnosis model. RESULTS AND CONCLUSION: The parameters included stroke volume, cardiac output, left ventricular ejection fraction, left atrial diameter, left ventricular internal diameter at end-systole, N-terminal pro-brain natriuretic peptide and heart rate variability. As for the Adaboost model, its sensitivity and specificity was 100% and 94.4%, respectively. At the same time the SVM model had good sensitivity and specificity, 86.5% and 89.4% respectively. Adaboost classification model can be accurate in the diagnosis of heart failure symptoms, the accuracy reached 89.36%. On the basis of the diagnosis of heart failure, the SVM classification model is effective in staging the severity of heart failure, staging accuracy for staging B and C was 86.49% and 81.48%, respectively. The findings indicate that, combining Adaboost and SVM machine learning models could provide an accurate diagnosis and staging model for heart failure.