Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.
OBJECTIVE:To investigate the value of psychophysical testing for the macular function in the diegnosis of diabetic retinopathy(DR). METHODS:To compare the testing results of macular light sensitivity and pattern visual evoked potential(P-VEP)of 30 eyes of 15 normal person with those of 82 eyes of 41 diabetic patients(27 eyes without DR,55 eyes with simple type DR ). RESULTS:The macular light sensitivity of diabetic patients is much lower than that of normal Control group(plt;0.05). In the diabetic group, 62.19% is abnormal in macular light sensitivity, 69.51% in P-VEP. CONCLUSION: Testing of macular light sensitivit y is helpful in finding of diabetic retinopathy and early deterioration of macular visual function in diabetics. （Chin J Ocul Fundus Dis,1996,12: 223-224）
PURPOSE:In search of the mechanism for photic retinal injury. METHODS:A visible light damage model was established in the primary cultured healthy,adult human RPE cells by using intense fluorescence light (2 400 Lx). RESULTS:Electron microscopy revealed swelling of the mitochondria and obscurity of nuclear membranous structure in the light damaged cells. The decrease or dissolution of organelle,vacuolization of cytoplasm and myelinic degeneration were found in some severely damaged cells. The level of intracellular SOD was decreased to 41% of that of the controls (P＜0.05). CONCLUSION:The structure of the RPE was damaged by the light radiation and the level of intracellular SOD was decreased. These suggested the light damage might be associated with the production of free radicals and the lipid perioxide reaction in membranous structure of cell. （Chin J Ocul Fundus Dis,1996,12: 174-175 ）
Objective To study the retinal light sensitivity of central visual field in normal children. Methods The QZS-Ⅱ automated perimetry was used to assess the visual field of centro-30deg;and centro-6deg; in normal or ametropic eyes in 60 eyes of 5～9 years old children. Results The mean sensitivity(MS)was not influenced by sex,age and laterality and ametropia of the eye.The normal type of dB distribution was obviously higher than the abnormal(P＜0.01).We set normal range as 30deg;MS＞19.3 dB, 6deg;MS＞22.5 dB.The abnormality of value or distribution didnprime;t appear in the same field. Conclusions In normal children,the dB distribution of visual field was mainly of the normal type.We suggest that in evaluating function of visual filed of the children,the dB distribution of centro-30deg;and centro-6deg;field and the value of MS should be included. (Chin J Ocul Fundus Dis, 1999, 15: 137-138)
Objective To evaluate the efficacy of light cured composite resin and glass ionomer cement for wedge shaped defect filling. Methods PubMed, The Cochrane Library (Issue 3, 2016), EMbase, CNKI, CBM, VIP and WanFang Data were searched from inception to April 5th 2016, to collect randomized controlled trials (RCTs) of light cured composite resin versus glass ionomer cement for wedge shaped defect filling. According to the inclusion and exclusion criteria, two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by RevMan 5.2 software. Results A total of 12 studies, involving 3 744 cases of teeth were included. Meta-analysis results showed that there was no significant difference in the two groups on the shedding rate after 2 years (OR=1.19, 95%CI 0.70 to 2.02,P=0.53). But the wear rate of the light cured composite resin group was significantly lower than that of the glass ionomer cement group (Peto OR=0.31, 95%CI 0.22 to 0.44,P<0.000 01). The light cured composite resin group had higher rate of micro leakage or secondary caries rate (OR=1.83, 95%CI 1.11 to 3.01,P=0.02) and incidence of endodontic or periapical lesions (Peto OR=2.84, 95%CI 1.82 to 4.45,P<0.000 01) than the glass ionomer cement group. Conclusion Glass ionomer cement for filling wedge-shaped defects of 2 years after the wear degree is higher than the light cured composite resin, but the occurrence probability of micro leakage or secondary caries and stimulation to pulp are lower than the light cured composite resin. Because of the limitations of the quality of the included studies, the above conclusion still needs to be verified by more high-quality studies.
The poor mechanical property and vulnerability to bacterial infections are the main problems in clinic for dental restoration resins. Based on this problem, the purpose of this study is to synthesize silver-titanium dioxide (Ag-TiO2) nanoparticles with good photocatalytic properties, and add them to the composite resin to improve the mechanical properties and photocatalytic antibacterial capability of the resin. The microstructure and chemical composition of Ag-TiO2 nanoparticles and composite resins were characterized. The results indicated that Ag existed in both metallic and silver oxide state in the Ag-TiO2, and Ag-TiO2 nanoparticles were uniformly dispersed in the resins. The results of mechanical experiments suggested that the mechanical properties of the composite resin were significantly improved due to the incorporation of Ag-TiO2 nanoparticles. The antibacterial results indicated that the Ag-TiO2 nanoparticle-filled composite resins exhibited excellent antibacterial activities under 660 nm light irradiation for 10 min due to the photocatalysis, and the Ag-TiO2 nanoparticle-filled composite resins could also exhibit excellent antibacterial activities after contact with bacteria for 24 h without light irradiation because of the release of Ag ions. In summary, this study provides a new antibacterial idea for the field of dental composite resins.
Wistar rats weaned were raised through 10 weeks under cyclic illumination of 12 hours light and 12 hours darkness,with four different fluorescent colour lighting condition:75 lx and 300lx blue light,300 lux white and 300lux pink light to study the change of superoxide dismutases(SOD)and lipid peroxied(LPO)in the retina.This paper shows that photic oxidative reaction reduces SOD in the retina and oxidizes polyunsaturated fatty acids to become LPO and that complex visible light oxidizes retina easier than simple wave lengths visible light does.The shorter the wave lengths of visible light is and the brighter the illumination is the more serious the oxidative damage of the retina is. （Chin J Ocul Fundus Dis,1993,9:14-16）
To address the issue of a large number of network parameters and substantial floating-point operations in deep learning networks applied to image segmentation for cardiac magnetic resonance imaging (MRI), this paper proposes a lightweight dilated parallel convolution U-Net (DPU-Net) to decrease the quantity of network parameters and the number of floating-point operations. Additionally, a multi-scale adaptation vector knowledge distillation (MAVKD) training strategy is employed to extract latent knowledge from the teacher network, thereby enhancing the segmentation accuracy of DPU-Net. The proposed network adopts a distinctive way of convolutional channel variation to reduce the number of parameters and combines with residual blocks and dilated convolutions to alleviate the gradient explosion problem and spatial information loss that might be caused by the reduction of parameters. The research findings indicate that this network has achieved considerable improvements in reducing the number of parameters and enhancing the efficiency of floating-point operations. When applying this network to the public dataset of the automatic cardiac diagnosis challenge (ACDC), the dice coefficient reaches 91.26%. The research results validate the effectiveness of the proposed lightweight network and knowledge distillation strategy, providing a reliable lightweighting idea for deep learning in the field of medical image segmentation.
Objective To evaluate the efficacy of the light cured flowable composite resin and the light cured pit and fissure sealant in the prevention of dental caries in children. Methods EMbase, CBM, The Cochrane Library, PubMed, Web of Science, CNKI, WanFang Data and VIP databases were searched from inception to January 1st, 2017 for randomized controlled trials (RCTs) or quasi-RCTs about the application of the light cured flowable composite resin and the light cured pit and fissure sealants. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, the RevMan 5.3 software was applied to conduct meta-analysis. Results A total of 13 studies were included. The results of meta-analysis showed that the complete retention rate of the light cured flowable composite resin group was higher than that of the light cured pit and fissure sealant group (6 months: RR=1.03, 95%CI 1.00 to 1.06, P=0.03; 12 months: RR=1.09, 95%CI 1.04 to 1.13, P=0.000 3; 24 months: RR=1.22, 95%CI 1.13 to 1.31, P<0.000 01). The incidence of caries of the light cured flowable composite resin group was lower than that of the light cured pit and fissure sealant group (12 months: Peto OR=0.30, 95%CI 0.16 to 0.56,P=0.000 2; 24 months: Peto OR=0.44, 95%CI 0.31 to 0.63, P<0.000 01). Conclusion The light cured flowable composite resin is superior to the light cured pit and fissure sealant in the complete retention and caries prevention. The above conclusions are needed to be verified by more high-quality clinical studies because of the limitation of the quality and follow-up time of studies.
ObjectiveTo observe the diagnostic value of six classification intelligent auxiliary diagnosis lightweight model for common fundus diseases based on fundus color photography. MethodsA applied research. A dataset of 2 400 color fundus images from Nanjing Medical University Eye Hospital and Zhejiang Mathematical Medical Society Smart Eye Database was collected, which was desensitized and labeled by a fundus specialist. Of these, 400 each were for diabetic retinopathy, glaucoma, retinal vein occlusion, high myopia, age-related macular degeneration, and normal fundus. The parameters obtained from the classical classification models VGGNet16, ResNet50, DenseNet121 and lightweight classification models MobileNet3, ShuffleNet2, GhostNet trained on the ImageNet dataset were migrated to the six-classified common fundus disease intelligent aid diagnostic model using a migration learning approach during training as initialization parameters for training to obtain the latest model. 1 315 color fundus images of clinical patients were used as the test set. Evaluation metrics included sensitivity, specificity, accuracy, F1-Score and agreement of diagnostic tests (Kappa value); comparison of subject working characteristic curves as well as area under the curve values for different models. ResultCompared with the classical classification model, the storage size and number of parameters of the three lightweight classification models were significantly reduced, with ShuffleNetV2 having an average recognition time per sheet 438.08 ms faster than the classical classification model VGGNet16. All 3 lightweight classification models had Accuracy > 80.0%; Kappa values > 70.0% with significant agreement; sensitivity, specificity, and F1-Score for the diagnosis of normal fundus images were ≥ 98.0%; Macro-F1 was 78.2%, 79.4%, and 81.5%, respectively. ConclusionThe intelligent assisted diagnosis of common fundus diseases based on fundus color photography is a lightweight model with high recognition accuracy and speed; the storage size and number of parameters are significantly reduced compared with the classical classification model.