This study was aimed to develop non-toxic, high transfection efficiency polyethyleneimine(PEI) cationic nanoparticles. The exosyndrome of PEI cationic nanoparticles was measured by zeta sizer, ultraviolet and visible spectroscopy. The condensation ability and the resistance to DNaseI of pEGFP-N1/PEI and pEGFP-N1/PEI modified polyethylene glycol(PEG) were evaluated by agarose gel electrophoresis. The cell toxicity of polyethyleneimine cationic nanoparticles was measured by using MTT test. The orthogonal design was used to optimize the transfection efficiency with the N/P ratio, the grafting ratio and the gene dosage as the factors. The experimental results showed that pEGFP-N1/PEI nanoparticles have lower cell toxicity, better composite ability and better resistance to DNAseI. The highest transfection efficiency of PEI cationic nanoparticles was 91% by using the PEI nanoparticles with the N/P ratio 40:1 and gene dosages 6 microg/well. PEI cationic nanoparticle modified by PEG effectively transferred DNA to hepatoma carcinoma cells and it is a non-toxic, with high transfection efficiency, and a promising non-viral carrier for gene delivery. The transfection efficiency will be improved by optimizing the experiment condition.
Carcinoma, Hepatocellular ; genetics ; pathology ; Cell Line, Tumor ; Gene Transfer Techniques ; Humans ; Liver Neoplasms ; genetics ; pathology ; Nanoparticles ; chemistry ; Polyethylene Glycols ; chemistry ; Polyethyleneimine ; chemistry ; Transfection ; methods

Objective To observe the clinical efficacy of control blood glucose by using CSII for the people who suf-fers?from colorectal cancer complicated with type 2 diabetes mellitus in peri-operation period. Methods Sixty patients who sufferred from colorectal cancer complicated with type 2 diabetes mellitus were selected and were randomly divid-ed into treatment group(30 cases) and control group(30 cases). The treatment group was treated with CSII (CSII group), while the control group was treated with conventional subcutaneous injection (MSII group). The blood glucose level be-fore and after treatment, blood glucose time required to reach the target, the incidence of hypoglycemia, insulin dosage, preoperative preparation time, time of hospitalization,the incidence of wound healing, and infection were detected. Re-sults Blood glucose levels of two groups decreased significantly after treatment, which reached effect satisfaction. The time of blood glucose control, the rate of hypoglycemia, insulin dosage, preoperative preparation time, hospitalization time,wound healing and infection rate of group CSII were lower than those of MSII group. Conclusion Efficacy and safety in peri-operative time of CSII in colorectal cancer operation with type 2 diabetes mellitus is superior to that of MSII.

OBJECTIVETo detect the expression of transient receptor potential canonical 1 (TRPC1) in a mouse model of ozone-induced lung inflammation and explore its role in lung inflammation.

METHODSIn a mouse model of lung inflammation established by ozone exposure, the expression of TRPC1 in the inflammatory lung tissues was detected by RT-PCR, Wstern blotting and immunohistochemistry.

RESULTSCompared to the control mice, the mice exposed to ozone showed significantly increased expression level of TRPC1 mRNA and protein in the inflammatory lung tissues (P<0.05). Immunohistochemistry showed increased TRPC1 protein expressions in the alveolar epithelial cells, bronchial epithelial cells, and inflammatory cells in the inflammatory lung tissues (P<0.05). The mRNA and protein expression levels of TRPC1 were positively correlated with the counts of white blood cells, macrophages, neutrophils and lymphocytes in the bronchoalveolar lavage fluid of the exposed mice (P<0.01).

CONCLUSIONTRPC1 may play a role in ozone-induced lung inflammation in mice.

Animals ; Bronchoalveolar Lavage Fluid ; Disease Models, Animal ; Gene Expression ; Inflammation ; pathology ; Lung ; metabolism ; pathology ; Mice ; Ozone ; adverse effects ; Pneumonia ; metabolism ; pathology ; RNA, Messenger ; TRPC Cation Channels ; metabolism

Objective:To explore the value of a deep learning model based on MRI in predicting the methylation status of MGMT in WHO Ⅱ, Ⅲ gliomas.Methods:The clinical and imaging data of 121 patients with WHO grade Ⅱ, Ⅲ glioma confirmed by surgical pathology and molecular pathology in the Second Hospital of Lanzhou University from June 2016 to June 2020 were retrospectively analyzed. Among them, the MGMT promoter was methylated. A total of 78 cases were metabolized and 43 cases were unmethylated. T 2WI and T 1WI enhanced sequence images of 121 cases of WHO Ⅱ, Ⅲ gliomas were collected, and all the images of each patient including the lesion level were selected manually, and were randomly divided into training set and validation set according to 7∶3. The EfficientNet-B3 convolutional neural network was used to build independent prediction models (T 2-net, T 1C-net, TS-net) based on T 2WI, T 1WI enhancement, T 2WI combined with T 1WI enhancement, and the prediction performance of each model was evaluated separately through the ROC curve. Results:The T 2-net model in the validation set presented an accuracy of 72.3%, a sensitivity of 64.7%, a specificity of 73.3%, and an area under the curve (AUC) of 0.72 for predicting the methylation status of the MGMT promoter in WHO Ⅱ, Ⅲ gliomas. The T 1C-net model showed an accuracy of 66.8%, a sensitivity of 68.3%, a specificity of 66.9%, and an AUC of 0.72. The TS-net model showed an accuracy of 81.8%, a sensitivity of 63.1%, a specificity of 85.0%, and AUC of 0.78. Conclusions:The EfficientNet-B3 convolutional neural network based on MRI can predict the methylation status of the MGMT promoter of WHO Ⅱ, Ⅲ gliomas; the TS-net model has the best prediction performance.

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
Artificial Intelligence ; Diabetes Mellitus ; Diabetic Foot ; Humans