Objective To establish and evaluate a hydrocephalus model in dogs. Methods Twelve healthy adult male mongrel dogs (weight, 10-15 kg) were randomly divided into the control group (n=6) and the experimental group (n=6). All the dogs were given CT and neurological examination to exclude congenital ventricular enlargement and neurological abnormity before they received hydrocephalus induction. Surgical procedures included the exposing of the foramen magnum area, the opening of the atlantooccipita anadesma, and the injecting of silicone oil (0.3 ml/kg) into the fourth ventricle through a silicone tube. Normal saline was injected in the control group. The Tarlov neurological fitness assessment and the Evan’s ratio were used to evaluatethe degree of hydrocephalus at 3, 14 and 56 days after operation. Results In the experimental group, the dogs were dull and unsteady in walking,and they drank and ate less. The lateral ventricle began to expand 3 days afteroperation, and then the temple horn of the lateral ventricle and the third ventricle were also affected 14 days after operation. The ventricles were enlarged progressively after operation. The Tarlov scores measured at 3, 14 and 56 days afteroperation had a significant difference at the same time point between the control group(5.83±0.75，6.50±0.55，6.00±0.63) and the experimental group (4.00±0.89，4.83±1.17，4.50±1.05，P＜0.01), but had no significant difference within the same group at different time points (P＞0.05). The Evan’s ratios measured at 3, 14 and 56 days after operation were 0.33±0.04,0.39±006，0.44±0.03，respectively,in the experimental group; and were 0.27±0.06，0.25±0.09, 0.26±0.05,respectively,in the control group. There was a significant difference atthe same time point between the two groups, and at different time points within the experimental group (P＜0.05).Conclusion The dog model of hydrocephalus induced by the injecting of silicone oil into the fourth ventricle has a highsuccess rate, and the model is appropriate for the studies on diagnosis and therapy of hydrocephalus.
ObjectiveTo establish multidrugresistance cell substrain of human hepatocellular carcinoma and to investigate its characteristics.MethodsSMMC7721 cell strain was cultured in Adriamycin(ADM). The multidrugresistance cell substrain SMMC7721/ADM was harvested after a long period of culture by gradually increasing the concentration of ADM and its characteristics were investigated. Results①The drug resistance of SMMC7721/ADM to ADM increased by 33.3 times, to Vincristine 16.8 times, to Diamminedichloroplatinum 2.8 times. ②The drug resistance cell substrain had almost the same growth velocity as its parental generation. The doubling time was 32.0 hours and 30.5 hours respectively. They had the analogous growth curves. ③The obvious difference between the drug resistance cell substrain and its parental generation was that the former’s microvilli became thick, short and scattered by scanning and transmitting electron microscopy. ④The multidrug resistance cell substrain kept the characteristics of hepatocellular carcinoma, it could be transplanted into the subcutaneous tissue of nude mice. ⑤The drug resistance of the cell substrain reduced to 28.0% and 9.2%after removal of the drug for 1 month and 2 months respectively, its drug resistance could remain stable (35.4 times) after 2 months of culture in ADM (0.04 μg/ml).ConclusionThe SMMC7721/ADM cell substrain has the stable fundamental characteristics of a drug resistance cell strain.
ObjectiveTo verify the influence of different variable selection methods on the performance of clinical prediction models. MethodsThree sample sets were extracted from the MIMIC database (acute myocardial infarction group, sepsis group, and cerebral hemorrhage group) using the direct entry of COX regression, step by step forward, step by step backward, LASSO, and ridge regression, based on random forest. These existing six methods of variable importance algorithm, and the optimal variable set of different selected methods were used to construct the model. Through the C index, the area under the ROC curve (AUC value) and the calibration curve, and the results within and between groups were compared. ResultsThe variables and numbers selected by the six variable selection methods were different, however, whether it was within or between groups did not reflect which method had the advantage of significantly improving the performance of the model. ConclusionsPrior to using the variable selection method to establish a clinical prediction model, we should first clarify the research purpose and determine the type of data. Combining medical knowledge to select a method that can meet the data type and simultaneously achieve the research purpose.
