Objective To establish a model for studying on mechanical responses of osteoblasts seeded in 3 dimensional(3D) scaffold. Methods Fifty pieces of bioderived cancellous bones, whose holes were 500 to 800 μm and density was 0.36 to 0.45g/cm3, were obtained as the scaffolds. They were cultured with the third passage suspension of Wistar rat. Twenty-four of the 50 scaffolds were constructed under apparent strain sine waveform with amplitude of 1 000 με, frequency of 3 Hz, and duration of 3 min/d, as experimental group. The other scaffolds were control group. After 3day coculture, osteoblasts were observed with scanning electron microscope. The proliferation of the osteoblasts was checked by MTT on scheduled date. Results Scanning electron microscopic observation showed that osteoblasts ttached and spread on the trabeculae, which presented the validity of the model under proper mechanical condition. Experiment showed that mechanical environment promoted theproliferation of osteoblasts. The observation of proliferation of osteoblasts showed that the quantity of osteoblasts in the experimental group was higher than that in the control group 1,4,8,12,16,20,24, and 28 days after culturing. Therewas significant difference between the two groups 12,16,20,24,and 28 days afterculturing(P＜0.05). Conclusion The establishment of the model can facilitate the study of mechanical responses of osteoblasts under different conditions.
PURPOSE: To explore the pathogenesis of anisometropic and amblyopias. METHODS:To carry out on monocular and binocular atropinized cat models during the developmental period for anisometropia and ametropia ,and measure the cytosomal sectional area and some parameters of the dendric field from the dorsal lateral geniculate nuclei (dLGN)of adult cats by using Golgi-Cox staining. RESULIS:The changes of cytosomal sectional areas and parameters about dendric fields in the dLGN of experimental cats were as following:significant differences between cells of dLGN's A1 lamina by the monocular atropinized eyes and normal ones, binocular atropinized eyea and normal ones;no significant difference between tbat driven by the monoular and binocular atropinized eyes. CONCLUSIONS:There might be resemble pathogenesis between anisomelropic and ametropic amblyopias. （Chin J Ocul Fundus Dis,1996,12:153-156）
Temporal lobe epilepsy is the most common type of epilepsy in clinic. In recent years, many studies have found that patients with temporal lobe epilepsy have different degrees of influence in executive function related fields. This influence may not only exist in a certain field of executive function, but may be affected in several fields, and may be related to the origin site of seizures. However, up to now, there is no unified standard for the composition of executive function, and it is widely accepted that the three core components of executive function are working memory, inhibitory control and cognitive flexibility/switching. In addition, the International League Against Epilepsy proposed a new definition in 2010, and epilepsy is a brain network disease. There is a close relationship between brain neural network and cognitive impairment. According to the cognitive field, the brain neural network can be divided into six types: default mode network, salience network, executive control network, dorsal attention network, somatic motor network and visual network. In recent years, there has been increasing evidence that four related internal brain networks are series in a range of cognitive processes. The executive dysfunction of temporal lobe epilepsy may be related to the changes of functional connectivity of neural network, and may be related to the left uncinate fasciculus. This article reviews the research progress related to executive function in temporal lobe epilepsy from working memory, inhibitory control and cognitive flexibility, and discusses the correlation between the changes of temporal lobe epilepsy neural network and executive function research.
Objective To systematically review risk prediction models of in-hospital cardiac arrest in patients with cardiovascular disease, and to provide references for related clinical practice and scientific research for medical professionals in China. Methods Databases including CBM, CNKI, WanFang Data, PubMed, ScienceDirect, Web of Science, The Cochrane Library, Wiley Online Journals and Scopus were searched to collect studies on risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease from January 2010 to July 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Results A total of 5 studies (4 of which were retrospective studies) were included. Study populations encompassed mainly patients with acute coronary syndrome. Two models were modeled using decision trees. The area under the receiver operating characteristic curve or C statistic of the five models ranged from 0.720 to 0.896, and only one model was verified externally and for time. The most common risk factors and immediate onset factors of in-hospital cardiac arrest in patients with cardiovascular disease included in the prediction model were age, diabetes, Killip class, and cardiac troponin. There were many problems in analysis fields, such as insufficient sample size (n=4), improper handling of variables (n=4), no methodology for dealing with missing data (n=3), and incomplete evaluation of model performance (n=5). Conclusion The prediction efficiency of risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease was good; however, the model quality could be improved. Additionally, the methodology needs to be improved in terms of data sources, selection and measurement of predictors, handling of missing data, and model evaluations. External validation of existing models is required to better guide clinical practice.
A acute partial obstructive hepatocholangitis model by selective ligation and injection of E coli into left hepatic bile duct was successfully founded in rat. Using parameters including mortality, mitochondrial glutamic oxalacetic transaminase and ornithine carbamoytransferase activity, pathological observation and blood culture of bacteria, we evaluated the model. The authors emphasize that this models is superior to the wole-bile-duct-challenged cholangitis model, which is characterized by liver injury.
ObjectiveTo evaluate Micron Ⅳ retinal imaging system in three mouse models of retinal diseases. MethodsMouse models of oxygen induced retinopathy (OIR) model (OIR group), N-methyl-N nitrosourea (MNU) model (MNU group) and N-methyl-D-aspartate (NMDA) model (NMDA group) were induced in 24 healthy male C57BL/6J mice. Fundus photograph, fundus fluorescein angiography (FFA) and optical coherence tomography (OCT) and electroretinogram (ERG) were used to evaluate these mice. All the imaging examinations were performed by Micron Ⅳ retinal imaging system. ResultsOIR mice showed tortuous and dilated retinal vessels in fundus photograph, neovascularization plexus and vascular leakage in FFA, and epiretinal fibrovascular tissue and tortuous expansion vascular vessels in OCT. MNU mice showed wax yellow optic disk without retinal pigmentary changes, slight thinning of retinal blood vessels in FFA, and normal structure and thickness in OCT. The a-wave amplitudes of the maximum mixed response decreased significantly, and were (15.38±4.36) μV and (13.78±5.52) μV at 2 or 3 days of modeling, respectively. NMDA mice showed a pale retina with vasospasm. ERG revealed that there was no obvious change in latency of a- and b-wave, but significantly decreased amplitude of b-wave at 12 hours and 24 hours after modeling with (72.28±7.18) μV and (65.35±9.18) μV, respectively. ConclusionMicron Ⅳ retinal imaging system is a real-time, non-invasive tool to study the retinal structure and function in animal models of retinal diseases.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.
We have performed guided chemoembolization on 84 patients of moderate and advanced carcinoma of liver using adriamycin lipiodol emulsion (A/L) since 1986. Result showed that the rate of improvement of symptoms was 86.1%, in 75% cases the AFP were decreased and in 79.2% the size of tumor were reduced. The mean survival time was 10.3 months which was much higher than that of the control group (5.6 months,Plt;0.001). THe survival rates of 1/2,1,2,3 year were 89.3%,43.4%,13.5% and 3.8% respctively that were significantly higher than those of the control group (51.2%, 11.5%,0) (Plt;0.01). Three patients underwent secondary resection after using A/L chemoembolization ans gelatin spinge central embolization with a longer survival rate. This may be a good method of treatment to the nonresectable liver cancers and may also be an easy way for postoperative observation.
ObjectiveTo establish an appropriate diabetic retinopathy (DR) risk assessment model for patients with type 2 diabetes mellitus (T2DM).MethodsA retrospective clinical analysis. From January 2016 to December 2017, 753 T2DM patients in the Third Affiliated Hospital of Southern Medical University were analyzed retrospectively. Digital fundus photography was taken in all patients. Fasting plasma glucose (FPG), HbA1c, total bilirubin (TB), blood platelet, total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), low density lipoprotein cholesterol (LDL-c), apolipoprotein-A (apoA), apolipoprotein-B (apoB), serum creatinine, blood urea nitrogen (BUN), blood uric acid, fibrinogen (Fg), estimated glomerular filtration (eGFR) were collected. The patients were randomly assigned to model group and testify group, each had 702 patients and 51 patients respectively. Logistic regression was used to screen risk factors of DR and develop an assessment scale that can be used to predict DR. Goodness of fit was examined using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve.ResultsAmong 702 patients in the model group, 483 patients were DR, 219 patients were NDR. The scores for DR risk were duration of diabetes ≥4.5 years, 4 points; total bilirubin ＜6.65 mol/L, 2 points; apoA≥1.18 g/L, 2 points; blood urea≥6.46 mmol/L, 1 points; HbA1c ≥7.75%, 2 points; HDL-c＜1.38 mmol/L, 2 points; diabetic nephropathy, 3 points; fibrinogen, 1 point. The area under the receiver operating characteristic curve was 0.787. The logistic regression analysis showed that the risk factors independently associated with DR were duration of diabetes (β=1.272, OR=3.569, 95%CI 2.283−5.578, P＜0.001), TB (β=0.744, OR=2.104, 95%CI 1.404−3.152, P＜0.001, BUN (β=0.401, OR=1.494, 95%CI 0.996−2.240, P=0.052), HbA1c (β=0.545, OR=1.724, 95%CI 1.165−2.55, P=0.006), HDL-c (β=0.666, OR=1.986, 95%CI 1.149−3.298, P=0.013), diabetic nephropathy (β=1.151, OR=3.162, 95%CI 2.080−4.806, P=0.013), Fg (β=0.333, OR=1.396, 95%CI 0.945−2.061, P=0.094). The risk model was P=1/[1+exp−(−3.799+1.272X1+0.744X2+0.769X3+0.401X4+0.545X5+0.666X6+1.151X7+0.333X8)]. X1= duration of diabetes, X2=TB, X3=apoA, X4=BUN, X5=HbA1c, X6=HDL-c, X7=diabetic nephropathy, X8=Fg. The area under the ROC curve was 0.787 and the Hosmer-Lemeshow test suggested excellent agreement (χ2=10.125, df=8, P=0.256) in model group. The area under the ROC curve was 0.869 and the Hosmer-Lemeshow test suggested excellent agreement (χ2=5.345, df=7, P=0.618) in model group.ConclusionThe area under the ROC curve for DR was 0.787. The duration of diabetes, TB, BUN, HbA1c, HDL-c, diabetic nephropathy, apoA, Fg are the risk factors of DR in T2DM patients.
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.