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）
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.
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.
ObjectiveThis study aims to conduct a bibliometric analysis of the dataset obtained from a systematic review of Model-Based Meta-Analysis (MBMA) studies to uncover research trends in MBMA. MethodsWe conducted a systematic search across databases including Embase, MEDLINE, Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and Sinomed. Relevant literature pertaining to MBMA was screened and included. Qualitative and quantitative analyses were performed to assess author contributions, temporal trends, national distribution, and disease prevalence. ResultsA total of 129 articles were included in the analysis, most published in being China (n=48), the United States (n=47), and the United Kingdom (n=7). The time span covered by these articles ranged from 2005 to 2023. A total of 531 researchers contributed to the included studies, with first authors from 15 different countries. The compilation encompassed 402 keywords, with the three most frequently used being “Meta-analysis” (n=28), “Model-based meta-analysis” (n=27), and “Pharmacokinetics” (n=14). The study covered 16 distinct disease categories, with the top three neoplasms (n=16), neurological disorders (n=14), and endocrine and metabolic diseases (n=13). ConclusionBibliometric analysis showed that the number of MBMA studies has increased significantly over the past three years, using a variety of key diseases as carriers. However, this new type of quantitative research has not yet attracted sufficient attention, and the research power is still concentrated in China and the United States; Moreover, a core group of authors has not yet been formed, it is necessary for scholars from various countries to strengthen multidisciplinary cooperation and communication to promote the production and translation of high-quality evidence.
ObjectiveThis study aimed to construct a Nomogram predictive model to assess the prognosis of patients with viral pneumonia complicated by diabetes mellitus.MethodsWe retrospectively collected data from patients with viral pneumonia who visited our hospital from January 2023 to February 2024 and divided them into diabetes and non-diabetes groups based on the presence of diabetes. Clinical data were collected and intergroup differences were analyzed. Subsequently, factors with statistical significance (P<0.05) were selected for univariate and multivariate Logistic regression analysis in the diabetes group to identify risk factors affecting patient survival. Based on the regression analysis results, a linear model was constructed to predict the survival risk of patients. Additionally, calibration curves and decision curve analysis (DCA) were plotted to assess the predictive accuracy and clinical net benefit of the model.ResultsThe study found significant intergroup differences in age (age), cough, dyspnea, respiratory rate at admission, heart rate, body temperature, and laboratory test results (including blood glucose Glu, glycated hemoglobin HbA1c, neutrophil ratio Neu, C-reactive protein Crp, etc.). Multivariate Logistic regression analysis confirmed that age (age), B-type natriuretic peptide (Bnp), neutrophil ratio (Neu), and lactate (Lac) are independent risk factors affecting the survival of patients with viral pneumonia and diabetes.The constructed nomogram prediction model was evaluated. The calibration curve demonstrated a high degree of consistency between the predicted probabilities and actual outcomes, with a non-significant Hosmer-Lemeshow test result (P>0.05). Decision curve analysis further showed that the model yielded no significant clinical net benefit at extreme probability thresholds, whereas it provided substantial clinical net benefit across all other threshold ranges. Collectively, these findings indicate that the model exhibits high predictive accuracy and holds significant value for clinical application. ConclusionsAge, serum B-type natriuretic peptide, neutrophil ratio, and lactate are independent risk factors for the survival of patients with viral pneumonia complicated by diabetes. The Nomogram predictive model constructed based on these factors has clinical value for prognosis assessment.
Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model—calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
In order to further promote the construction of healthy Tibet, West China Hospital of Sichuan University, which has rich experience in providing assistance to Tibetan areas, has proposed a new model of “136” assistance to Tibet. In this new model, the demand of the assisted hospital is the center. Technology, talent and management as three core elements are starting point. Six special topics were taken as the dimensions, including specialist capacity building, technology popularization, establishment of chronic disease prevention and treatment system, appropriate talent training, hospital management training and model effect evaluation. This paper introduces the structural framework of the model and its application in Tibetan hospitals. It also puts forward the problems existing in the work of assisting Tibet and gives relevant suggestions, aiming to provide a reference for the improvement and perfection of medical aid work in Tibet.
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.
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.
Liver transplantation in hamster-to-rat is a good model for the research in xenograft, but it is difficult to be performed. In order to simplify its procedures, 38 cases of liver transplantation in hamster-to-rat were performed with several technique improvments on the basis of orthotopic liver transplantation in rats. In the light of hamster’s anatomy, three cuffs anastomosis method was used. Because of its high stability and high survival rate, the model can be used widely as the research for liver xenograft.