Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0018799 (heart disease)
 34,133 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Limulus cascade reaction is found to be frequently activated by the plasma in the patients undergoing cardiopulmonary bypass (CPB). Recently, it is considered that this phenomenon occurs by factor G pathway reactive activity (GPRA) rather than endotoxin that activates factor C pathway. In the present report, we investigated circulating endotoxin and GPRA in 32 patients undergoing CPB. Plasma was pretreated by the New PCA method, and assay was performed by two limulus tests; Toxicolor (reactive with endotoxin and GPRA) and Endospecy (endotoxin-specific). Thirty-two patients were classified into four groups by clinical diagnosis, Group A; dissecting aneurysm of the aorta (6 patients), Group B; ischemic heart disease (9 patients), Group C; heart valve disease (8 patients), Group D; congenital heart disease and myxoma in left atrium (9 patients). GPRA in Ringer's lactate hardly increased during previous circulation before operation. The highest plasma GPRA concentration in all patients was observed at the end of CPB, 415 +/- 342pg/ml, but plasma endotoxin concentration was within normal range. At the end of CPB, plasma GPRA concentrations were 761 +/- 180pg/ml in Group A, 646 +/- 341pg/ml in Group B, 230 +/- 86pg/ml in Group C, and 113 +/- 65pg/ml in Group D. GPRA increase ratio (GPRA increase value/CPB time) of Groups A and B was significantly higher (p < 0.05) than that of Groups C and D. It was suggested that the origin of plasma GPRA during CPB was not the CPB circuit but the tissue of the patients.
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PMID:[Limulus test (factor G pathway) positive substance during cardiopulmonary bypass]. 787 88

Patent ductus arteriosus (PDA) is the most common congenital heart disease in Mexico. The clinical manifestations of the PCA are from asymptomatic patients to the presence of heart failure. Its management should be individualized based on clinical, hemodynamic data and presence of pulmonary hypertension. Our objective was to provide current medical recommendations based on the best, available scientific evidence for the diagnosis, study and therapeutic decisions of the PCA. Established a standardized sequence to search for Practice Guidelines, based on the clinical questions about PCA diagnosis and treatment. Most of the recommendations were taken from selected guidelines and supplemented with the remaining material. The information is expressed in levels of evidence (E) and grade of recommendation (R) according to the characteristics of the study design and type of publications. Currently produces large amounts of medical information in a relatively short period of time which is necessary to have evidence-based CPG to facilitate and standardize the diagnostic decision-treatment to provide better care for children and adults with PCA.
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PMID:[Clinical guide practice. Patent ductus arteriosus]. 2323 52

Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.
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PMID:Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm. 3285 67