Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0728731 (prematurity)
 7,134 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Premature and critically ill infants are highly susceptible to Candida albicans. This study evaluated the lymphocyte-mediated antifungal capacity of infants relative to birth weight, prematurity, and illness severity. Growth inhibition of C. albicans by lymphocytes from preterm and low-birth weight infants was significantly reduced, compared with full-term and normal-weight infants. Lymphocyte growth inhibition of C. albicans is dependent on cell adhesion to the fungus. Compared with full-term infants, lymphocytes from preterm infants had a reduced capacity to adhere to C. albicans. Furthermore, infants with greater severity of illness (score for neonatal acute physiology [SNAP], >or=10) exhibited significantly reduced lymphocyte-mediated antifungal capacity and fungal adhesion. Although gestational age, birth weight, and SNAP were significantly associated with lymphocyte-mediated growth inhibition and adhesion, stepwise regression analysis demonstrated that gestational age best predicted both lymphocyte growth inhibition of and adhesion to the fungus.
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PMID:Reduced lymphocyte-mediated antifungal capacity in high-risk infants. 1208 75

The decision-making process for estimating the optimal dosage is critical in clinical settings. In the neonatal intensive care unit (NICU), preterm neonates suffering from apnea of prematurity, optimum drug dosage can make a difference between life and death. To improve clinical decision making in the NICU, we have developed prediction models using machine learning algorithms. We have used optimized Support Vector Machine (SVM), decision trees with ensembles created using Bagging, Boosting, Random Forest, optimized Multi Layer Perceptron (MLP) and Deep Learning to predict adequacy of caffeine, a methylxanthine used to prevent the development of recurrent apneas, to reduce the need for mechanical ventilation. The respective models developed were evaluated using 100 clinical caffeine cases collected from the Neonatal Intensive Care Unit (NICU) of Kasturba Medical College, Manipal. Our results indicate that a deep belief network (DBN) having an area under curve (AUC) of 0.91, followed by an optimized MLP with the Score for Neonatal Acute Physiology I (SNAP I) as an input feature, outperform other models for assessing the drug effectiveness. Furthermore, the optimized MLP followed by a DBN, with SNAP I as an input feature is a more accurate model for predicting the therapeutic concentration of caffeine. These results suggest that the proposed SNAP I (illness severity score) acts as a critical input variable to enhance the performance of the prediction model. The machine learning approach is very useful for building decision support systems in the NICU in general, and it provides specific solutions to optimize the administration of lifesaving drugs to neonates who are very sensitive to dosages. Using our method, physicians can assess the adequacy and efficacy of caffeine on the study population in a NICU before administering it to neonates.
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PMID:Estimation of Caffeine Regimens: A Machine Learning Approach for Enhanced Clinical Decision Making at a Neonatal Intensive Care Unit (NICU). 3005 27