Prognostic implications of changes in cardiac troponin I levels in patients with non-ST elevation acute coronary syndrome

Abstract

Objective: Information is limited on the prognostic implications of cardiac troponin I (cTnI) changes during the first days of non-ST elevation acute coronary syndrome (NSTE-ACS).

Methods: High-sensitivity cTnI levels were measured at study inclusion and after 48?h in 1615 conservatively managed NSTE-ACS patients from the Global Use of Strategies To Open Occluded Coronary Arteries (GUSTO) IV trial.

Results: Patients with moderately increased cTnI levels and without a relevant decrease over time had a significantly raised mortality at 30 days and 1 year. No relevant associations between cTnI changes and recurrent myocardial infarction were seen.

Conclusion: The cTnI change is predictive for subsequent mortality in selected conservatively managed NSTE-ACS patients.     

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

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Dosiomics-based prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma patients

PurposeTo predict the incidence of radiation-induced hypothyroidism (RHT) in nasopharyngeal carcinoma (NPC) patients, dosiomics features based prediction models were established.Materials and methodsA total of 145 NPC patients treated with radiotherapy from January 2012 to January 2015 were included. Dosiomics features of the dose distribution within thyroid gland were extracted. The minimal-redundancy-maximal-relevance (mRMR) criterion was used to rank the extracted features and selected the most relevant features. Machine learning (ML) algorithms including logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were utilized to establish prediction models, respectively. Nested sampling and hyper-tuning methods were adopted to train and validate the prediction models. The dosiomics-based (DO) prediction models were evaluated through comparing with the dose-volume factor-based (DV) models in terms of the area under the receiver operating characteristic (ROC) curve (AUC). The demographics factors (age and gender) were included in both DO model and DV model.ResultsAge, V45 and 37 dosiomics features exhibited significant correlations with RHT in univariate analysis. For prediction performance, DO prediction models exhibited better results with the best AUC value 0.7 while DV prediction models 0.61. In DO prediction models, the AUC values displayed a trend from ascending to descending with the increasing of selected features. The highest AUC value was achieved when the number of selected features was 3. In DV prediction model, similar trend was not observed.ConclusionThis study established a prediction model based on the dosiomics features with better performance than conventional dose-volume factors, leading to early predict the possible RHT among NPC patients who had received radiotherapy and take precaution measures for NPC patients.     

Nucleosomal Positioning and Genetic Divergence Study Based on DNA Flexibility Map

Abstract

Based on worm like chain model, DNA structural parameters—tilt, roll and rise, derived from crystallographic database have been used to determine the flexibility of DNA that regulates the nucleosomal translational positioning. Theoretically derived data has been compared to the experimental values available in Ioshikhes and Trifonov's database. The methodology has been extended to determine the flexibility of 18S rRNA genome in eukarya, where yeast shows a distinct difference when compared with mammals like human, mouse and rabbit.