Ten-Year Efficacy and Safety of Once-Daily Tacrolimus in Kidney Transplant: A Prospective Cohort Study.

Tacrolimus is a cornerstone in the immunosuppressive therapy of kidney transplantation. The once-daily formulation of tacrolimus has been shown to improve adherence of patients without affecting short-term efficacy. However, long-term proof of once-daily tacrolimus efficacy and safety is still lacking. From January 2009 to November 2013, 170 clinically stable kidney transplant patients were offered to change from the ongoing twice-daily tacrolimus (TDT) formulation to a once-daily tacrolimus (ODT) regimen. Kidney transplant recipients agreeing to the change to be treated with an ODT regimen (n = 105, estimated glomerular filtration rate [eGFR] 57.1 ± 1.6 mL/min/1.73 m2) and patients continuing on a TDT formulation (n = 65, eGFR 52.0 ± 2.2 mL/min/1.73 m2) were prospectively followed (median follow-up time 10.4 and 12.6 years in the ODT and TDT groups, respectively, P = not significant). At the end of the follow-up, patients in both groups experienced similar eGFR (50.4 ± 2.2 vs 48.0 ± 2.7 mL/min/1.73 m2 in the ODT and TDT groups, respectively, P = not significant). No differences were observed in biopsy-proven acute rejection, overall graft survival, doubling of serum creatinine, and new onset of proteinuria. The 2 groups also had a comparable rate of death, sepsis, and neoplasia. In conclusion, ODT appears safe and effective in stable kidney graft recipients even 10 years after transplantation. These findings support the use of ODT as a primary tacrolimus formulation in patients with kidney transplantation.

Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification.

In this paper, a novel hierarchical multi-class SVM (H-MSVM) with extreme learning machine (ELM) as kernel is proposed to classify electroencephalogram (EEG) signals for epileptic seizure detection. A clinical EEG benchmark dataset having five classes, obtained from Department of Epileptology, Medical Center, University of Bonn, Germany, is considered in this work for validating the clinical utilities. Wavelet transform-based features such as statistical values, largest Lyapunov exponent, and approximate entropy are extracted and considered as input to the classifier. In general, SVM provides better classification accuracy, but takes more time for classification and also there is scope for a new multi-classification scheme. In order to mitigate the problem of SVM, a novel multi-classification scheme based on hierarchical approach, with ELM kernel, is proposed. Experiments have been conducted using holdout and cross-validation methods on the entire dataset. Metrics namely classification accuracy, sensitivity, specificity, and execution time are computed to analyze the performance of the proposed work. The results show that the proposed H-MSVM with ELM kernel is efficient in terms of better classification accuracy at a lesser execution time when compared to ANN, various multi-class SVMs, and other research works which use the same clinical dataset.