Reproducibility of tDCS Results in a Randomized Trial: Failure to Replicate Findings of tDCS-Induced Enhancement of Verbal Fluency.

BACKGROUND: Transcranial direct current stimulation (tDCS) has been shown to enhance verbal productivity, but the finding and extent of enhancement vary across studies. Few attempts to replicate positive tDCS findings have been reported, suggesting the possibility of publication bias. OBJECTIVE: We aimed to replicate the tDCS methodology and findings of Cattaneo, Pisoni, and Papagno (2011, Neuroscience 183:64-70) in a new population sample. We hypothesized that our study of anodal tDCS would improve verbal fluency production similarly to the original study. METHODS: In our single-blind, sham-controlled crossover experiment, 14 healthy young adults were randomly assigned to receive 2 mA of anodal and sham stimulation to the Broca area in counterbalanced order before completing verbal fluency tasks. RESULTS: Participants tolerated the stimulation well. Despite closely mirroring the original study methods, we saw no main effect of stimulation condition: F1,13=0.002, P=0.97, letter fluency sham mean (standard deviation)=16.8 (2.3), letter fluency anodal=17.5 (3.8), category fluency sham=25.3 (5.4), or category fluency anodal=24.7 (5.2), η≤0.01. CONCLUSIONS: While tDCS may enhance cerebral functions in general, the lack of consistency between studies suggests either that this tDCS protocol does not affect verbal fluency or, at minimum, that tDCS may be more sensitive to experimental conditions than has been thought. Our findings also highlight the need for replication studies in brain stimulation research. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (Identifier NCT01602263).

Selected serum cytokines and vitamin D levels as potential prognostic markers of acute ischemic stroke.

Inflammation-derived oxidative stress is postulated to contribute to neuronal damage leading to poor clinical outcomes in Acute Ischemic Stroke (AIS). We aimed to investigate the association between serum levels of selected cytokines (IL-1ß, IFN-γ, IL-4), and vitamin D in ischemic stroke progression, and their accuracy in predicting AIS prognosis, among Sri Lankans. We compared 60 AIS patients admitted in 4 phases post-stroke onset (<6 h; 6-24 h; 24-48 h; 48-96 h; n = 15/phase), with 15 age- and sex-matched controls. The 30-day functional outcome (FO) was assessed using the modified Rankin Scale (mRS). Serum cytokine and vitamin D levels were quantified using sandwich ELISAs, and competitive ELISA, respectively. The CombiROC web tool established optimal prognostic biomarker combinations. Serum IL-1ß and IFN-γ were elevated in all four phases following stroke onset while IL-4 was elevated exclusively in the recovery phase (48-96 h) (p<0.05). Th1 bias polarization of the Th1:Th2 cytokine (IFN-γ:IL-4) ratio occurred with AIS progression while a Th2 bias occurred during AIS recovery (p<0.05). Lower serum IL-1ß and higher IL-4 levels were associated with a good FO (p<0.05), while lower Vitamin D levels were related to a poor FO (p = 0.001). The triple-biomarker panel, IL-4- IFN-γ -Vit D, accurately predicted AIS prognosis (sensitivity = 100%, specificity = 91.9%, area under the curve = 0.98). Serum immunologic mediators IFN-γ, IL-4, and vitamin D may be useful biomarkers of AIS prognosis and may serve as therapeutic targets in improving stroke outcomes. Vitamin D supplementation may improve the prognosis of AIS patients. Furthermore, binary logistic model fitted for FO indicated Th1:Th2 cytokine ratio (IFN-γ:IL-4), vitamin D status, history of stroke, and ischemic heart disease as significant predictors of AIS prognosis.

NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.

BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies. RESULTS: We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region. CONCLUSIONS: The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.