Effects of auditory-motor synchronization on 400-m sprint performance: An applied study

There is a conspicuous dearth of empirical research regarding the ergogenic and psychological effects of synchronous music when applied in a sports training context. The main purpose of this longitudinal intervention study was to investigate the ergogenic and psychological effects of synchronous music applied over a one-month period of speed endurance training. Twelve participants (six women and six men; 21.1 ± 1.7 years) were randomly assigned to either an experimental group (sprint training coordinated with synchronous music) or a control group (conventional sprint training). Immediately after each training session and each time trial, the Feeling Scale, CR-10 Rating of Perceived Exertion Scale, and Physical Activity Enjoyment Scale were administered to each participant. No significant interaction effect of Group × Time for Rating of Perceived Exertion ( p = .898) or Physical Activity Enjoyment Scale ( p = .411) was identified during the training sessions. A significant Group × Time interaction was identified for Feeling Scale scores ( p = .007). Nonetheless, following Bonferroni adjustment for pairwise comparisons, the between-group differences in Feeling Scale scores did not reach significance. No significant interaction effect of Group × Time or main effect of group was identified for sprint performance, although the latter effect was associated with a large effect size (ηp2 = 0.35). Experimental group participants executed the 400-m time trials 5.07% faster than control group participants. This finding is noteworthy from an applied perspective, given the potential ergogenic effects associated with auditory-motor synchronization. </jats:p

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p

Induction of anti-tumor immunity by trifunctional antibodies in patients with peritoneal carcinomatosis

<p>Abstract</p> <p>Peritoneal carcinomatosis (PC) from epithelial tumors is a fatal diagnosis without efficient treatment. Trifunctional antibodies (trAb) are novel therapeutic approaches leading to a concerted anti-tumor activity resulting in tumor cell destruction. In addition, preclinical data in mouse tumor models demonstrated the induction of long lasting tumor immunity after treatment with trAb. We describe the induction of anti-tumor specific T-lymphocytes after intraperitoneal administration of trAb in patients with PC.</p> <p>9 patients with progressive PC from gastric (n = 6) and ovarian cancer (n = 2), and cancer of unknown primary (n = 1) received 3 escalating doses of trAb after surgery and/or ineffective chemotherapy. The trAb EpCAM × CD3 (10, 20, 40 μg) or HER2/neu × CD3 (10, 40, 80 μg) were applicated by intraperitoneal infusion. Four weeks after the last trAb application, all patients were restimulated by subdermal injection of trAb + autologous PBMC + irradiated autologous tumor cells. Immunological reactivity was tested by analyzing PBMC for specific tumor reactive CD4+/CD8+ T lymphocytes using an IFN-γ secretion assay.</p> <p>In 5 of 9 patients, tumor reactive CD4+/CD8+ T-lymphocytes increased significantly, indicating specific anti-tumor immunity. A clinical response (stable disease, partial regression) has been observed in 5 of 9 patients, with a mean time to progression of 3.6 months. Follow-up showed a mean survival of 11.8 months (median 8.0 months) after trAb therapy.</p> <p>TrAb are able to induce anti-tumor immunity after intraperitoneal application and restimulation. The induction of long-lasting anti-tumor immunity may provide an additional benefit of the intraperitoneal therapy with trAb and should be further elevated in larger clinical trials.</p

Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from genomic features along the genome. The resulting models accurately predict individual enhancer-promoter interactions across diverse cell lines with a false discovery rate up to fifteen times smaller than using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer-promoter pairs. Most of this signature is not proximal to the enhancers and promoters, but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions