Pasteurization of donated human milk preserves it for storage and makes it safe for feeding, but at the expense of its composition, nutritional values and functions. Here, we aimed to investigate the impact of Holder Pasteurization (HoP) and High Pressure Processing (HPP) methods on miRNA in human milk and to evaluate impact of these changes on miRNA functions. Milk samples obtained from women in 50^th day of lactation (n = 3) were subjected either to HoP, HPP or remained unpasteurized as a control. Subsequently, miRNA was isolated from whole material and exosomal fraction and sequenced with Illumina NextSeq 500. Sequencing data were processed, read counts were mapped to miRNA and analyzed both quantitatively with DESeq2 and functionally with DIANA mirPath v.3. While HPP caused statistically insignificant decrease in number of miRNA reads compared to unprocessed material, HoP led to 82-fold decrease in whole material (p = 0.0288) and 302-fold decrease in exosomes (p = 0.0021) not leaving enough reads for further analysis. Changes in composition of miRNA fraction before and after HPP indicated uneven stability of individual miRNAs under high pressure conditions, with miR-30d-5p identified as relatively stable and miR-29 family as sensitive to HPP. Interestingly, about 2/3 of unprocessed milk miRNA content consists of only 10 distinct miRNAs with miR-148a-3p at the top. Functional analysis of most abundant human milk miRNAs showed their involvement in signaling pathways, cell communication, proliferation and metabolism that are obviously important in rapidly growing infants. Functions of miRNAs which suffered the greatest depletion during HPP were similar to roles of the majority of unprocessed human milk’s miRNA, which indicates that those functions may be weakened although not completely lost. Our findings indicate that HPP is less detrimental to human milk miRNAs than HoP and should be considered in further research on recommended processing procedures for human milk banks.

Background: Multi-gene expression assays are an attractive tool in revealing complex regulatory mechanisms in living organisms. Normalization is an indispensable step of data analysis in all those studies, since it removes unwanted, non-biological variability from data. In targeted qPCR assays it is typically performed with respect to prespecified reference genes, but the lack of robust strategy of their selection is reported in literature, especially in studies concerning circulating microRNAs (miRNA). Unfortunately, this problem impedes translation of scientific discoveries on miRNA biomarkers into widely available laboratory assays. Previous studies concluded that averaged expressions of multi-miRNA combinations are more stable references than single genes. However, due to the number of such combinations the computational load is considerable and may be hindering for objective reference selection in large datasets. Existing implementations of normalization algorithms (geNorm, NormFinder and BestKeeper) have poor performance and may require days to compute stability values for all potential reference as the evaluation is performed sequentially. Results: We designed NormiRazor - an integrative tool which implements those methods in a parallel manner on a graphics processing unit (GPU) using CUDA platform. We tested our approach on publicly available miRNA expression datasets. As a result, the times of executions on 8 datasets containing from 50 to 400 miRNAs (subsets of GSE68314) decreased 18.7 ±0.6 (mean ±SD), 104.7 ±4.2 and 76.5 ±2.2 times for geNorm, BestKeeper and NormFinder with respect to previous Python implementation. To allow for easy access to normalization pipeline for biomedical researchers we implemented NormiRazor as an online platform where a user could normalize their datasets based on the automatically selected references. It is available at norm.btm.umed.pl, together with instruction manual and exemplary datasets. Conclusions: NormiRazor allows for an easy, informed choice of reference genes for qPCR transcriptomic studies. As such it can improve comparability and repeatability of experiments and in longer perspective help translate newly discovered biomarkers into readily available assays.

Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique – artificial neural networks – to analyse this problem. This new methodology, contrary to previous ones, allows detection of both linear and non-linear dependencies without assuming their charactera priori. The aims of this work included: development of models predicting separately growth during 1st year of treatment and final height as well as identification of important predictors and in-depth analysis of their influence on treatment’s effectiveness. The models were derived on the basis of clinical data of 272 patients treated for at least 1 year, 133 of whom have already attained final height. Starting from models containing 17 and 20 potential predictors, respectively for 1st year and final height model, we were able to reduce their number to 9 and 10. Basing on the final models, IGF-I concentration and earlier growth were indicated as belonging to most important predictors of response to GH therapy, while results of GH secretion tests were automatically excluded as insignificant. Moreover, majority of the dependencies were observed to be non-linear, thus using neural networks seems to be reasonable approach despite it being more complex than previously applied methods.

Identifying germlineBRCA1/2mutation carriers is vital for reducing their risk of breast and ovarian cancer. To derive a serum miRNA-based diagnostic test we used samples from 653 healthy women from six international cohorts, including 350 (53.6%) withBRCA1/2mutations and 303 (46.4%)BRCA1/2 wild-type. All individuals were cancer-free before and at least 12 months after sampling. RNA-sequencing followed by differential expression analysis identified 19 miRNAs significantly associated withBRCAmutations, 10 of which were ultimately used for classification: hsa-miR-20b-5p, hsa-miR-19b-3p, hsa-let-7b-5p, hsa-miR-320b, hsa-miR-139-3p, hsa-miR-30d-5p, hsa-miR-17-5p, hsa-miR-182-5p, hsa-miR-421, hsa-miR-375-3p. The final logistic regression model achieved area under the receiver operating characteristic curve 0.89 (95% CI: 0.87–0.93), 93.88% sensitivity and 80.72% specificity in an independent validation cohort. Mutated gene, menopausal status or having preemptive oophorectomy did not affect classification performance. Circulating microRNAs may be used to identifyBRCA1/2mutations in patients of high risk of cancer, offering an opportunity to reduce screening costs.

Introduction Vitamin D3 [25(OH)D] deficiency is a significant problem in Polish children. In many regions of the world, 25(OH)D concentrations show seasonal variation and differences between boys and girls, due to seasonal differences in insolation, as well as different sociological and...

Introduction. The effectiveness of the currently utilized therapies for FoG is limited. Several studies demonstrated a beneficial impact of Nordic walking (NW) on several gait parameters in Parkinson’s disease, but only one paper reported reduction of freezing.Research Question. In the present study, the question is whether NW is an effective therapeutic intervention in FoG.Methods. Twenty PD subjects trained NW for 12 weeks, with a frequency of twice per week. Each session lasted about 60 minutes. Twenty patients in the control group did not use any form of physiotherapy (no-intervention group). Freezing of Gait Questionnaire (FOGQ), the Timed Up and Go (TUG) test, and the Provocative Test for Freezing and Motor Blocks (PTFMB) were performed at baseline, immediately after the end of NW program, and three months later.Results. The results of FOGQ, TUG, and total PTFMB revealed significant improvement after completing the exercise program, and this effect persisted at follow-up. The results of the PTFMB subtests showed a different effect of NW on particular subtypes of FoG. Start hesitation, sudden transient blocks that interrupt gait, and blocks on turning improved considerably, while motor blocks, when walking through narrow space and on reaching the target, did not respond to NW training.Significance. The results show, for the first time, that FoG during turning and step initiation, two most common forms of this gait disorder, has been significantly reduced by NW training. Different responses of particular subtypes of FoG to NW probably reflect their different pathophysiologies.Conclusions. The present study showed that NW training had a beneficial effect on FOG in PD and that the achieved improvement is long-lasting. Future research should clarify whether the observed improvement limited to FoG triggered by only some circumstances reflects different pathomechanisms of FoG subtypes.