The complex microbiome from native semen to embryo culture environment in human in vitro fertilization procedure

Background Only a few microbial studies have conducted in IVF (in vitro fertilization), showing the high-variety bacterial contamination of IVF culture media to cause damage to or even loss of cultured oocytes and embryos. We aimed to determine the prevalence and counts of bacteria in IVF samples, and to associate them with clinical outcome. Methods The studied samples from 50 infertile couples included: raw (n = 48), processed (n = 49) and incubated (n = 50) sperm samples, and IVF culture media (n = 50). The full microbiome was analyzed by 454 pyrosequencing and quantitative analysis by real-time quantitative PCR. Descriptive statistics, t-, Mann-Whitney tests and Spearman's correlation were used for comparison of studied groups. Results The study involved normozoospermic men. Normal vaginal microbiota was present in 72.0% of female partners, while intermediate microbiota and bacterial vaginosis were diagnosed in 12.0 and 16.0%, respectively. The decreasing bacterial loads were found in raw (35.5%), processed (12.0%) and sperm samples used for oocyte insemination (4.0%), and in 8.0% of IVF culture media. The most abundant genera of bacteria in native semen and IVF culture media were Lactobacillus, while in other samples Alphaproteobacteria prevailed. Staphylococcus sp. was found only in semen from patients with inflammation. Phylum Bacteroidetes was in negative correlation with sperm motility and Alphaproteobacteria with high-quality IVF embryos. Conclusion Our study demonstrates that IVF does not occur in a sterile environment. The prevalent bacteria include classes Bacilli in raw semen and IVF culture media, Clostridia in processed and Bacteroidia in sperm samples used for insemination. The presence of Staphylococcus sp. and Alphaproteobacteria associated with clinical outcomes, like sperm and embryo quality.Peer reviewe

Automatic analysis of facilitated taste-liking

This paper focuses on: (i) Automatic recognition of taste-liking from facial videos by comparatively training and evaluating models with engineered features and state-of-the-art deep learning architectures, and (ii) analysing the classification results along the aspects of facilitator type, and the gender, ethnicity, and personality of the participants. To this aim, a new beverage tasting dataset acquired under different conditions (human vs. robot facilitator and priming vs. non-priming facilitation) is utilised. The experimental results show that: (i) The deep spatiotemporal architectures provide better classification results than the engineered feature models; (ii) the classification results for all three classes of liking, neutral and disliking reach F1 scores in the range of 71%-91%; (iii) the personality-aware network that fuses participants’ personality information with that of facial reaction features provides improved classification performance; and (iv) classification results vary across participant gender, but not across facilitator type and participant ethnicity.EPSR

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning

Cellular distribution of vascular endothelial growth factor A (VEGFA) and B (VEGFB) and VEGF receptors 1 and 2 in focal cortical dysplasia type IIB

Members of the vascular endothelial growth factor (VEGF) family are key signaling proteins in the induction and regulation of angiogenesis, both during development and in pathological conditions. However, signaling mediated through VEGF family proteins and their receptors has recently been shown to have direct effects on neurons and glial cells. In the present study, we immunocytochemically investigated the expression and cellular distribution of VEGFA, VEGFB, and their associated receptors (VEGFR-1 and VEGFR-2) in focal cortical dysplasia (FCD) type IIB from patients with medically intractable epilepsy. Histologically normal temporal cortex and perilesional regions displayed neuronal immunoreactivity (IR) for VEGFA, VEGFB, and VEGF receptors (VEGFR-1 and VEGFR-2), mainly in pyramidal neurons. Weak IR was observed in blood vessels and there was no notable glial IR within the grey and white matter. In all FCD specimens, VEGFA, VEGFB, and both VEGF receptors were highly expressed in dysplastic neurons. IR in astroglial and balloon cells was observed for VEGFA and its receptors. VEGFR-1 displayed strong endothelial staining in FCD. Double-labeling also showed expression of VEGFA, VEGFB and VEGFR-1 in cells of the microglia/macrophage lineage. The neuronal expression of both VEGFA and VEGFB, together with their specific receptors in FCD, suggests autocrine/paracrine effects on dysplastic neurons. These autocrine/paracrine effects could play a role in the development of FCD, preventing the death of abnormal neuronal cells. In addition, the expression of VEGFA and its receptors in glial cells within the dysplastic cortex indicates that VEGF-mediated signaling could contribute to astroglial activation and associated inflammatory reactions

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis