Semen quality and waiting time to pregnancy explored using association mining.

BACKGROUND: Assessment of semen quality is a key pillar in the evaluation of men from infertile couples. Usually, semen parameters are interpreted individually because the interactions between parameters are difficult to account for. OBJECTIVES: To determine how combinations of classical semen parameters and female partner age were associated with waiting time to pregnancy (TTP). MATERIALS AND METHODS: Semen results of 500 fertile men, information of TTP, and partner age were used for regressions and to detect breaking points. For a modified Association Rule Mining algorithm, semen parameters were categorized as High, Medium, and Low. RESULTS: Men ≤32.1 years and women ≤32.9 years had shorter TTP than older. Decreasing TTP was associated with increasing level of individual semen parameters up to threshold values: sperm concentration 46 mill/mL, total sperm count 179 mill, progressive motility 63%, and normal morphology 11.5%. Using association mining, approximately 100 combinations of semen parameters and partner age were associated with TTP. TTP ≤ 1 month often co-occurred with high percentages of progressive motility (≥62%) and morphologically normal spermatozoa (≥10.5%). Furthermore, TTP ≤ 1 did not tend to appear with lower percentages of these two semen parameters or high partner age (≥32 years). However, high percentages of motile or normal spermatozoa could not compensate for sperm concentration ≤42 mill/mL or total sperm count ≤158 mill. The prolonging effect of high partner age could not be compensated for by the man's semen quality. DISCUSSION AND CONCLUSION: Using association mining, we observed that TTP was best predicted when combinations of semen parameters were accounted for. Sperm counts, motility, and morphology were all important, and no single semen parameter was inferior. Additionally, female age above 32 years had a negative impact on TTP that could not be compensated for by high semen parameters of the man.

Conflicting associations between dietary patterns and changes of anthropometric traits across subgroups of middle-aged women and men.

BACKGROUND: Individuals respond differently to dietary intake leading to different associations between diet and traits. Most studies have investigated large cohorts without subgrouping them. OBJECTIVE: The purpose was to identify non-uniform associations between diets and anthropometric traits that appeared to be in conflict with one another across subgroups. DESIGN: We used a cohort comprising 43,790 women and men, the Danish Diet, Cancer and Health study, which includes a baseline examination at age 50-64 years and a follow-up about 5 years later. The baseline examination involved anthropometrics, body fat percentage, a food frequency questionnaire and information on lifestyle. From the questionnaire data we computed association rules between the intake of food groups and changes in waist circumference and body weight. Using association rule mining on subgroups and gender-specific cohorts, we identified non-uniform associations. The two gender-specific cohorts were stratified into subgroups using a non-linear, self-organizing map based method. RESULTS: We found 22 and 7 cases of conflicting rules in 8 participant subgroups for different anthropometric traits in women and men, respectively. For example, in a subgroup of women moderate waist loss was associated with a dietary pattern characterized by low intake in both cabbages and wine, in conflict with the association trends of both dietary factors in the female cohort. The finding of more conflicting rules in women suggests that inter-individual differences in response to dietary intake are stronger in women than in men. CONCLUSIONS: This combined stratification and association discovery approach revealed epidemiological relationships between dietary factors and changes in anthropometric traits in subgroups that take food group interactions into account. Conflicting rules adds an additional layer of complexity that should be integrated into the study of these relationships, for example in relation to genotypes.

Naïve human pluripotent stem cells respond to Wnt, Nodal and LIF signalling to produce expandable naïve extra-embryonic endoderm.

Embryonic stem cells (ESCs) exist in at least two states that transcriptionally resemble different stages of embryonic development. Naïve ESCs resemble peri-implantation stages and primed ESCs the pre-gastrulation epiblast. In mouse, primed ESCs give rise to definitive endoderm in response to the pathways downstream of Nodal and Wnt signalling. However, when these pathways are activated in naïve ESCs, they differentiate to a cell type resembling early primitive endoderm (PrE), the blastocyst-stage progenitor of the extra-embryonic endoderm. Here, we apply this context dependency to human ESCs, showing that activation of Nodal and Wnt signalling drives the differentiation of naïve pluripotent cells toward extra-embryonic PrE, or hypoblast, and these can be expanded as an in vitro model for naïve extra-embryonic endoderm (nEnd). Consistent with observations made in mouse, human PrE differentiation is dependent on FGF signalling in vitro, and we show that, by inhibiting FGF receptor signalling, we can simplify naïve pluripotent culture conditions, such that the inhibitor requirements closer resemble those used in mouse. The expandable nEnd cultures reported here represent stable extra-embryonic endoderm, or human hypoblast, cell lines.This article has an associated 'The people behind the papers' interview.

Combing the Hairball: Improving Visualization of miRNA-Target Interaction Networks.

The visualization of regulatory networks is becoming increasingly important in order to understand molecular mechanisms and diseases. MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) responsible of the post-transcriptional regulation of messenger RNAs (mRNAs) and other ncRNAs. MiRNAs are involved in numerous biological processes including development, cell proliferation, and apoptosis. They are also key molecules in major complex diseases such as cancer and cardiovascular diseases. A single miRNA can regulate many targets, making the analysis and visualization of these complex networks challenging. Here, we present standard and advanced visualization approaches to represent networks with a special focus on miRNA-target interactions.