Urogynaecological and obstetric issues in women with the exstrophy-epispadias complex

OBJECTIVE

To review of the sexual and urogynaecological issues faced by a large cohort of women with the exstrophy-epispadias complex (EEC).

PATIENTS AND METHODS

The study comprised 83 women and girls with EEC; a confidential survey was mailed to identify their social and sexual concerns. Fifty-six women had classical bladder exstrophy (CBE), 13 had female epispadias (FE) and 14 had cloacal exstrophy (CE). Data on the initial method of reconstruction and urogynaecological problems were obtained from a review of the hospital records. Information on continence, infection and sexual function was obtained from 34 completed surveys.

RESULTS

The bladder was closed in 51 patients with CBE and 13 with CE. Urinary calculi developed in 10 patients with CBE, two with FE and three with CE. Vaginal and uterine prolapse occurred an earlier age in patients with EEC. Eight women had 13 pregnancies, eight of which resulted in normal healthy children. Overall continence was achieved in 85% of the women surveyed. Urinary tract infections remained a frequent problem for women with EEC; only 27% of respondents indicated that they were infection-free. Women aged > 18 years (24) who responded indicated that they had appropriate sexual desire; 16 were sexually active and the mean age for commencing sexual activity was 19.9 years. Six patients had dyspareunia and 10 indicated that they had orgasms. However, five additional patients indicated that they had restricted intercourse, as they were dissatisfied with the cosmesis of their external genitalia.

CONCLUSIONS

Sexual and gynaecological issues become increasingly important in patients with EEC as they become adults. Understanding these issues faced by patients with EEC as they mature will permit better counselling of future patients.

Constructing a gene semantic similarity network for the inference of disease genes

Motivation

The inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes.

Results

We propose to rely on the biological process domain of the gene ontology to construct a gene semantic similarity network and then use the network to infer disease genes. We show that the constructed network covers about 50% more genes than a typical PPI network. By analyzing the gene semantic similarity network with the PPI network, we show that gene pairs tend to have higher semantic similarity scores if the corresponding proteins are closer to each other in the PPI network. By analyzing the gene semantic similarity network with a phenotype similarity network, we show that semantic similarity scores of genes associated with similar diseases are significantly different from those of genes selected at random, and that genes with higher semantic similarity scores tend to be associated with diseases with higher phenotype similarity scores. We further use the gene semantic similarity network with a random walk with restart model to infer disease genes. Through a series of large-scale leave-one-out cross-validation experiments, we show that the gene semantic similarity network can achieve not only higher coverage but also higher accuracy than the PPI network in the inference of disease genes.

Contact

ruijiang@tsinghua.edu.cn

Chriz, a chromodomain protein specific for the interbands of Drosophila melanogaster polytene chromosomes

Polytene interphase chromosomes are compacted into a series of bands and interbands reflecting their organization into independent chromosomal domains. In order to understand chromosomal organization, we set out to study the role of proteins that are selective for interbands. Here we describe the Drosophila melanogaster chromodomain protein Chriz that is coimmunoprecipitated with the zinc finger protein Z4. Both proteins colocalize exclusively to the interbands on Drosophila polytene chromosomes. Like Z4, Chriz is ubiquitously expressed throughout development and is associated with chromatin in all interphase nuclei. Following dissociation from chromatin, early in mitosis Chriz binds to the centrosomes and to the mitotic spindle. Newly induced amorphic Chriz alleles are early lethal, and ubiquitous overexpression of Chriz is lethal as well. Available Chriz hypomorphs which survive until pupal stage have a normal chromosomal phenotype. Reducing Z4 protein does not affect Chriz binding to polytene chromosomes and vice versa. Z4 is still chromosomally bound when Chriz protein is depleted by RNA interference.

The effect of intrathecal bupivacaine/morphine on quality of recovery in robot-assisted radical prostatectomy: a randomised controlled trial

Robot‐assisted radical prostatectomy causes discomfort in the immediate postoperative period. This randomised controlled trial investigated if intrathecal bupivacaine/morphine, in addition to general anaesthesia, could be beneficial for the postoperative quality of recovery. One hundred and fifty‐five patients were randomly allocated to an intervention group that received intrathecal 12.5 mg bupivacaine/300 μg morphine (20% dose reduction in patients > 75 years) or a control group receiving a subcutaneous sham injection and an intravenous loading dose of 0.1 mg.kg⁻¹ morphine. Both groups received standardised general anaesthesia and the same postoperative analgesic regimen. The primary outcome was a decrease in the Quality of Recovery‐15 (QoR‐15) questionnaire score on postoperative day 1. The intervention group (n = 76) had less reduction in QoR‐15 on postoperative day 1; median (IQR [range]) 10% (1–8 [−60% to 50%]) vs. 13% (5–24 [−6% to 50%]), p = 0.019, and used less morphine during the admission; 2 mg (1–7 [0–41 mg]) vs. 15 mg (12–20 [8–61 mg]), p < 0.001. Furthermore, they perceived lower pain scores during exertion; numeric rating scale (NRS) 3 (1–6 [0–9]) vs. 5 (3–7 [0–9]), p = 0.001; less bladder spasms (NRS 1 (0–2 [0–10]) vs. 2 (0–5 [0–10]), p = 0.001 and less sedation; NRS 2 (0–3 [0–10]) vs. 3 (2–6 [0–10]), p = 0.005. Moreover, the intervention group used less rescue medication. Pruritus was more severe in the intervention group; NRS 4 (1–7 [0–10]) vs. 0 (0–1 [0–10]), p = 0.000. We conclude that despite a modest increase in the incidence of pruritus, multimodal pain management with intrathecal bupivacaine/morphine remains a viable option for robot‐assisted radical prostatectomy.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.

Walking on a user similarity network towards personalized recommendations

Personalized recommender systems have been receiving more and more attention in addressing the serious problem of information overload accompanying the rapid evolution of the world-wide-web. Although traditional collaborative filtering approaches based on similarities between users have achieved remarkable success, it has been shown that the existence of popular objects may adversely influence the correct scoring of candidate objects, which lead to unreasonable recommendation results. Meanwhile, recent advances have demonstrated that approaches based on diffusion and random walk processes exhibit superior performance over collaborative filtering methods in both the recommendation accuracy and diversity. Building on these results, we adopt three strategies (power-law adjustment, nearest neighbor, and threshold filtration) to adjust a user similarity network from user similarity scores calculated on historical data, and then propose a random walk with restart model on the constructed network to achieve personalized recommendations. We perform cross-validation experiments on two real data sets (MovieLens and Netflix) and compare the performance of our method against the existing state-of-the-art methods. Results show that our method outperforms existing methods in not only recommendation accuracy and diversity, but also retrieval performance.

A sequence-based method to predict the impact of regulatory variants using random forest

Background

Most disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. In spite of the common agreement that such variants may disrupt biological functions of their hosting regulatory elements, it remains a great challenge to characterize the risk of a genetic variant within the implicated genome sequence. Therefore, it is essential to develop an effective computational model that is not only capable of predicting the potential risk of a genetic variant but also valid in interpreting how the function of the genome is affected with the occurrence of the variant.

Results

We developed a method named kmerForest that used a random forest classifier with k-mer counts to predict accessible chromatin regions purely based on DNA sequences. We demonstrated that our method outperforms existing methods in distinguishing known accessible chromatin regions from random genomic sequences. Furthermore, the performance of our method can further be improved with the incorporation of sequence conservation features. Based on this model, we assessed importance of the k-mer features by a series of permutation experiments, and we characterized the risk of a single nucleotide polymorphism (SNP) on the function of the genome using the difference between the importance of the k-mer features affected by the occurrence of the SNP. We conducted a series of experiments and showed that our model can well discriminate between pathogenic and normal SNPs. Particularly, our model correctly prioritized SNPs that are proved to be enriched for the binding sites of FOXA1 in breast cancer cell lines from previous studies.

Conclusions

We presented a novel method to interpret functional genetic variants purely base on DNA sequences. The proposed k-mer based score offers an effective means of measuring the impact of SNPs on the function of the genome, and thus shedding light on the identification of genetic risk factors underlying complex traits and diseases.

Wogonoside exerts growth-suppressive effects against T acute lymphoblastic leukemia through the STAT3 pathway

Wogonoside is the main flavonoid of the traditional Chinese medicinal herb Scutellaria baicalensis Georgi and has been found to induce growth suppression in myelogenous leukemia cells. However, its activity in T acute lymphoblastic leukemia (T-ALL) is still unclear. In this study, T-ALL cell lines MOLT-3 and Jurkat were exposed to different concentrations of wogonoside for 48 h, and cell viability, cell cycle distribution, and apoptosis were measured. The involvement of signal transducers and activators of transcription 3 (STAT3) signaling in the activity of wogonoside was checked. The in vivo effect of wogonoside on T-ALL growth was investigated in a xenograft mouse model. Wogonoside significantly inhibited the viability of MOLT-3 and Jurkat cells, with the IC₅₀ (the half maximal concentration) of 68.5 ± 3.8 and 52.6 ± 4.3 μM, respectively. However, healthy T lymphocytes were unaffected. Wogonoside-treated Jurkat cells exhibited a G1-phase cell cycle arrest and significant apoptosis, which was coupled with inactivation of STAT3 signaling. Overexpression of constitutively active STAT3 reversed wogonoside-mediated growth suppression and apoptosis and restored the expression of cyclin D1, Mcl-1, and Bcl-xL. In vivo studies demonstrated that wogonoside retarded tumor growth and suppressed STAT3 phosphorylation in Jurkat xenografts. In conclusion, wogonoside suppresses the growth of T-ALL through the STAT3 pathway and may have therapeutic benefits in this disease.

Chinese mothers' reactions to their child's chronic illness

Improvements in health status in the People's Republic of China have led to a shift in the burden of illness from acute, communicable disease to long-term illness, particularly in urban areas. The Chinese family's response to chronic illness is influenced by many factors, including the traditional cultural value of family responsibility as well as economic determinants. Through direct interviews with 75 women, we sought to understand the financial, social, emotional, and physical effects of caring for a chronically ill child on a Chinese mother's life and caregiving ability and to identify the sources of support available to her.

Prioritisation of candidate Single Amino Acid Polymorphisms using one-class learning machines

Recent advancements of the next-generation sequencing technology have enabled the direct sequencing of rare genetic variants in both case and control individuals. Although there have been a few statistical methods for uncovering potential associations between multiple rare variants and human inherited diseases, most of these methods require computational approaches to filter out non-functional variants for the purpose of maximising the statistical power. To tackle this problem, we formulate the detection of genetic variants that are associated with a specific type of disease from the perspective of one-class novelty learning. We focus on a typical type of genetic variants called Single Amino Acid Polymorphisms (SAAPs), and we take advantages of a feature selection mechanism and two one-class learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering disease-associated SAAPs.

EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.

Mimvec: a deep learning approach for analyzing the human phenome

Background

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing.

Results

We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones.

Conclusions

Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

The experience of the family of children with chronic illness at home in China

The increasing prevalence of the nuclear family in China challenges the cultural ideal and tradition of the extended family caring for children with chronic illness at home. This study examined and documented the caregiving experiences in a sample of this population of children and their families. A descriptive survey with both quantitative and qualitative questions was developed and used to interview a total of 75 families in three urban areas of China. Factors such as the severity of illness, physical symptoms, the amount of assistance the child needed for daily living, and the emotional and social impact on the child were identified. Descriptions of the family's living circumstances, caregiving situations, and the child's experiences are illustrated through the use of three case studies.

[Comparison on pharmacological functions of pheretima]

Hot water extraction of Pheretima have marked actions in antipyretic, hypotensive, antiasthmatic and diuresis. Among them Guang-Pheretima and Hu-Pheretima are stronger than Tu-Pheretima in antipyretic. The difference of the intensity of Pheretima in the other actions is not obvious.

Improved method for the preparation of malonyl coenzyme A

Malonyl coenzyme A is synthesized by transacylating S-malonyl-N-decanoyl cysteamine with coenzyme A. A simplified procedure for the preparation of S-malonyl-N-decanoyl cysteamine, using cysteamine as starting material, is described.

Identification of disease-related nsSNPs via the integration of protein sequence features and domain-domain interaction data

Recent studies have suggested the common disease-rare variant (CD-RV) hypothesis in the mapping of disease-related genetic variants and have proposed a number of statistical methods to detect associations between rare variants and human inherited diseases. However, most of these methods take the selection of functional variants as a preliminary step in order to maximise the power of statistical tests. To meet this end, we put forward a filtration approach to identify genetic variants that are potentially associated with a query disease of interest from the perspective of one-class novelty learning. We propose to prioritise candidate non-synonymous single nucleotide polymorphisms (nsSNPs) relying on the integrated use of two sequence conservation properties of amino acids calculated from multiple sequence alignment of protein sequences and one functional similarity measure derived from domain-domain interaction data. We show the power of this approach in the detection of disease-related nsSNP via large-scale leave-one-out cross-validation experiments.