120 research outputs found
Diblock copolymers at a homopolymer-homopolymer-interface: a Monte Carlo simulation
The properties of diluted symmetric A-B diblock copolymers at the interface
between A and B homopolymer phases are studied by means of Monte Carlo (MC)
simulations of the bond fluctuation model. We calculate segment density
profiles as well as orientational properties of segments, of A and B blocks,
and of the whole chain. Our data support the picture of oriented ``dumbbells'',
which consist of mildly perturbed A and B Gaussian coils. The results are
compared to a self consistent field theory (SCFT) for single copolymer chains
at a homopolymer interface. We also discuss the number of interaction contacts
between monomers, which provide a measure for the ``active surface'' of
copolymers or homopolymers close to the interface
Tuning the critical gelation temperature of thermo-responsive diblock copolymer worm gels
Amphiphilic diblock copolymer nano-objects can be readily prepared using reversible addition–fragmentation chain transfer (RAFT) polymerization. For example, poly(glycerol monomethacrylate) (PGMA) chain transfer agents (CTA) can be chain-extended using 2-hydroxypropyl methacrylate (HPMA) via RAFT aqueous dispersion polymerization to form well-defined spheres, worms or vesicles at up to 25% solids. The worm morphology is of particular interest, since multiple inter-worm contacts lead to the formation of soft free-standing gels, which undergo reversible degelation on cooling to sub-ambient temperatures. However, the critical gelation temperature (CGT) for such thermo-responsive gels is ≤20 °C, which is relatively low for certain biomedical applications. In this work, a series of new amphiphilic diblock copolymers are prepared in which the core-forming block comprises a statistical mixture of HPMA and di(ethylene glycol) methyl ether methacrylate (DEGMA), which is a more hydrophilic monomer than HPMA. Statistical copolymerizations proceeded to high conversion and low polydispersities were achieved in all cases (Mw/Mn < 1.20). The resulting PGMA-P(HPMA-stat-DEGMA) diblock copolymers undergo polymerization-induced self-assembly at 10% w/w solids to form free-standing worm gels. SAXS studies indicate that reversible (de)gelation occurs below the CGT as a result of a worm-to-sphere transition, with further cooling to 5 °C affording weakly interacting copolymer chains with a mean aggregation number of approximately four. This corresponds to almost molecular dissolution of the copolymer spheres. The CGT can be readily tuned by varying the mean degree of polymerization and the DEGMA content of the core-forming statistical block. For example, a CGT of 31 °C was obtained for PGMA59-P(HPMA91-stat-DEGMA39). This is sufficiently close to physiological temperature (37 °C) to suggest that these new copolymer gels may offer biomedical applications as readily-sterilizable scaffolds for mammalian cells, since facile cell harvesting can be achieved after a single thermal cycle
Reproductive health for refugees by refugees in Guinea III: maternal health
BACKGROUND: Maternal mortality can be particularly high in conflict and chronic emergency settings, partly due to inaccessible maternal care. This paper examines associations of refugee-led health education, formal education, age, and parity on maternal knowledge, attitudes, and practices among reproductive-age women in refugee camps in Guinea. METHODS: Data comes from a 1999 cross-sectional survey of 444 female refugees in 23 camps. Associations of reported maternal health outcomes with exposure to health education (exposed versus unexposed), formal education (none versus some), age (adolescent versus adult), or parity (nulliparous, parous, grand multiparous), were analysed using logistic regression. RESULTS: No significant differences were found in maternal knowledge or attitudes. Virtually all respondents said pregnant women should attend antenatal care and knew the importance of tetanus vaccination. Most recognised abdominal pain (75%) and headaches (24%) as maternal danger signs and recommended facility attendance for danger signs. Most had last delivered at a facility (67%), mainly for safety reasons (99%). Higher odds of facility delivery were found for those exposed to RHG health education (adjusted odds ratio 2.03, 95%CI 1.23-3.01), formally educated (adjusted OR 1.93, 95%CI 1.05-3.92), or grand multipara (adjusted OR 2.13, 95%CI 1.21-3.75). Main reasons for delivering at home were distance to a facility (94%) and privacy (55%). CONCLUSIONS: Refugee-led maternal health education appeared to increase facility delivery for these refugee women. Improved knowledge of danger signs and the importance of skilled birth attendance, while vital, may be less important in chronic emergency settings than improving facility access where quality of care is acceptable
Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.
Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands
Square Packing and Structural Arrangement of ABC Triblock Copolymer Spheres in Thin Films
Prevalence and architecture of de novo mutations in developmental disorders.
The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year
Efficient Synthesis of Narrowly Dispersed Brush Copolymers and Study of Their Assemblies: The Importance of Side Chain Arrangement
Large-scale discovery of novel genetic causes of developmental disorders
Despite three decades of successful, predominantly phenotype-driven discovery of the genetic causes of monogenic disorders1, up to half of children with severe developmental disorders of probable genetic origin remain without a genetic diagnosis. Particularly challenging are those disorders rare enough to have eluded recognition as a discrete clinical entity, those with highly variable clinical manifestations, and those that are difficult to distinguish from other, very similar, disorders. Here we demonstrate the power of using an unbiased genotype-driven approach2 to identify subsets of patients with similar disorders. By studying 1,133 children with severe, undiagnosed developmental disorders, and their parents, using a combination of exome sequencing3,4,5,6,7,8,9,10,11 and array-based detection of chromosomal rearrangements, we discovered 12 novel genes associated with developmental disorders. These newly implicated genes increase by 10% (from 28% to 31%) the proportion of children that could be diagnosed. Clustering of missense mutations in six of these newly implicated genes suggests that normal development is being perturbed by an activating or dominant-negative mechanism. Our findings demonstrate the value of adopting a comprehensive strategy, both genome-wide and nationwide, to elucidate the underlying causes of rare genetic disorders
Prevalence, phenotype and architecture of developmental disorders caused by de novo mutation: The Deciphering Developmental Disorders Study
Individuals with severe, undiagnosed developmental disorders (DDs) are enriched for damaging de novo mutations (DNMs) in developmentally important genes. We exome sequenced 4,293 families with individuals with DDs, and meta-analysed these data with published data on 3,287 individuals with similar disorders. We show that the most significant factors influencing the diagnostic yield of de novo mutations are the sex of the affected individual, the relatedness of their parents and the age of both father and mother. We identified 94 genes enriched for damaging de novo mutation at genome-wide significance (P < 7 × 10−7), including 14 genes for which compelling data for causation was previously lacking. We have characterised the phenotypic diversity among these genetic disorders. We demonstrate that, at current cost differentials, exome sequencing has much greater power than genome sequencing for novel gene discovery in genetically heterogeneous disorders. We estimate that 42% of our cohort carry pathogenic DNMs (single nucleotide variants and indels) in coding sequences, with approximately half operating by a loss-of-function mechanism, and the remainder resulting in altered-function (e.g. activating, dominant negative). We established that most haplo insufficient developmental disorders have already been identified, but that many altered-function disorders remain to be discovered. Extrapolating from the DDD cohort to the general population, we estimate that developmental disorders caused by DNMs have an average birth prevalence of 1 in 213 to 1 in 448 (0.22-0.47% of live births), depending on parental age
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The contribution of X-linked coding variation to severe developmental disorders
Abstract: Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders
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