Placenta accreta:adherent placenta due to Asherman syndrome

It is important to be aware of the risk of abnormally invasive placenta in patients with a history of Asherman syndrome and uterine scarring. A prenatal diagnosis by ultrasonography is useful when planning of mode of delivery

Patient-Specific Virtual Insertion of Electrode Array for Electrical Simulations of Cochlear Implants

International audienceSensorineural hearing loss is becoming one the most common reasons of disability. Worldwide 278 million people (around 25% of people above 45 years) suffer from moderate to several hearing disorders. Cochlear implantation (CI) enables to convert sound to an electrical signal that directly stimulates the auditory nerves via the electrode array surgically placed. However, this technique is intrinsically patient-dependent and its range of outcomes is very broad. A major source of outcome variability resides in the electrode array insertion. It has been reported to be one of the most important steps in cochlear implant surgery. In this context, we propose a method for patient-specific virtual electrode insertion further used into a finite element electrical simulation, and consequently improving the planning of the surgical implantation. The anatomical parameters involved in the electrode insertion such as the curvature and the number of turns of the cochlea, make virtual insertion highly challenging. Moreover, the influence of the insertion parameters and the use of different manufactured electrode arrays increase the range of scenarios to be considered for the implantation of a given patient. To this end, the method we propose is fast, easily parameterizable and applicable to a wide range of anatomies and insertion configurations. Our method is novel for targeting automatic virtual electrode insertion. Also, it combines high-resolution imaging techniques and clinical data to be further used into a finite element study and predict implantation outcomes in humans

Automatic Generation of a Computational Model for Monopolar Stimulation of Cochlear Implants

International audienceCochlear implants have the potential to significantly improve severe sensorineural hearing loss. However, the outcome of this technique is highly variable and depends on patient-specific factors. We previously proposed a method for patient-specific electrical simulation after CI, which can assist in surgical planning of the CI and determination of the electrical stimulation pattern. However, the virtual implant placement and mesh generation were carried out manually and the process was not easily applied automatically for further cochlear anatomies. Moreover, in order to optimize the implant designs, it is important to develop a way to stimulate the results of the implantation in a population of virtual patients. In this work we propose an automatic framework for patient-specific electrical simulation in CI surgery. To the best of our knowledge, this is the first method proposed for patient-specific generation of hearing models which combines high-resolution imaging techniques, clinical CT data and virtual electrode insertion. Furthermore, we show that it is possible to use the computational models of virtual patients to simulate the results of the electrical activation of the implant in the cochlea and surrounding bone. This is an important step because it allows us to advance towards a complete surgical planning and implant optimization procedure

Potential pitfalls of modelling ribosomal RNA data in phylogenetic tree reconstruction: Evidence from case studies in the Metazoa

<p>Abstract</p> <p>Background</p> <p>Failure to account for covariation patterns in helical regions of ribosomal RNA (rRNA) genes has the potential to misdirect the estimation of the phylogenetic signal of the data. Furthermore, the extremes of length variation among taxa, combined with regional substitution rate variation can mislead the alignment of rRNA sequences and thus distort subsequent tree reconstructions. However, recent developments in phylogenetic methodology now allow a comprehensive integration of secondary structures in alignment and tree reconstruction analyses based on rRNA sequences, which has been shown to correct some of these problems. Here, we explore the potentials of RNA substitution models and the interactions of specific model setups with the inherent pattern of covariation in rRNA stems and substitution rate variation among loop regions.</p> <p>Results</p> <p>We found an explicit impact of RNA substitution models on tree reconstruction analyses. The application of specific RNA models in tree reconstructions is hampered by interaction between the appropriate modelling of covarying sites in stem regions, and excessive homoplasy in some loop regions. RNA models often failed to recover reasonable trees when single-stranded regions are excessively homoplastic, because these regions contribute a greater proportion of the data when covarying sites are essentially downweighted. In this context, the RNA6A model outperformed all other models, including the more parametrized RNA7 and RNA16 models.</p> <p>Conclusions</p> <p>Our results depict a trade-off between increased accuracy in estimation of interdependencies in helical regions with the risk of magnifying positions lacking phylogenetic signal. We can therefore conclude that caution is warranted when applying rRNA covariation models, and suggest that loop regions be independently screened for phylogenetic signal, and eliminated when they are indistinguishable from random noise. In addition to covariation and homoplasy, other factors, like non-stationarity of substitution rates and base compositional heterogeneity, can disrupt the signal of ribosomal RNA data. All these factors dictate sophisticated estimation of evolutionary pattern in rRNA data, just as other molecular data require similarly complicated (but different) corrections.</p

The immunogenicity of a viral cytotoxic T cell epitope is controlled by its MHC-bound conformation

Thousands of potentially antigenic peptides are encoded by an infecting pathogen; however, only a small proportion induce measurable CD8+ T cell responses. To investigate the factors that control peptide immunogenicity, we have examined the cytotoxic T lymphocyte (CTL) response to a previously undefined epitope (77APQPAPENAY86) from the BZLF1 protein of Epstein-Barr virus (EBV). This peptide binds well to two human histocompatibility leukocyte antigen (HLA) allotypes, HLA-B*3501 and HLA-B*3508, which differ by a single amino acid at position 156 (156Leucine vs. 156Arginine, respectively). Surprisingly, only individuals expressing HLA-B*3508 show evidence of a CTL response to the 77APQPAPENAY86 epitope even though EBV-infected cells expressing HLA-B*3501 process and present similar amounts of peptide for CTL recognition, suggesting that factors other than peptide presentation levels are influencing immunogenicity. Functional and structural analysis revealed marked conformational differences in the peptide, when bound to each HLA-B35 allotype, that are dictated by the polymorphic HLA residue 156 and that directly affected T cell receptor recognition. These data indicate that the immunogenicity of an antigenic peptide is influenced not only by how well the peptide binds to major histocompatibility complex (MHC) molecules but also by its bound conformation. It also illustrates a novel mechanism through which MHC polymorphism can further diversify the immune response to infecting pathogens

The Shaping of T Cell Receptor Recognition by Self-Tolerance

SummaryDuring selection of the T cell repertoire, the immune system navigates the subtle distinction between self-restriction and self-tolerance, yet how this is achieved is unclear. Here we describe how self-tolerance toward a trans-HLA (human leukocyte antigen) allotype shapes T cell receptor (TCR) recognition of an Epstein-Barr virus (EBV) determinant (FLRGRAYGL). The recognition of HLA-B8-FLRGRAYGL by two archetypal TCRs was compared. One was a publicly selected TCR, LC13, that is alloreactive with HLA-B44; the other, CF34, lacks HLA-B44 reactivity because it arises when HLA-B44 is coinherited in trans with HLA-B8. Whereas the alloreactive LC13 TCR docked at the C terminus of HLA-B8-FLRGRAYGL, the CF34 TCR docked at the N terminus of HLA-B8-FLRGRAYGL, which coincided with a polymorphic region between HLA-B8 and HLA-B44. The markedly contrasting footprints of the LC13 and CF34 TCRs provided a portrait of how self-tolerance shapes the specificity of TCRs selected into the immune repertoire

Natural micropolymorphism in human leukocyte antigens provides a basis for genetic control of antigen recognition

Human leukocyte antigen (HLA) gene polymorphism plays a critical role in protective immunity, disease susceptibility, autoimmunity, and drug hypersensitivity, yet the basis of how HLA polymorphism influences T cell receptor (TCR) recognition is unclear. We examined how a natural micropolymorphism in HLA-B44, an important and large HLA allelic family, affected antigen recognition. T cell–mediated immunity to an Epstein-Barr virus determinant (EENLLDFVRF) is enhanced when HLA-B*4405 was the presenting allotype compared with HLA-B*4402 or HLA-B*4403, each of which differ by just one amino acid. The micropolymorphism in these HLA-B44 allotypes altered the mode of binding and dynamics of the bound viral epitope. The structure of the TCR–HLA-B*4405EENLLDFVRF complex revealed that peptide flexibility was a critical parameter in enabling preferential engagement with HLA-B*4405 in comparison to HLA-B*4402/03. Accordingly, major histocompatibility complex (MHC) polymorphism can alter the dynamics of the peptide-MHC landscape, resulting in fine-tuning of T cell responses between closely related allotypes

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed