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

Resource limitation drives spatial organization in microbial groups.

Dense microbial groups such as bacterial biofilms commonly contain a diversity of cell types that define their functioning. However, we have a limited understanding of what maintains, or purges, this diversity. Theory suggests that resource levels are key to understanding diversity and the spatial arrangement of genotypes in microbial groups, but we need empirical tests. Here we use theory and experiments to study the effects of nutrient level on spatio-genetic structuring and diversity in bacterial colonies. Well-fed colonies maintain larger well-mixed areas, but they also expand more rapidly compared with poorly-fed ones. Given enough space to expand, therefore, well-fed colonies lose diversity and separate in space over a similar timescale to poorly fed ones. In sum, as long as there is some degree of nutrient limitation, we observe the emergence of structured communities. We conclude that resource-driven structuring is central to understanding both pattern and process in diverse microbial communities

High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia

Colorectal cancer (CRC) remains the third most common cancer worldwide, with a growing incidence among young adults. Multiple studies have presented associations between the gut microbiome and CRC, suggesting a link with cancer risk. Although CRC microbiome studies continue to profile larger patient cohorts with increasingly economical and rapid DNA sequencing platforms, few common associations with CRC have been identified, in part due to limitations in taxonomic resolution and differences in analysis methodologies. Complementing these taxonomic studies is the newly recognized phenomenon that bacterial organization into biofilm structures in the mucus layer of the gut is a consistent feature of right-sided (proximal), but not left-sided (distal) colorectal cancer. In the present study, we performed 16S rRNA gene amplicon sequencing and biofilm quantification in a new cohort of patients from Malaysia, followed by a meta-analysis of eleven additional publicly available data sets on stool and tissue-based CRC microbiota using Resphera Insight, a high-resolution analytical tool for species-level characterization. Results from the Malaysian cohort and the expanded meta-analysis confirm that CRC tissues are enriched for invasive biofilms (particularly on right-sided tumors), a symbiont with capacity for tumorigenesis (Bacteroides fragilis), and oral pathogens including Fusobacterium nucleatum, Parvimonas micra, and Peptostreptococcus stomatis. Considered in aggregate, species from the Human Oral Microbiome Database are highly enriched in CRC. Although no detected microbial feature was universally present, their substantial overlap and combined prevalence supports a role for the gut microbiota in a significant percentage (>80%) of CRC cases