Development and validation of a new dynamic computer-controlled model of the human stomach and small intestine.

For ethical, regulatory, and economic reasons, in vitro human digestion models are increasingly used as an alternative to in vivo assays. This study aims to present the new Engineered Stomach and small INtestine (ESIN) model and its validation for pharmaceutical applications. This dynamic computer-controlled system reproduces, according to in vivo data, the complex physiology of the human stomach and small intestine, including pH, transit times, chyme mixing, digestive secretions, and passive absorption of digestion products. Its innovative design allows a progressive meal intake and the differential gastric emptying of solids and liquids. The pharmaceutical behavior of two model drugs (paracetamol immediate release form and theophylline sustained release tablet) was studied in ESIN during liquid digestion. The results were compared to those found with a classical compendial method (paddle apparatus) and in human volunteers. Paracetamol and theophylline tablets showed similar absorption profiles in ESIN and in healthy subjects. For theophylline, a level A in vitro-in vivo correlation could be established between the results obtained in ESIN and in humans. Interestingly, using a pharmaceutical basket, the swelling and erosion of the theophylline sustained release form was followed during transit throughout ESIN. ESIN emerges as a relevant tool for pharmaceutical studies but once further validated may find many other applications in nutritional, toxicological, and microbiological fields. Biotechnol. Bioeng. 2016;113: 1325-1335. © 2015 Wiley Periodicals, Inc.

Shape-constrained Gaussian process regression for surface reconstruction and multimodal, non-rigid image registration.

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.

Bayesian hierarchical models for disease mapping applied to contagious pathologies.

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to model rare infectious diseases proposing specific and generic variants of this methodology. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. In consequence, we test models including a Negative Binomial distribution (instead of the usual Poisson distribution) to deal with local overdispersion. We also use a specific spatio-temporal link in order to better model the stronger spatial and temporal dependencies due to the transmission of the disease. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).