Computational insights into colonic motility: Mechanical role of mucus in homeostasis and inflammation.

Colonic motility plays a vital role in maintaining proper digestive function. The rhythmic contractions and relaxations facilitate various types of motor functions that generate both propulsive and non-propulsive motility modes which in turn generate shear stresses on the epithelial surface. However, the interplay between colonic mucus, shear stress, and epithelium remains poorly characterized. Here, we present a colonic computational model that describes the potential roles of mucus and shear stress in both homeostasis and ulcerative colitis (UC). Our model integrates several key features, including the properties of the mucus bilayer and faeces, intraluminal pressure, and crypt characteristics to predict the time-space mosaic of shear stress. We show that the mucus thickness which could vary based on the severity of UC, may significantly reduce the amount of shear stress applied to the colonic crypts and effect faecal velocity. Our model also reveals an important spatial shear stress variance in homeostatic colonic crypts that suggests shear stress may have a modulatory role in epithelial cell migration, differentiation, apoptosis, and immune surveillance. Together, our study uncovers the rather neglected roles of mucus and shear stress in intestinal cellular processes during homeostasis and inflammation.

Development of a digital twin of a tablet that mimics a real solid dosage form: Differences in the dissolution profile in conventional mini-USP II and a biorelevant colon model.

The performance of colon-targeted solid dosage forms is commonly assessed using standardised pharmacopeial dissolution apparatuses like the USP II or the miniaturised replica, the mini-USP II. However, these fail to replicate the hydrodynamics and shear stresses in the colonic environment, which is crucial for the tablet's drug release process. In this work, computer simulations are used to create a digital twin of a dissolution apparatus and to develop a method to create a digital twin of a tablet that behaves realistically. These models are used to investigate the drug release profiles and shear rates acting on a tablet at different paddle speeds in the mini-USP II and biorelevant colon models to understand how the mini-USP II can be operated to achieve more realistic (i.e., in vivo) hydrodynamic conditions. The behaviour of the tablet and the motility patterns used in the simulations are derived from experimental and in vivo data, respectively, to obtain profound insights into the tablet's disintegration/drug release processes. We recommend an "on-off" operating mode in the mini-USP II to generate shear rate peaks, which would better reflect the in vivo conditions of the human colon instead of constant paddle speed.

Regional economic burden of revision total knee replacement: A cost-complexity analysis.

BACKGROUND: GIRFT tasked regional networks with addressing case-load, complexity-spread and cost of revision knee replacement (KR), but the regional cost burden is not clear. The tariff for revision KR is currently not dependent on surgical complexity. 2 years of revision KR complexity data using the validated Revision Knee Complexity Classification (RKCC) checklist as a demonstration of complexity spread in the region has previously been published. The aims of this study were to estimate the annual regional cost of revision TKR using existing data, and estimate the cost/saving of complexity-clustering using existing data from 8 revision centres. METHODS: Financial data from the regional high-volume centre for one year (2019) of RKCC data collection was obtained. Mean cost, tariff and balance was calculated for R1, R2 and R3 (RKCC), and applied to data from each revision centre to provide local estimates. Complexity clustering was considered using 3 hypothetical scenarios of high-volume centre absorbing R2s and/or R3s in place of R1s. RESULTS: Mean net loss was £2,290.08 for R1s, £6,471.42 for R2s and £6,454.26 for R3s. The estimated total annual loss for the region was £1,005,025. Complexity-clustering was associated with greater losses; £162,918 for high-volume centre taking R2s and R3s, £37,477.60 for taking just R3s and £125,440 for taking just R2s. CONCLUSION: Revision TKR surgery is expensive and insufficiently remunerated with current measures. Restructuring of regional workload would create additional financial burden on specialist centres with current tariff awards structure. Managing reimbursement at a regional or central level may help to incentivise compliance with GIRFT ideals.

Transformation of measurement uncertainties into low-dimensional feature vector space.

Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for postprocessing or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples, Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose the global ocean temperature data. The uses of the method range from supporting decision-making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterization of the El Niño Southern Oscillation.

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input-output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input-output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with 'symptoms' that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.

Simulation of pandemics in real cities: enhanced and accurate digital laboratories.

This study develops a modelling framework for simulating the spread of infectious diseases within real cities. Digital copies of Birmingham (UK) and Bogotá (Colombia) are generated, reproducing their urban environment, infrastructure and population. The digital inhabitants have the same statistical features of the real population. Their motion is a combination of predictable trips (commute to work, school, etc.) and random walks (shopping, leisure, etc.). Millions of individuals, their encounters and the spread of the disease are simulated by means of high-performance computing and massively parallel algorithms for several months and a time resolution of 1 minute. Simulations accurately reproduce the COVID-19 data for Birmingham and Bogotá both before and during the lockdown. The model has only one adjustable parameter calculable in the early stages of the pandemic. Policymakers can use our digital cities as virtual laboratories for testing, predicting and comparing the effects of policies aimed at containing epidemics.

The duality between particle methods and artificial neural networks.

The algorithm behind particle methods is extremely versatile and used in a variety of applications that range from molecular dynamics to astrophysics. For continuum mechanics applications, the concept of 'particle' can be generalized to include discrete portions of solid and liquid matter. This study shows that it is possible to further extend the concept of 'particle' to include artificial neurons used in Artificial Intelligence. This produces a new class of computational methods based on 'particle-neuron duals' that combines the ability of computational particles to model physical systems and the ability of artificial neurons to learn from data. The method is validated with a multiphysics model of the intestine that autonomously learns how to coordinate its contractions to propel the luminal content forward (peristalsis). Training is achieved with Deep Reinforcement Learning. The particle-neuron duality has the advantage of extending particle methods to systems where the underlying physics is only partially known, but we have observations that allow us to empirically describe the missing features in terms of reward function. During the simulation, the model evolves autonomously adapting its response to the available observations, while remaining consistent with the known physics of the system.

Modelling and simulation of the hydrodynamics and mixing profiles in the human proximal colon using Discrete Multiphysics.

The proximal part of the colon offers opportunities to prolong the absorption window following oral administration of a drug. In this work, we used computer simulations to understand how the hydrodynamics in the proximal colon might affect the release from dosage forms designed to target the colon. For this purpose, we developed and compared three different models: a completely-filled colon, a partially-filled colon and a partially-filled colon with a gaseous phase present (gas-liquid model). The highest velocities of the liquid were found in the completely-filled model, which also shows the best mixing profile, defined by the distribution of tracking particles over time. No significant differences with regard to the mixing and velocity profiles were found between the partially-filled model and the gas-liquid model. The fastest transit time of an undissolved tablet was found in the completely-filled model. The velocities of the liquid in the gas-liquid model are slightly higher along the colon than in the partially-filled model. The filling level has an impact on the exsisting shear forces and shear rates, which are decisive factors in the development of new drugs and formulations.

Modelling and simulation of flow and agglomeration in deep veins valves using discrete multi physics.

The hemodynamics in flexible deep veins valves is modelled by means of discrete multi-physics and an agglomeration algorithm is implemented to account for blood accrual in the flow. Computer simulations of a number of valves typologies are carried out. The results show that the rigidity and the length of the valve leaflets play a crucial role on both mechanical stress and stagnation in the flow. Rigid and short membranes may be inefficient in preventing blood reflux, but reduce the volume of stagnant blood potentially lowering the chances of thrombosis. Additionally, we also show that in venous valves, cell agglomeration is driven by stagnation rather than mechanical stress.

Using discrete multi-physics for detailed exploration of hydrodynamics in an in vitro colon system.

We developed a mathematical model that describes the motion of viscous fluids in the partially-filled colon caused by the periodic contractions of flexible walls (peristalsis). In-vitro data are used to validate the model. The model is then used to identify two fundamental mechanisms of mass transport: the surfing mode and the pouring mode. The first mechanism is faster, but only involves the surface of the liquid. The second mechanism causes deeper mixing, and appears to be the main transport mechanism. Based on the gained understanding, we propose a series of measures that can improve the reliability of in-vitro models. The tracer in PET-like experiments, in particular, should not be injected in the first pocket, and its viscosity should be as close as possible to that of the fluid. If these conditions are not met, the dynamics of the tracer and the fluid diverge, compromising the accuracy of the in-vitro data.