Changes in the Urinary Microbiome After Transurethral Resection of Non-muscle-Invasive Bladder Cancer: Insights from a Prospective Observational Study.

BACKGROUND: This study aimed to characterize the urinary and tumor microbiomes in patients with non-muscle-invasive bladder cancer (NMIBC) before and after transurethral resection of the bladder tumor (TURBT). METHODS: This single-center prospective study included 26 samples from 11 patients with low-grade Ta papillary NMIBC. Urine samples were collected at the index TURBT and at a 1-year follow-up cystoscopy. The metagenomic analysis of bacterial and archaeal populations was performed based on the highly variable V3-V4 region of the 16S rRNA gene. RESULTS: Phylogenetic alpha diversity of the bladder microbiome detected in urine was found to be lower at the 1-year follow-up cystoscopy compared to the time of the index TURBT (p < 0.01). Actinomyces, Candidatus cloacimonas, Sphingobacterium, Sellimonas, Fusobacterium, and Roseobacter were more differentially enriched taxa in urine at the follow-up cystoscopy than at the index TURBT. Beta diversity of urine microbiome significantly changed over time (p < 0.05). Phylogenetic alpha diversity of the microbiome was greater in tumor tissues than in paired urine samples (p<0.01). Sphingomonas, Acinetobacter, Candidatus, and Kocuria were more differentially overrepresented in tumor tissues than in urine. The enrichment of the abundance of Corynebacterium and Anaerococcus species in urine collected at TURBT was observed in patients who experienced recurrence within the follow-up period. CONCLUSIONS: In patients with low-grade NMIBC, the urine microbiome undergoes changes over time after removal of the tumor. The microbiome detected in tumor tissues is more phylogenetically diverse than in paired urine samples collected at TURBT. The interplay between bladder microbiome, tumor microbiome, and their alterations requires further studies to elucidate their predictive value and perhaps therapeutic implications.

Image processing for detecting complete two dimensional properties' distribution of granules produced in twin screw granulation.

In this study, an image processing technique is implemented to measure complete two-dimensional particle size and liquid content distribution (2D-distribution) of the granules produced in twin screw granulation (TSG). The effects of liquid binder viscosity and liquid to solid ratio (L/S) on the 2D-distribution, and the residence time distribution were studied. The effect of screw configuration on granule formation at different conditions was also investigated, were the mean residence time distribution (MRTD) in conveying elements decreases with the increase of L/S ratio and viscosity. While in kneading elements the MRTD decreases with the increase of L/S and increases with the increase of viscosity. The mean liquid saturation level of the granule is exponentially related to its size. As well, the increase in binder viscosity and L/S ratio leads to more uneven/bi-model particle size distribution (PSD) in the conveying elements, while kneading elements change the initial bi-model PSDs into more homogenous mono-model like distributions.

Complete two dimensional population balance modelling of wet granulation in twin screw.

In this study, a complete two dimensional (internal coordinates) population balance model (2D-PBM) is developed, calibrated and validated as a predictive tool for predicting the particle size and the liquid content distribution of the granules produced from twin screw granulation (TSG). The model is calibrated and validated using experimental distributions for the two internal coordinates that are captured using image processing. Granulation runs are conducted at multiple liquid to solid (L/S) ratios and liquid binder viscosities, and then used to calibrate and validate the 2D-PBM. The mathematical model accounts for aggregation and breakage of the particles occurring in three zones of the TSG with inhomogeneous screw configurations (2 conveying zones and 1 kneading zone). A Madec aggregation kernel, and a linear breakage selection function are used in the 2D-PBM and finite volume numerical approximation is used for solving the model. The calibrated model shows that the aggregation rate in the conveying elements is higher than in the kneading elements while the breakage rate in the kneading elements is much higher than in the conveying elements. Also, the increase in L/S ratio and liquid viscosity leads to higher aggregation rates and lower breakage rates.

Compartmental approach for modelling twin-screw granulation using population balances.

In this study, a compartmental population balance model (CPBM) is developed as a predictive tool of particle size distribution (PSD) for wet granulation in co-rotating twin-screw granulator (TSG). This model is derived in terms of liquid to solid ratio (L/S) and screw speed representing the main process parameters of the TSG. The mathematical model accounts for aggregation and breakage of the particles occurring in five compartments of the TSG with inhomogeneous screw configurations (3 conveying zones and 2 kneading zones). Kapur's aggregation kernel is implemented in granulation and finite volume numerical method is adapted for solving the mathematical model. The results show a dramatic improvement in solution accuracy compared to the cell average numerical method. Moreover, Kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. Finally, the CPBM model is calibrated and validated using the experimental data.

Mass-based finite volume scheme for aggregation, growth and nucleation population balance equation.

In this paper, a new mass-based numerical method is developed using the notion of Forestier-Coste & Mancini (Forestier-Coste & Mancini 2012, SIAM J. Sci. Comput. 34, B840-B860. (doi:10.1137/110847998)) for solving a one-dimensional aggregation population balance equation. The existing scheme requires a large number of grids to predict both moments and number density function accurately, making it computationally very expensive. Therefore, a mass-based finite volume is developed which leads to the accurate prediction of different integral properties of number distribution functions using fewer grids. The new mass-based and existing finite volume schemes are extended to solve simultaneous aggregation-growth and aggregation-nucleation problems. To check the accuracy and efficiency, the mass-based formulation is compared with the existing method for two kinds of benchmark kernels, namely analytically solvable and practical oriented kernels. The comparison reveals that the mass-based method computes both number distribution functions and moments more accurately and efficiently than the existing method.

Multi-dimensional population balance modelling of pharmaceutical formulations for continuous twin-screw wet granulation: Determination of liquid distribution.

Two-dimensional population balance model (PBM) is developed in order to model pharmaceutical granules formation in a twin-screw wet granulator. Granule size and liquid content are considered as internal coordinates, while axial length of granulator is considered as external coordinate. Two types of initial liquid distribution are considered for the model development, i.e. constant and linear distributions. The main focus is on modeling and validation of liquid content distribution of granules. Regime-separated approach was used in order to capture the non-homogeneity of the granulator. The plug flow regime is considered for the conveying zone, while well-mixed regime is assumed for the kneading zone of twin-screw granulator. Aggregation and breakage are considered as the main mechanisms for granule formation and size control. Cell average method is used for solution of the PBM based on lumped parameter approach. In order to determine experimentally the distribution of liquid, liquid binder by dye addition was used in the process. The model findings are calibrated and validated by comparing with measured liquid content in each size fraction. The measured data is collected on a 12 mm twin-screw wet granulator using microcrystalline cellulose (MCC) and water soluble dye plus water as binder. The model indicated to be valid for MCC and needs to be validated with further excipients. The results revealed that increasing screw speed led to more uniform liquid distribution. Finally, the model findings indicated that 2D PBM is capable of predicting liquid distribution, and can be used as predictive tool in pharmaceutical continuous granulation.

ANN-Kriging hybrid model for predicting carbon and inorganic phosphorus recovery in hydrothermal carbonization.

Modeling of hydrothermal carbonization (HTC) of poultry litter to high-value materials was conducted in order to understand the process and predict the influence of process parameters on product properties. Reaction temperature and time were considered as inputs, whereas carbon and inorganic phosphorous recovery were considered as responses in the model. Artificial neural network (ANN) model was used in order to correlate the process parameters to the outputs. The model was trained and validated using the data collected from HTC experiments carried out at temperatures between 150 ≤ T ≤ 300 °C, and residence time between 30 ≤ t ≤ 480 min. In order to improve the predictability of ANN, more theoretical data points were generated using Kriging approach based on the available measured data. Kriging interpolation improved the ANN model dramatically in training and validation phases, where the carbon recovery model fitting was improved by 0.94% and 9.2% in training and validation respectively, and the inorganic phosphorous (IP) recovery model fitting was improved by a staggering 16.4% and 19.6% in training and validation respectively. This improvement is also reflecting on the derived profiles of carbon and IP recovery in terms of the process parameters. The validated model was then used to understand the effect of process parameters on the response. It was revealed that temperature has more significant effect on the carbon and phosphorous recovery, while the effect of reaction time is more important at low reaction temperatures. The derived profiles shows a monotonic increase in IP recovery and a monotonic decrease in Carbon recovery at higher temperatures and time, this is due to multiple mechanism occurring simultaneously in the HTC reactor at various temperatures and times.

Pyrolysis of waste tires: A modeling and parameter estimation study using Aspen Plus®.

This paper presents a simulation flowsheet model of a waste tire pyrolysis process with feed capacity of 150kg/h. A kinetic rate-based reaction model is formulated in a form implementable in the simulation package Aspen Plus, giving the flowsheet model the capability to predict more than 110 tire pyrolysis products as reported in experiments by Laresgoiti et al. (2004) and Williams (2013) for the oil and gas products respectively. The simulation model is successfully validated in two stages: firstly against experimental data from Olazar et al. (2008) by comparing the mass fractions for the oil products (gas, liquids (non-aromatics), aromatics, and tar) at temperatures of 425, 500, 550 and 610°C, and secondly against experimental results of main hydrocarbon products (C7 to C15) obtained by Laresgoiti et al. (2004) at temperatures of 400, 500, 600, and 700°C. The model was then used to analyze the effect of pyrolysis process temperature and showed that increased temperatures led to chain fractions from C10 and higher to decrease while smaller chains increased; this is attributed to the extensive cracking of the larger hydrocarbon chains at higher temperatures. The utility of the flowsheet model was highlighted through an energy analysis that targeted power efficiency of the process determined through production profiles of gasoline and diesel at various temperatures. This shows, through the summation of the net power gain from the plant for gasoline plus diesel that the maximum net power lies at the lower temperatures corresponding to minimum production of gasoline and maximum production of diesel. This simulation model can thus serve as a robust tool to respond to market conditions that dictate fuel demand and prices while at the same time identifying optimum process conditions (e.g. temperature) driven by process economics.