Development of a computational simulator of the extracorporeal membrane oxygenation and its validation with in vitro measurements.

In the recent years, the use of extracorporeal membrane oxygenation (ECMO) has grown substantially, posing the need of having specialized medical and paramedical personnel dedicated to it. Optimization of the therapy, definition of new therapeutic strategies, and ECMO interaction with the cardiorespiratory system require numerous specific skills and preclinical models for patient successful management. The aim of the present work is to develop and validate a computational model of ECMO and connect it to an already existing lumped parameter model of the cardiorespiratory system. The ECMO model was connected between the right atrium and the aorta of the cardiorespiratory simulator. It includes a hydraulic module that is a representation of the tubing, oxygenator, and pump. The resulting pressures and flows within the ECMO circuit were compared to the measurements conducted in vitro on a real ECMO. Additionally, the hemodynamic effects the ECMO model elicited on the cardiorespiratory simulator were compared with experimental data taken from the literature. The comparison between the hydraulic module and the in vitro measurements evidenced a good agreement in terms of flow, pressure drops across the pump, across the oxygenator and the tubing (maximal percentage error recorded was 17.6%). The hemodynamic effects of the ECMO model on the cardiovascular system were in agreement with what observed experimentally in terms of cardiac output, systemic pressure, pulmonary arterial pressure, and left atrial pressure. The ECMO model we developed and embedded into the cardiorespiratory simulator, is a useful tool for the investigation of basic physiological mechanisms and principles of ECMO therapy. The model was sided by a user interface dedicated to training applications. As such, the resulting simulator can be used for the education of students, medical and paramedical personnel.

Choline Chloride-Lactic Acid-Based NADES As an Extraction Medium in a Response Surface Methodology-Optimized Method for the Extraction of Phenolic Compounds from Hazelnut Skin.

Deep eutectic solvents (DESs) are promising green solvents for the extraction of compounds from food byproducts. Hazelnut (Corylus avellana L.) is one of the most commonly cultivated tree nuts worldwide. The skin represents one of the major byproducts of the hazelnut industry and accounts for 2.5% of the total hazelnut kernel weight. It is a rich source of phenolic compounds like flavan-3-ols, flavonols, dihydrochalcones, and phenolic acids. In this work, fifteen DESs based on choline chloride and betaine, with different compositions, were studied in order to test their phenolic compounds extraction efficiency through the determination of their total concentration via Folin-Ciocalteu assay. A qualitative analysis of extracted phenolic compounds was assessed by HPLC with UV and MS detection. Using the DES with the best extraction efficiency, a new ultrasound-assisted solid liquid extraction (UA-SLE) method was optimized though the response surface methodology (RSM), taking into account some extraction parameters. Efficient recovery of extracted phenolic compounds was achieved using a 35% water solution of choline chloride and lactic acid (molar ratio 1:2) as an extraction solvent, working at 80 °C and with a solid-to-solvent ratio of 1:25 gmL-1. The optimized conditions made it possible to recover 39% more phenolic compounds compared to a classic organic solvent.

A Novel Three-Compartmental Model for Artificial Pancreas: Development and Validation.

Closed-loop insulin delivery system, also known as artificial pancreas (AP), provides the blood glucose control in diabetic patients, enabling the automatic blood-sugar management and reducing the risks and improving the lives of people with diabetes. A new three-compartmental model of glucose-insulin interaction for AP is presented and tested in this paper. The glucose and insulin "spaces" are split into a plasma compartment and interstitial fluids compartment, respectively. The model includes an additional subcutaneous compartment and provides three explicit delays and three parameters influencing the regulatory system and correlating with the physiopathology of the patients. Two delays are related with hepatic glucose production and insulin secretion; the third delay represents the lag time in the absorption of exogenous insulin in subcutaneous tissue. The parameters regulate the system dynamics acting on the glucose utilization and the insulin secretion. The clinical data (including information on food ingestion and exogenous insulin injection) from five case studies of Type 1 diabetics are presented and used to validate the mathematical model. After training the parameters for each case study, the model well simulates the glucose level during a 4-day test. The estimated values are physiologically meaningful and provide a further insight on the subject's dysfunctions and on the state of the disease. The results have been also compared with a parallel simulation carried out by implementing a previous two-compartmental model. The proposed algorithm produces a lower sum of the squared error between the simulated and the measured glucose concentrations over time.

Removal of Methylene Blue from Wastewater by Waste Roots from the Arsenic-Hyperaccumulator Pteris vittata: Fixed Bed Adsorption Kinetics.

Phytoremediation of arsenic-contaminated water was successfully conducted by means of the perennial fern Pteris vittate, which is an arsenic-hyperaccumulator plant able to grow in hydroponic cultures. In order to avoid the costs linked to the disposal of As-contaminated biomass, in this work, Pteris vittata waste roots were tested as a low-cost bio-adsorbent for the removal of methylene blue (MB) from water in a fixed-bed adsorption configuration. As a matter of fact, methylene blue can negatively impact the growth and health of algae and plants by blocking light from reaching them in water, which can alter their normal biological processes. Previous works have already shown the potentiality of such material toward the uptake of methylene blue; however, all the studies conducted were just focused on batch-mode experiments. In this work, column runs were carried out at 20 °C, evaluating the bed void fraction for each test and hence estimating the apparent density of the material (300 g/L). The breakthrough curves collected were fitted by means of a mathematical model based on the linear driving force (LDF) approximation to obtain information on the mass transfer mechanism occurring in the system. A relation for the product between the LDF mass transfer coefficient and the solid specific surface (kLDFas) with respect to the Reynolds (Re) dimensionless number was obtained (kLDFas=0.45Re). The range of validity of such expression was Re<0.025. Its applicability was deeply discussed: in such conditions, the technology is ready to be tested at larger scales.

Modeling and Validation of an Ultra-Compact Regenerative Liver Dialysis Device.

The availability of a wearable artificial liver that facilitates extracorporeal dialysis outside of medical facilities would represent a significant advancement for patients requiring dialysis. The objective of this preliminary investigation is to explore, using validated mathematical models based on in vitro data, the feasibility of developing a novel, cost-effective, and highly compact extracorporeal liver support device that can be employed as a transitional therapy to transplantation outside of clinical settings. Such an innovation would offer substantial cost savings to the national healthcare system while significantly improving the patient's quality of life. The experimental components consisted of replacing traditional adsorbent materials with albumin-functionalized silica microspheres due to their capacity to adsorb bilirubin, one of the toxins responsible for liver failure. Two configurations of the dialysis module were tested: one involved dispersing the adsorbent particles in dialysis fluid, while the other did not require dialysis fluid. The results demonstrate the superior performance of the first configuration compared to the second. Although the clinical applicability of these models remains distant from the current stage, further studies will focus on optimizing these models to develop a more compact and wearable device.

Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application.

BACKGROUND: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population. METHODS: A Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) Recurrent Neural Network were optimized on glucose, insulin, and meal data of 10 virtual pediatric patients. The trained models were then implemented on two edge-computing boards to evaluate the feasibility of an edge system for glucose forecasting in terms of prediction accuracy and inference time. RESULTS: The LSTM model achieved the best numeric and clinical accuracy when tested in the .tflite format, whereas the CNN achieved the best clinical accuracy in uint8. The inference time for each prediction was far under the limit represented by the sampling period. CONCLUSION: Both models effectively predict glucose in pediatric patients in terms of numerical and clinical accuracy. The edge implementation did not show a significant performance decrease, and the inference time was largely adequate for a real-time application.

Effect of Water-Ethanol Extraction as Pre-Treatment on the Adsorption Properties of Aloe vera Waste.

The adsorption properties of Aloe vera (Aloe barbadensis Miller) for the uptake of Methylene Blue (MB) from water were investigated after pre-treating the material with water-ethanol solutions at different ethanol concentrations: 0% v/v (AV0), 25% v/v (AV25), and 50% v/v (AV50). The pre-treated materials were characterized as follows: the pHZC was evaluated to be 6, 5.7, and 7.2 for AV0, AV25, and AV50, respectively; from BET-BJH analysis the mesoporous nature of the material and an increase from 108.2 (AV0) to 331.7 (AV50) m2/kg of its solid surface area was observed; TG analysis revealed a significat increase in volatile compounds from the untreated (5.4%) to the treated materials (8.9%, 10.3%, and 11.3% for AV0, AV25, and AV50, respectively). Adsorption batch tests were then performed to investigate the equilibrium, the kinetics, and the thermodynamics of the process. Results suggested that the Langmuir model was in agreement with the experimental results, and values for qmax of 199 mg/g, 311 mg/g, and 346 mg/g were calculated for AV0, AV25, and AV50, respectively. The kinetic results were used to develop a mathematical model to estimate the effective diffusion coefficient for each type of Aloe adopted. Effective diffusion coefficients of 5.43·10-7 cm2/min, 3.89·10-7 cm2/min, and 5.78·10-7 cm2/min were calculated for AV0, AV25, and AV50, respectively. It was found that pre-treatment, on the one hand, enhances the adsorption capacity of the material and on the other, reduces its affinity toward MB uptake.

Biorefining food waste through the anaerobic conversion of endogenous lactate into caproate: A fragile balance between microbial substrate utilization and product inhibition.

New technologies development and renewable source exploitation are key tools to realize the European Green Deal and to boost the bio-based economy. In this context, fermentation of organic residues as food waste is an efficient method to obtain marketable products such as carboxylic acids widely applied in industrial production. Under favourable thermodynamic conditions, short chain fatty acids deriving from primary fermentation could be biologically converted into medium-chain fatty acids as caproate via chain elongation (CE) process, by using ethanol or lactate as electron donors. This study evaluates the effectivity of producing caproate from Food Waste extract rich in organics with in situ electron donor production. The test carried out at OLR 15 gCOD L-1d-1 showed high Volatile Fatty Acids (from acetic to caproic acid) yields (0.37 g g-1CODfed), with a maximum caproate concentration of 8 g L-1. The associated microbiome was composed by lactate-producing bacteria (Corynebacterium, Lactobacillus, and Olsenella) and by chain elongators (Clostridiaceae and Caproiciproducens). By stressing the system with OLR increase up to 20 gCOD L-1d-1, the CE process was inhibited by the high concentration of caproate (low occurrence of Clostridiaceae and Caproiciproducens). Nevertheless, after few days of stop-feeding regime imposed to the system, the microbiome restored its capability to proceed with lactate-based CE pathways. Different batch tests carried out with the inhibited biomass at increasing initial caproate concentration confirmed its impact on lactate utilization kinetics.

In-Silico Prediction of Oral Drug Bioavailability: A multi-boluses approach.

This work focuses on a new mathematical model able to describe in a simple manner the intestinal physiology, in order to better study drug absorption and bioavailability. The aim of our model is to overcome the limitations of physiological pharmacokinetics models of the literature, introducing a different modelling approach. The core of the new proposed model is a Discrete-Continuous Approach (DCA): a sequence of boluses travels in the investigated portion of the intestine, in counter-current with blood that flows in continuous mode. No empirical equations are implemented in this model. Simulation results show an excellent correlation between the predicted and experimental concentration profile used to validate our model. Our new approach provides a simple tool, with a good reliability, to analyze a very complex phenomenon, using only few parameters.

Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques.

BACKGROUND: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. METHODS: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). RESULTS: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. CONCLUSIONS: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

The impact of the intestinal microbiota and the mucosal permeability on three different antibiotic drugs.

BackgroundThe totality of bacteria, protozoa, viruses and fungi that lives in the human body is called microbiota. Human microbiota specifically colonizes the skin, the respiratory and urinary tract, the urogenital tract and the gastrointestinal system. This study focuses on the intestinal microbiota to explore the drug-microbiota relationship and, therefore, how the drug bioavailability changes in relation to the microbiota biodiversity to identify more personalized therapies, with the minimum risk of side effects. MethodsTo achieve this goal, we developed a new mathematical model with two compartments, the intestine and the blood, which takes into account the colonic mucosal permeability variation - measured by Ussing chamber system on human colonic mucosal biopsies - and the fecal microbiota composition, determined through microbiota 16S rRNA sequencing analysis. Both of the clinical parameters were evaluated in a group of Irritable Bowel Syndrome patients compared to a group of healthy controls. Key ResultsThe results show that plasma drug concentration increases as bacterial concentration decreases, while it decreases as intestinal length decreases too. ConclusionsThe study provides interesting data since in literature there are not yet mathematical models with these features, in which the importance of intestinal microbiota, the "forgotten organ", is considered both for the subject health state and in the nutrients and drugs metabolism.

A revised Sorensen model: Simulating glycemic and insulinemic response to oral and intra-venous glucose load.

In 1978, Thomas J. Sorensen defended a thesis in chemical engineering at the University of California, Berkeley, where he proposed an extensive model of glucose-insulin control, model which was thereafter widely employed for virtual patient simulation. The original model, and even more so its subsequent implementations by other Authors, presented however a few imprecisions in reporting the correct model equations and parameter values. The goal of the present work is to revise the original Sorensen's model, to clearly summarize its defining equations, to supplement it with a missing gastrio-intestinal glucose absorption and to make an implementation of the revised model available on-line to the scientific community.

Hydrogen Production via Steam Reforming: A Critical Analysis of MR and RMM Technologies.

'Hydrogen as the energy carrier of the future' has been a topic discussed for decades and is today the subject of a new revival, especially driven by the investments in renewable electricity and the technological efforts done by high-developed industrial powers, such as Northern Europe and Japan. Although hydrogen production from renewable resources is still limited to small scale, local solutions, and R&D projects; steam reforming (SR) of natural gas at industrial scale is the cheapest and most used technology and generates around 8 kg CO2 per kg H2. This paper is focused on the process optimization and decarbonization of H2 production from fossil fuels to promote more efficient approaches based on membrane separation. In this work, two emerging configurations have been compared from the numerical point of view: the membrane reactor (MR) and the reformer and membrane module (RMM), proposed and tested by this research group. The rate of hydrogen production by SR has been calculated according to other literature works, a one-dimensional model has been developed for mass, heat, and momentum balances. For the membrane modules, the rate of hydrogen permeation has been estimated according to mass transfer correlation previously reported by this research group and based on previous experimental tests carried on in the first RMM Pilot Plant. The methane conversion, carbon dioxide yield, temperature, and pressure profile are compared for each configuration: SR, MR, and RMM. By decoupling the reaction and separation section, such as in the RMM, the overall methane conversion can be increased of about 30% improving the efficiency of the system.

A new gastro-intestinal mathematical model to study drug bioavailability.

This work focuses on a new mathematical model which describes the gastro-intestinal absorption of drugs and the effect of food interactions on drugs bioavailability. The model structure consists of five compartments (stomach, duodenum, jejunum feeding, intestine and blood) simulated though different in-series reactors. All the enzymatic reactions taking place in the gastro-intestinal system are described through the Michaelis-Menten kinetic equations. The model has been tested for drug administration (paracetamol and ketoprofen) with and without the meal digestion. The model has been validated through pharmacokinetics curves obtained from in vivo tests (reported in the literature) and used to simulate the drug absorption dynamics in different conditions. The maximum blood concentration were 0.153 mmol L-1 and 0.0243 mmol L-1, respectively for paracetamol and ketoprofen. The time to reach the maximum concentration for the paracetamol and ketoprofen was around 55 min. In case of contemporary meal digestion, the maximum concentration of paracetamol in the blood was 0.100 mmol L-1 and 0.0135 mmol L-1 for ketoprofen; the time to reach the maximum concentration was 3 h and 45 min for paracetamol and 3 h and 35 min for ketoprofen. The drugs showed different pharmacokinetics, in agreement with the literature, during the digestion of food. To show the predictive capacity of the model, the simulations were also compared against additional experimental data (obtained from in vivo tests available in the literature) relative to ketoprofen administration with food.

Oxygen Consumption Characteristics in 3D Constructs Depend on Cell Density.

Oxygen is not only crucial for cell survival but also a determinant for cell fate and function. However, the supply of oxygen and other nutrients as well as the removal of toxic waste products often limit cell viability in 3-dimensional (3D) engineered tissues. The aim of this study was to determine the oxygen consumption characteristics of 3D constructs as a function of their cell density. The oxygen concentration was measured at the base of hepatocyte laden constructs and a tightly controlled experimental and analytical framework was used to reduce the system geometry to a single coordinate and enable the precise identification of initial and boundary conditions. Then dynamic process modeling was used to fit the measured oxygen vs. time profiles to a reaction and diffusion model. We show that oxygen consumption rates are well-described by Michaelis-Menten kinetics. However, the reaction parameters are not literature constants but depend on the cell density. Moreover, the average cellular oxygen consumption rate (or OCR) also varies with density. We discuss why the OCR of cells is often misinterpreted and erroneously reported, particularly in the case of 3D tissues and scaffolds.

Anaerobic co-digestion of food waste and waste activated sludge: ADM1 modelling and microbial analysis to gain insights into the two substrates' synergistic effects.

The reasons for the acidification problem affecting Food Waste (FW) anaerobic digestion were explored, combining the outcomes of microbiological data (FISH and CARD-FISH) and process modelling, based on the Anaerobic Digestion Model n°1 (ADM1). Long term semi continuous experiments were carried out, both with sole FW and with Waste Activated Sludge (WAS) as a co-substrate, at varying operational conditions (0.8-2.2 g VS L-1 d-1) and FW / WAS ratios. Acidification was observed along FW mono-digestion, making it necessary to buffer the digesters; ADM1 modelling and experimental results suggested that this phenomenon was due to the methanogenic activity decline, most likely related to a deficiency in trace elements. WAS addition, even at proportions as low as 10% of the organic load, settled the acidification issue; this ability was related to the promotion of the methanogenic activity and the consequent enhancement of acetate consumption, rather than to WAS buffering capacity. The ability of the ADM1 to model processes affected by low microbial activity, such as FW mono-digestion, was also assessed. It was observed that the ADM1 was only adequate for digestions with a high activity level for both bacteria and methanogens (FISH/CARD-FISH ratio preferably >0.8) and, under these conditions, the model was able to correctly predict the relative abundance of both microbial populations, extrapolated from FISH analysis.

Mass Transfer Coefficient in Multi-Stage Reformer/Membrane Modules for Hydrogen Production.

Hydrogen is a promising energy carrier, and is exploitable to extract energy from fossil fuels, biomasses, and intermittent renewable energy sources and its generation from fossil fuels, with CO2 separation at the source being one of the most promising pathways for fossil fuels' utilization. This work focuses on a particular configuration called the Reformer and Membrane Module (RMM), which alternates between stages of Steam Reforming (SR) reactions with H2 separation stages to overcome the thermodynamic limit of the conventional SR. The configuration has numerous advantages with respect to the more widely studied and tested membrane reactors, and has been tested during a pilot-scale research project. Although numerous modelling works appeared in the literature, the design features of the material exchanger (in the so-called RMM architecture) of different geometrical configurations have not been developed, and the mass transfer correlations, capable of providing design tools useful for such membrane modules, are not available. The purpose of this work is therefore to apply a physical-mathematical model of the mass transfer, in three different geometries, considering both concentration polarization and membrane permeation, in order to: (i) simulate the cited experimental results; (ii) estimate the scaling-up correlations for the "material exchange modules"; and (iii) identify the mass transfer limiting regime in relation to the gas mass flow rate.

Thermodynamic features of dioxins' adsorption.

In this paper, the six more poisonous species among all congeners of dioxin group are taken into account, and the P-T diagram for each of them is developed. Starting from the knowledge of vapour tensions and thermodynamic parameters, the theoretical adsorption isotherms are calculated according to the Langmuir's model. In particular, the Langmuir isotherm parameters (K and wmax) have been validated through the estimation of the adsorption heat (ΔHads), which varies in the range 20-24kJ/mol, in agreement with literature values. This result will allow to put the thermodynamical basis for a rational design of different process units devoted to dioxins removal.