Understanding glioblastoma invasion using physically-guided neural networks with internal variables.

Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine.

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity.

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.

Computational modelling of multi-cell migration in a multi-signalling substrate.

Cell migration is a vital process in many biological phenomena ranging from wound healing to tissue regeneration. Over the past few years, it has been proven that in addition to cell-cell and cell-substrate mechanical interactions (mechanotaxis), cells can be driven by thermal, chemical and/or electrical stimuli. A numerical model was recently presented by the authors to analyse single cell migration in a multi-signalling substrate. That work is here extended to include multi-cell migration due to cell-cell interaction in a multi-signalling substrate under different conditions. This model is based on balancing the forces that act on the cell population in the presence of different guiding cues. Several numerical experiments are presented to illustrate the effect of different stimuli on the trajectory and final location of the cell population within a 3D heterogeneous multi-signalling substrate. Our findings indicate that although multi-cell migration is relatively similar to single cell migration in some aspects, the associated behaviour is very different. For instance, cell-cell interaction may delay single cell migration towards effective cues while increasing the magnitude of the average net cell traction force as well as the local velocity. Besides, the random movement of a cell within a cell population is slightly greater than that of single cell migration. Moreover, higher electrical field strength causes the cell slug to flatten near the cathode. On the other hand, as with single cell migration, the existence of electrotaxis dominates mechanotaxis, moving the cells to the cathode or anode pole located at the free surface. The numerical results here obtained are qualitatively consistent with related experimental works.

A coupled mechano-biochemical model for bone adaptation.

Bone remodelling is a fundamental biological process that controls bone microrepair, adaptation to environmental loads and calcium regulation among other important processes. It is not surprising that bone remodelling has been subject of intensive both experimental and theoretical research. In particular, many mathematical models have been developed in the last decades focusing in particular aspects of this complicated phenomenon where mechanics, biochemistry and cell processes strongly interact. In this paper, we present a new model that combines most of these essential aspects in bone remodelling with especial focus on the effect of the mechanical environment into the biochemical control of bone adaptation mainly associated to the well known RANKL-RANK-OPG pathway. The predicted results show a good correspondence with experimental and clinical findings. For example, our results indicate that trabecular bone is more severely affected both in disuse and disease than cortical bone what has been observed in osteoporotic bones. In future, the methodology proposed would help to new therapeutic strategies following the evolution of bone tissue distribution in osteoporotic patients.

Tuneable hydrogel patterns in pillarless microfluidic devices.

Organ-on-chip (OOC) technology has recently emerged as a powerful tool to mimic physiological or pathophysiological conditions through cell culture in microfluidic devices. One of its main goals is bypassing animal testing and encouraging more personalized medicine. The recent incorporation of hydrogels as 3D scaffolds into microfluidic devices has changed biomedical research since they provide a biomimetic extracellular matrix to recreate tissue architectures. However, this technology presents some drawbacks such as the necessity for physical structures as pillars to confine these hydrogels, as well as the difficulty in reaching different shapes and patterns to create convoluted gradients or more realistic biological structures. In addition, pillars can also interfere with the fluid flow, altering the local shear forces and, therefore, modifying the mechanical environment in the OOC model. In this work, we present a methodology based on a plasma surface treatment that allows building cell culture chambers with abutment-free patterns capable of producing precise shear stress distributions. Therefore, pillarless devices with arbitrary geometries are needed to obtain more versatile, reliable, and biomimetic experimental models. Through computational simulation studies, these shear stress changes are demonstrated in different designed and fabricated geometries. To prove the versatility of this new technique, a blood-brain barrier model has been recreated, achieving an uninterrupted endothelial barrier that emulates part of the neurovascular network of the brain. Finally, we developed a new technology that could avoid the limitations mentioned above, allowing the development of biomimetic OOC models with complex and adaptable geometries, with cell-to-cell contact if required, and where fluid flow and shear stress conditions could be controlled.

Experimentally-guided in silico design of engineered heart tissues to improve cardiac electrical function after myocardial infarction.

Engineered heart tissues (EHTs) built from human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) showed promising results for cardiac function restoration following myocardial infarction. Nevertheless, human iPSC-CMs have longer action potential and lower cell-to-cell coupling than adult-like CMs. These immature electrophysiological properties favor arrhythmias due to the generation of electrophysiological gradients when hiPSC-CMs are injected in the cardiac tissue. Culturing hiPSC-CMs on three-dimensional (3D) scaffolds can promote their maturation and influence their alignment. However, it is still uncertain how on-scaffold culturing influences the overall electrophysiology of the in vitro and implanted EHTs, as it requires expensive and time consuming experimentation. Here, we computationally investigated the impact of the scaffold design on the EHT electrical depolarization and repolarization before and after engraftment on infarcted tissue. We first acquired and processed electrical recordings from in vitro EHTs, which we used to calibrate the modeling and simulation of in silico EHTs to replicate experimental outcomes. Next, we built in silico EHT models for a range of scaffold pore sizes, shapes (square, rectangular, auxetic, hexagonal) and thicknesses. In this setup, we found that scaffolds made of small (0.2 mm2), elongated (30° half-angle) hexagons led to faster EHT activation and better mimicked the cardiac anisotropy. The scaffold thickness had a marginal role on the not engrafted EHT electrophysiology. Moreover, EHT engraftment on infarcted tissue showed that the EHT conductivity should be at least 5% of that in healthy tissue for bidirectional EHT-myocardium electrical propagation. For conductivities above such threshold, the scaffold made of small elongated hexagons led to the lowest activation time (AT) in the coupled EHT-myocardium. If the EHT conductivity was further increased and the hiPSC-CMs were uniformly oriented parallel to the epicardial cells, the total AT and the repolarization time gradient decreased substantially, thus minimizing the likelihood for arrhythmias after EHT transplantation.

3D computational modelling of cell migration: a mechano-chemo-thermo-electrotaxis approach.

Single cell migration constitutes a fundamental phenomenon involved in many biological events such as wound healing, cancer development and tissue regeneration. Several experiments have demonstrated that, besides the mechanical driving force (mechanotaxis), cell migration may be also influenced by chemical, thermal and/or electrical cues. In this paper, we present an extension of a previous model of the same authors adding the effects of chemotaxis, thermotaxis and electrotaxis to the initial mechanotaxis model of cell migration, allowing us to predict cell migration behaviour under different conditions and substrate properties. The present model is based on the balance of effective forces during cell motility in the presence of the several guiding cues. This model has been applied to several numerical experiments to demonstrate the effect of the different drivers onto the cell path and final location within a certain three-dimensional substrate with heterogeneous properties. Our findings indicate that the presence of the chemotaxis, thermotaxis and/or electrotaxis reduce, in general, the random component of cell movement, being this reduction more important in the case of electrotaxis that can be considered a dominating signal during cell migration (besides the underlying mechanical effects). These results are qualitatively in agreement with well-known experimental ones.

Response of sheep chondrocytes to changes in substrate stiffness from 2 to 20 Pa: effect of cell passaging.

AIM: The influence of culture substrate stiffness (in the kPa range) on chondrocyte behavior has been described. Here we describe the response to variations in substrate stiffness in a soft range (2-20 Pa), as it may play a role in understanding cartilage physiopathology. METHODS: We developed a system for cell culture in substrates with different elastic moduli using collagen hydrogels and evaluated chondrocytes after 2, 4, and 7 days in monolayer and three-dimensional (3D) cultures. Experiments were performed in normoxia and hypoxia in order to describe the effect of a low oxygen environment on chondrocytes. Finally, we also evaluated if dedifferentiated cells preserve the capacity for mechanosensing. RESULTS: Chondrocytes showed less proliferating activity when cultured in monolayer in the more compliant substrates. Expression of the cartilage markers Aggrecan (Acan), type II collagen (Col2a1), and Sox9 was upregulated in the less stiff gels (both in monolayer and in 3D culture). Stiffer gels induced an organization of the actin cytoskeleton that correlated with the loss of a chondrocyte phenotype. When cells were cultured in hypoxia, we observed changes in the cellular response that were mediated by HIF-1α. Results in 3D hypoxia cultures were opposite to those found in normoxia, but remained unchanged in monolayer hypoxic experiments. Similar results were found for dedifferentiated cells. CONCLUSIONS: Chondrocytes respond differently according to the stiffness of the substrate. This response depends greatly on the oxygen environment and on whether the chondrocyte is embedded or grown onto the hydrogel, since mechanosensing capacity was not lost with cell expansion.

Culture of human bone marrow-derived mesenchymal stem cells on of poly(L-lactic acid) scaffolds: potential application for the tissue engineering of cartilage.

PURPOSE: Due to the attractive properties of poly(L-lactic acid) (PLLA) for tissue engineering, the aim was to determine the growth and differentiation capacity of mesenchymal stromal cells (MSCs) in PLLA scaffolds and their potential use in the treatment of cartilage diseases. METHODS: MSCs were cultured in PLLA films and thin porous membranes to study adherence and proliferation. Permeability and porosity were determined for the different scaffolds employed. The optimal conditions for cell seeding were first determined, as well as cell density and distribution inside the PLLA. Scaffolds were then maintained in expansion or chondrogenic differentiation media for 21 days. Apoptosis, proliferation and chondrogenic differentiation was assessed after 21 days in culture by immunohistochemistry. Mechanical characteristics of scaffolds were determined before and after cell seeding. RESULTS: MSCs uniformly adhered to PLLA films as well as to porous membranes. Proliferation was detected only in monolayers of pure PLLA, but was no longer detected after 10 days. Mechanical characterization of PLLA scaffolds showed differences in the apparent compression elastic modulus for the two sizes used. After determining high efficiencies of seeding, the production of extracellular matrix (ECM) was determined and contained aggrecan and collagens type I and X. ECM produced by the cells induced a twofold increase in the apparent elastic modulus of the composite. CONCLUSIONS: Biocompatible PLLA scaffolds have been developed that can be efficiently loaded with MSCs. The scaffold supports chondrogenic differentiation and ECM deposition that improves the mechanics of the scaffold. Although this improvement does not met the expectations of a hyaline-like cartilage ECM, in part due to the lack of a mechanical stimulation, their potential use in the treatment of cartilage pathologies encourages to improve the mechanical component.

Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology.

Cellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favourable/unfavourable conditions or treatments.

A mechanobiological model for tumor spheroid evolution with application to glioblastoma: A continuum multiphysics approach.

BACKGROUND: Spheroids are in vitro quasi-spherical structures of cell aggregates, eventually cultured within a hydrogel matrix, that are used, among other applications, as a technological platform to investigate tumor formation and evolution. Several interesting features can be replicated using this methodology, such as cell communication mechanisms, the effect of gradients of nutrients, or the creation of realistic 3D biological structures. The main objective of this work is to link the spheroid evolution with the mechanical activity of cells, coupled with nutrient consumption and the subsequent cell dynamics. METHOD: We propose a continuum mechanobiological model which accounts for the most relevant phenomena that take place in tumor spheroid evolution under in vitro suspension, namely, nutrient diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. The model is qualitatively validated, after calibration of the model parameters, versus in vitro experiments of spheroids of different glioblastoma cell lines. RESULTS: Our model is able to explain in a novel way quite different setups, such as spheroid growth (up to six times the initial configuration for U-87 MG cell line) or shrinking (almost half of the initial configuration for U-251 MG cell line); as the result of the mechanical interplay of cells driven by cellular evolution. CONCLUSIONS: Glioblastoma tumor spheroid evolution is driven by mechanical interactions of the cell aggregate and the dynamical evolution of the cell population. All this information can be used to further investigate mechanistic effects in the evolution of tumors and their role in cancer disease.

The Role of Mechanical Properties and Structure of Type I Collagen Hydrogels on Colorectal Cancer Cell Migration.

Mechanical interactions between cells and their microenvironment play an important role in determining cell fate, which is particularly relevant in metastasis, a process where cells invade tissue matrices with different mechanical properties. In vitro, type I collagen hydrogels have been commonly used for modeling the microenvironment due to its ubiquity in the human body. In this work, the combined influence of the stiffness of these hydrogels and their ultrastructure on the migration patterns of HCT-116 and HT-29 spheroids are analyzed. For this, six different types of pure type I collagen hydrogels by changing the collagen concentration and the gelation temperature are prepared. The stiffness of each sample is measured and its ultrastructure is characterized. Cell migration studies are then performed by seeding the spheroids in three different spatial conditions. It is shown that changes in the aforementioned parameters lead to differences in the mechanical stiffness of the matrices as well as the ultrastructure. These differences, in turn, lead to distinct cell migration patterns of HCT-116 and HT-29 spheroids in either of the spatial conditions tested. Based on these results, it is concluded that the stiffness and the ultrastructural organization of the matrix can actively modulate cell migration behavior in colorectal cancer spheroids.

A comparison between the effect of systemic and coated drug delivery in osteoporotic bone after dental implantation.

The increased life expectancy has boomed the demand of dental implants in the elderly. As a consequence, considering the effect of poorer bone quality, due to aging or associated diseases such as osteoporosis, on the success of dental restoration is becoming increasingly important. Bisphosphonates are one of the most used drugs to overcome the effect of osteoporosis as they increase bone density. Bisphosphonates modify the physiological bone remodeling process by adhering to the bone surface, reducing the activity of osteoclasts. This study aims at comparing the effect on bone remodeling of two drug delivery methods of Bisphosphonates: local delivery by coating the implant surface and systemic delivery. A chemo-mechano-biological bone remodeling model validated in a previous paper was used here. The two drug delivery schemes were modeled by means of a finite element approach. In the systemic drug delivery case, the amount of drug that reaches the bone compartment was calculated using a pharmacokinetic model while in the local drug delivery system, the dose was calculated using Fickean diffusion. In particular, the effect of Zoledronate is studied here. The two drug delivery approaches are compared between them and with a control case with no drug. The results show that the use of Bisphosphonates increases the mechanical strength of bone, thus improving the implant fixation along time. Systemic drug delivery affects the entire skeleton, while local drug delivery only affects the area around the dental implant, which reduces the side effects of Bisphosphonates, such as increasing the mineral content, which may promote bone brittleness and microdamage far from the implant. These results support the conclusion that dental implants coated with Bisphosphonates can be a good solution for osteoporotic or low bone density patients without the long-term side effects of systemic drug delivery.

Fabrication of human myocardium using multidimensional modelling of engineered tissues.

Biofabrication of human tissues has seen a meteoric growth triggered by recent technical advancements such as human induced pluripotent stem cells (hiPSCs) and additive manufacturing. However, generation of cardiac tissue is still hampered by lack of adequate mechanical properties and crucially by the often unpredictable post-fabrication evolution of biological components. In this study we employ melt electrowriting (MEW) and hiPSC-derived cardiac cells to generate fibre-reinforced human cardiac minitissues. These are thoroughly characterized in order to build computational models and simulations able to predict their post-fabrication evolution. Our results show that MEW-based human minitissues display advanced maturation 28 post-generation, with a significant increase in the expression of cardiac genes such as MYL2, GJA5, SCN5A and the MYH7/MYH6 and MYL2/MYL7 ratios. Human iPSC-cardiomyocytes are significantly more aligned within the MEW-based 3D tissues, as compared to conventional 2D controls, and also display greater expression of C×43. These are also correlated with a more mature functionality in the form of faster conduction velocity. We used these data to develop simulations capable of accurately reproducing the experimental performance. In-depth gauging of the structural disposition (cellular alignment) and intercellular connectivity (C×43) allowed us to develop an improved computational model able to predict the relationship between cardiac cell alignment and functional performance. This study lays down the path for advancing in the development ofin silicotools to predict cardiac biofabricated tissue evolution after generation, and maps the route towards more accurate and biomimetic tissue manufacture.

Is arterial wall-strain stiffening an additional process responsible for atherosclerosis in coronary bifurcations?: an in vivo study based on dynamic CT and MRI.

Coronary bifurcations represent specific regions of the arterial tree that are susceptible to atherosclerotic lesions. While the effects of vessel compliance, curvature, pulsatile blood flow, and cardiac motion on coronary endothelial shear stress have been widely explored, the effects of myocardial contraction on arterial wall stress/strain (WS/S) and vessel stiffness distributions remain unclear. Local increase of vessel stiffness resulting from wall-strain stiffening phenomenon (a local process due to the nonlinear mechanical properties of the arterial wall) may be critical in the development of atherosclerotic lesions. Therefore, the aim of this study was to quantify WS/S and stiffness in coronary bifurcations and to investigate correlations with plaque sites. Anatomic coronary geometry and cardiac motion were generated based on both computed tomography and MRI examinations of eight patients with minimal coronary disease. Computational structural analyses using the finite element method were subsequently performed, and spatial luminal arterial wall stretch (LW(Stretch)) and stiffness (LW(Stiff)) distributions in the left main coronary bifurcations were calculated. Our results show that all plaque sites were concomitantly subject to high LW(Stretch) and high LW(Stiff), with mean amplitudes of 34.7 ± 1.6% and 442.4 ± 113.0 kPa, respectively. The mean LW(Stiff) amplitude was found slightly greater at the plaque sites on the left main coronary artery (mean value: 482.2 ± 88.1 kPa) compared with those computed on the left anterior descending and left circumflex coronary arteries (416.3 ± 61.5 and 428.7 ± 181.8 kPa, respectively). These findings suggest that local wall stiffness plays a role in the initiation of atherosclerotic lesions.

Modularity in developmental biology and artificial organs: a missing concept in tissue engineering.

Tissue engineering is reviving itself, adopting the concept of biomimetics of in vivo tissue development. A basic concept of developmental biology is the modularity of the tissue architecture according to which intermediates in tissue development constitute semiautonomous entities. Both engineering and nature have chosen the modular architecture to optimize the product or organism development and evolution. Bioartificial tissues do not have a modular architecture. On the contrary, artificial organs of modular architecture have been already developed in the field of artificial organs. Therefore the conceptual support of tissue engineering by the field of artificial organs becomes critical in its new endeavor of recapitulating in vitro the in vivo tissue development.

On the effect of antiresorptive drugs on the bone remodeling of the mandible after dental implantation: a mathematical model.

Bone remodeling identifies the process of permanent bone change with new bone formation and old bone resorption. Understanding this process is essential in many applications, such as optimizing the treatment of diseases like osteoporosis, maintaining bone density in long-term periods of disuse, or assessing the long-term evolution of the bone surrounding prostheses after implantation. A particular case of study is the bone remodeling process after dental implantation. Despite the overall success of this type of implants, the increasing life expectancy in developed countries has boosted the demand for dental implants in patients with osteoporosis. Although several studies demonstrate a high success rate of dental implants in osteoporotic patients, it is also known that the healing time and the failure rate increase, necessitating the adoption of pharmacological measures to improve bone quality in those patients. However, the general efficacy of these antiresorptive drugs for osteoporotic patients is still controversial, requiring more experimental and clinical studies. In this work, we investigate the effect of different doses of several drugs, used nowadays in osteoporotic patients, on the evolution of bone density after dental implantation. With this aim, we use a pharmacokinetic-pharmacodynamic (PK/PD) mathematical model that includes the effect of antiresorptive drugs on the RANK/RANK-L/OPG pathway, as well as the mechano-chemical coupling with external mechanical loads. This mechano-PK/PD model is then used to analyze the evolution of bone in normal and osteoporotic mandibles after dental implantation with different drug dosages. We show that using antiresorptive agents such as bisphosphonates or denosumab increases bone density and the associated mechanical properties, but at the same time, it also increases bone brittleness. We conclude that, despite the many limitations of these very complex models, the one presented here is capable of predicting qualitatively the evolution of some of the main biological and chemical variables associated with the process of bone remodeling in patients receiving drugs for osteoporosis, so it could be used to optimize dental implant design and coating for osteoporotic patients, as well as the drug dosage protocol for patient-specific treatments.

Image-based Cardiac Electrophysiology Simulation through the Meshfree Mixed Collocation Method.

State-of-the-art solvers for in silico cardiac electro-physiology employ the Finite Element Method to solve complex anatomical models. While this is a robust and accurate tech-nique, it requires a high-quality mesh to prevent its accuracy from being severely deteriorated. The generation of a good quality mesh for realistic anatomical models can be very time-consuming, making the translation to the clinics challenging, especially if we try to use patient-specific geometries.Aiming to tackle this challenge, we propose an image-based model generation approach based on the meshfree Mixed Col-location Method. The flexibility provided by this method during model generation allows building meshfree models directly from the image data in an automatic procedure. Furthermore, this approach allows interpreting the simulation results directly in the voxel coordinates system of the image.We simulate electrical propagation in a porcine biventricular model with the proposed method and we compare the results with those obtained using the Finite Element Method. We conclude that the proposed method can generate results that are in good agreement with the Finite Element Method solution, alleviating the requirement of a mesh and user-input during modeling with only minimum efficiency overhead.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.

Evidence-Based Selection on the Appropriate FIT Cut-Off Point in CRC Screening Programs in the COVID Pandemic.

Background: The COVID pandemic has forced the closure of many colorectal cancer (CRC) screening programs. Resuming these programs is a priority, but fewer colonoscopies may be available. We developed an evidence-based tool for decision-making in CRC screening programs, based on a fecal hemoglobin immunological test (FIT), to optimize the strategy for screening a population for CRC. Methods: We retrospectively analyzed data collected at a regional CRC screening program between February/2014 and November/2018. We investigated two different scenarios: not modifying vs. modifying the FIT cut-off value. We estimated program outcomes in the two scenarios by evaluating the numbers of cancers and adenomas missed or not diagnosed in due time (delayed). Results: The current FIT cut-off (20-µg hemoglobin/g feces) led to 6,606 colonoscopies per 100,000 people invited annually. Without modifying this FIT cut-off value, when the optimal number of individuals invited for colonoscopies was reduced by 10-40%, a high number of CRCs and high-risk adenomas (34-135 and 73-288/100.000-people invited, respectively) will be undetected every year. When the FIT cut-off value was increased to where the colonoscopy demand matched the colonoscopy availability, the number of missed lesions per year was remarkably reduced (9-36 and 29-145/100.000 people, respectively). Moreover, the unmodified FIT scenario outcome was improved by prioritizing the selection process based on sex (males) and age, rather than randomly reducing the number invited. Conclusions: Assuming a mismatch between the availability and demand for annual colonoscopies, increasing the FIT cut-off point was more effective than randomly reducing the number of people invited. Using specific risk factors to prioritize access to colonoscopies should be also considered.