On the quest of reliable 3D dynamic in vitro blood-brain barrier models using polymer hollow fiber membranes: pitfalls, progress, and future perspectives

With the increasing concern of neurodegenerative diseases, the development of new therapies and effective pharmaceuticals targeted to central nervous system (CNS) illnesses is crucial for ensuring social and economic sustainability in an ageing world. Unfortunately, many promising treatments at the initial stages of the pharmaceutical development process, that is at the in vitro screening stages, do not finally show the expected results at the clinical level due to their inability to cross the human blood-brain barrier (BBB), highlighting the inefficiency of in vitro BBB models to recapitulate the real functionality of the human BBB. In the last decades research has focused on the development of in vitro BBB models from basic 2D monolayer cultures to 3D cell co-cultures employing different system configurations. Particularly, the use of polymeric hollow fiber membranes (HFs) as scaffolds plays a key role in perfusing 3D dynamic in vitro BBB (DIV-BBB) models. Their incorporation into a perfusion bioreactor system may potentially enhance the vascularization and oxygenation of 3D cell cultures improving cell communication and the exchange of nutrients and metabolites through the microporous membranes. The quest for developing a benchmark 3D dynamic in vitro blood brain barrier model requires the critical assessment of the different aspects that limits the technology. This article will focus on identifying the advantages and main limitations of the HFs in terms of polymer materials, microscopic porous morphology, and other practical issues that play an important role to adequately mimic the physiological environment and recapitulate BBB architecture. Based on this study, we consider that future strategic advances of this technology to become fully implemented as a gold standard DIV-BBB model will require the exploration of novel polymers and/or composite materials, and the optimization of the morphology of the membranes towards thinner HFs (<50 μm) with higher porosities and surface pore sizes of 1–2 µm to facilitate the intercommunication via regulatory factors between the cell co-culture models of the BBB.This work was financially supported by projects PID2019-105827RB-I00 and PCI2018-092929 (fifth EIG-Concert Japan joint call) funded by MCIN/AEI/10.13039/501100011033

Transendothelial Movement of Adiponectin in Diabetic Vasculature

Adiponectin is one of the most abundant circulatory hormone that plays an important role on homeostasis of glucose and lipid, oxidative stress, and inflammation by enhancing insulin sensitivity. It is highly implicated to pathogenesis of metabolic syndrome. This thesis examined: (study 1) glucocorticoids effect on adiponectin flux by regulation of permeability and its mechanism involved, (study 2) impact of high glucose on transendothelial movement of adiponectin and a whole-body biodistribution to understand functional significance, (study3) influence of iron overload on endothelial permeability of adiponectin to investigate the regulatory mechanism. Findings from study 1 indicated that glucocorticoids altered tight junction profiles that led to reduce endothelial paracellular permeability and to decrease adiponectin contents in rat skeletal muscle. In study 2, the data demonstrated that hyperglycemia decreased vascular permeability and resulted in increased adiponectin transendothelial movement, which observations were tested by multifaceted vasculature platforms in vivo, ex vivo and 2D & 3D in vitro with high glucose treatment. Lastly, study 3 showed that iron overload induced oxidative stress and altered tight junction expression to elevate permeability of endothelial monolayers. This increased adiponectin movement across the endothelial barrier. In summary, my studies demonstrated that adiponectin transendothelial movement was regulated by vascular permeability. The alteration of permeability relied on expression of tight junction and its regulatory mechanism resulted from diabetic conditions

Quantitative imaging of coronary blood flow

Positron emission tomography (PET) is a nuclear medicine imaging modality based on the administration of a positron-emitting radiotracer, the imaging of the distribution and kinetics of the tracer, and the interpretation of the physiological events and their meaning with respect to health and disease. PET imaging was introduced in the 1970s and numerous advances in radiotracers and detection systems have enabled this modality to address a wide variety of clinical tasks, such as the detection of cancer, staging of Alzheimer's disease, and assessment of coronary artery disease (CAD). This review provides a description of the logic and the logistics of the processes required for PET imaging and a discussion of its use in guiding the treatment of CAD. Finally, we outline prospects and limitations of nanoparticles as agents for PET imaging

Predicting glucose level with an adapted branch predictor

Background and objective Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit. Methods We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction. Results To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%. Conclusions When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity

Peptidome and Proteome Peritoneal Dialysate Evolutionary Atlas (P3DEVOATLAS)

Peritoneal membrane (PM) failure in patients with end stage renal disease submitted to peritoneal dialysis (PD) cannot be predicted and does not occur in every patient in the same sequence and to the same extent. Moreover, long-term PD leads to morphological and functional alterations in the PM, reducing the lifespan of this dialysis up to five years, and forcing the replacement of PD by other renal replacement therapies. This represents a lower quality of life for the patients and extra cost of tens of million euros per year for the Portuguese National Health System. Peritoneal dialysis effluent (PDE) represents an underestimated biochemical window into the peritoneum and a useful reservoir of potential clinical biomarkers. Therefore, this work aims to develop longitudinal studies to unravel the evolution of the peptidome and proteome of the PDE with time, to identify specific molecular changes that can be particularly interesting for the understanding and early detection of long-term PM alterations. To achieve this goal, mass spectrometry (MS)-based methods are needed to improve PDE proteome and peptidome analysis and to overcome some drawbacks that can arise from such a complex biological sample that can hamper the proteome and peptidome coverage. For this reason, this thesis is focused also in the use of sample treatments and methodologies to reduce PDE sample complexity prior to MS analysis. Therefore, different methods of sample treatment were assessed with success as proteomics tools for getting insight into the PDE proteome and peptidome. Furthermore, this research constitutes the first proteome and peptidome-based longitudinal study of PD patient. In addition, the results represent the highest proteome and peptidome coverage ever achieved for this complex sample. Hence, this knowledge could be useful for the proteomic and clinical PD-devoted research community

Quantification of 18F-FDG PET kinetic parameters using an image-derived input function and multimodal integration with resting-state fMRI metrics

Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints.Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints