Quantification of the Temporal Evolution of Collagen Orientation in Mechanically Conditioned Engineered Cardiovascular Tissues

Load-bearing soft tissues predominantly consist of collagen and exhibit anisotropic, non-linear visco-elastic behavior, coupled to the organization of the collagen fibers. Mimicking native mechanical behavior forms a major goal in cardiovascular tissue engineering. Engineered tissues often lack properly organized collagen and consequently do not meet in vivo mechanical demands. To improve collagen architecture and mechanical properties, mechanical stimulation of the tissue during in vitro tissue growth is crucial. This study describes the evolution of collagen fiber orientation with culture time in engineered tissue constructs in response to mechanical loading. To achieve this, a novel technique for the quantification of collagen fiber orientation is used, based on 3D vital imaging using multiphoton microscopy combined with image analysis. The engineered tissue constructs consisted of cell-seeded biodegradable rectangular scaffolds, which were either constrained or intermittently strained in longitudinal direction. Collagen fiber orientation analyses revealed that mechanical loading induced collagen alignment. The alignment shifted from oblique at the surface of the construct towards parallel to the straining direction in deeper tissue layers. Most importantly, intermittent straining improved and accelerated the alignment of the collagen fibers, as compared to constraining the constructs. Both the method and the results are relevant to create and monitor load-bearing tissues with an organized anisotropic collagen network

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Effect of Strain Magnitude on the Tissue Properties of Engineered Cardiovascular Constructs

Mechanical loading is a powerful regulator of tissue properties in engineered cardiovascular tissues. To ultimately regulate the biochemical processes, it is essential to quantify the effect of mechanical loading on the properties of engineered cardiovascular constructs. In this study the Flexercell FX-4000T (Flexcell Int. Corp., USA) straining system was modified to simultaneously apply various strain magnitudes to individual samples during one experiment. In addition, porous polyglycolic acid (PGA) scaffolds, coated with poly-4-hydroxybutyrate (P4HB), were partially embedded in a silicone layer to allow long-term uniaxial cyclic mechanical straining of cardiovascular engineered constructs. The constructs were subjected to two different strain magnitudes and showed differences in biochemical properties, mechanical properties and organization of the microstructure compared to the unstrained constructs. The results suggest that when the tissues are exposed to prolonged mechanical stimulation, the production of collagen with a higher fraction of crosslinks is induced. However, straining with a large strain magnitude resulted in a negative effect on the mechanical properties of the tissue. In addition, dynamic straining induced a different alignment of cells and collagen in the superficial layers compared to the deeper layers of the construct. The presented model system can be used to systematically optimize culture protocols for engineered cardiovascular tissues

The mechanisms of pharmacokinetic food-drug interactions - A perspective from the UNGAP group

The simultaneous intake of food and drugs can have a strong impact on drug release, absorption, distribution, metabolism and/or elimination and consequently, on the efficacy and safety of pharmacotherapy. As such, food-drug interactions are one of the main challenges in oral drug administration. Whereas pharmacokinetic (PK) food-drug interactions can have a variety of causes, pharmacodynamic (PD) food-drug interactions occur due to specific pharmacological interactions between a drug and particular drinks or food. In recent years, extensive efforts were made to elucidate the mechanisms that drive pharmacokinetic food-drug interactions. Their occurrence depends mainly on the properties of the drug substance, the formulation and a multitude of physiological factors. Every intake of food or drink changes the physiological conditions in the human gastrointestinal tract. Therefore, a precise understanding of how different foods and drinks affect the processes of drug absorption, distribution, metabolism and/or elimination as well as formulation performance is important in order to be able to predict and avoid such interactions. Furthermore, it must be considered that beverages such as milk, grapefruit juice and alcohol can also lead to specific food-drug interactions. In this regard, the growing use of food supplements and functional food requires urgent attention in oral pharmacotherapy. Recently, a new consortium in Understanding Gastrointestinal Absorption-related Processes (UNGAP) was established through COST, a funding organisation of the European Union supporting translational research across Europe. In this review of the UNGAP Working group "Food-Drug Interface", the different mechanisms that can lead to pharmacokinetic food-drug interactions are discussed and summarised from different expert perspectives

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Intestinal disposition of quercetin and its phase-II metabolites after oral administration in healthy volunteers

OBJECTIVES: Quercetin is one of the main dietary flavonoids and undergoes a substantial intestinal phase-II metabolism. Quercetin conjugates have been detected in plasma and in urine, but their presence in the small intestine has not been assessed. This study aimed to investigate the intestinal metabolism and metabolite excretion of quercetin by the human small intestinal wall after oral dosing. METHODS: Six healthy volunteers were given a capsule of 500 mg of quercetin with 240 ml of water. Duodenal fluids were collected using the intraluminal sampling technique for 4 h and analysed by LC-MS/MS. KEY FINDINGS: Phase-II metabolites of quercetin were detected and quantified in aspirated intestinal fluids. Metabolites appeared almost immediately after administration, indicating an intestinal metabolism and apical excretion into the lumen. Quercetin-3'-O-glucuronide was found to be the main intestinal metabolite. Our results could not conclude on the enterohepatic recycling of quercetin or its metabolites, although several individual profiles showed distinctive peaks. CONCLUSIONS: This study highlights the intestinal metabolism and excretion of quercetin and its conjugates in humans and gives insights into the relevant concentrations which should be used to investigate potential food-drug interactions in vitro.status: publishe

Etude par spectroscopie Raman de la décomplexation photoinduite du matériau GaAs hydrogéné

International audienc

Synthesis of γ-Bi2MoO6 catalyst studied by combined high-resolution powder diffraction, XANES and Raman spectroscopy

International audienc