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The pig (Sus Scrofa Domestica) is an accepted model for preclinical evaluation of prosthetic heart valves and trans-catheter implantation techniques. Understanding porcine cardiac dimensions through three-dimensional computed tomography (CT), increases preclinical study success, leading to higher cost efficiency and to the observance of the obligation to the 3 R principles. Cardiac CT images of twenty-four Swiss large white pigs were segmented; aortic root, mitral valve, pulmonary trunk, tricuspid valve, as well as the aorto-mitral angle and left atrial height were analyzed. Correlation coefficient (r) was calculated in relation to body weight. In Swiss large white pigs, valvular dimensions, length of the pulmonary artery and ascending aorta as well as left atrial height correlate with body weight. Coronary ostia heights and aorto-mitral angle size can be neglected in animal size selection; no changes were found for either of the two parameters with increasing body weight., Scientific Reports, 10 (1), ISSN:2045-2322

Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information about patients. However, text data from eHRs, e.g., discharge summary notes, are challenging in their analysis because these notes are free-form texts and the writing formats and styles vary considerably between different records. For this reason, in this paper we study deep learning neural networks in combination with natural language processing to analyze text data from clinical discharge summaries. We provide a detail analysis of patient phenotyping, i.e., the automatic prediction of ten patient disorders, by investigating the influence of network architectures, sample sizes and information content of tokens. Importantly, for patients suffering from Chronic Pain, the disorder that is the most difficult one to classify, we find the largest performance gain for a combined word- and sentence-level input convolutional neural network (ws-CNN). As a general result, we find that the combination of data quality and data quantity of the text data is playing a crucial role for using more complex network architectures that improve significantly beyond a word-level input CNN model. From our investigations of learning curves and token selection mechanisms, we conclude that for such a transition one requires larger sample sizes because the amount of information per sample is quite small and only carried by few tokens and token categories. Interestingly, we found that the token frequency in the eHRs follow a Zipf law and we utilized this behavior to investigate the information content of tokens by defining a token selection mechanism. The latter addresses also issues of explainable AI.

Bone regeneration is a complex process and the clinical translation of tissue engineered constructs (TECs) remains a challenge. The combination of biomaterials and mesenchymal stem cells (MSCs) may enhance the healing process through paracrine effects. Here, we investigated the influence of cell format in combination with a collagen scaffold on key factors in bone healing process, such as mineralization, cell infiltration, vascularization, and ECM production. MSCs as single cells (2D-SCs), assembled into microtissues (3D-MTs) or their corresponding secretomes were combined with a collagen scaffold and incubated on the chicken embryo chorioallantoic membrane (CAM) for 7 days. A comprehensive quantitative analysis was performed on a cellular level by histology and by microcomputed tomography (microCT). In all experimental groups, accumulation of collagen and glycosaminoglycan within the scaffold was observed over time. A pronounced cell infiltration and vascularization from the interface to the surface region of the CAM was detected. The 3D-MT secretome showed a significant mineralization of the biomaterial using microCT compared to all other conditions. Furthermore, it revealed a homogeneous distribution pattern of mineralization deposits in contrast to the cell-based scaffolds, where mineralization was only at the surface. Therefore, the secretome of MSCs assembled into 3D-MTs may represent an interesting therapeutic strategy for a next-generation bone healing concept., Scientific Reports, 11 (1), ISSN:2045-2322

Recent research indicated the potential of cold physical plasma in cancer therapy. The plethora of plasma-derived reactive oxygen and nitrogen species (ROS/RNS) mediate diverse antitumor effects after eliciting oxidative stress in cancer cells. We aimed at exploiting this principle using a newly designed dual-jet neon plasma source (Vjet) to treat colorectal cancer cells. A treatment time-dependent ROS/RNS generation induced oxidation, growth retardation, and cell death within 3D tumor spheroids were found. In TUM-CAM, a semi in vivo model, the Vjet markedly reduced vascularized tumors' growth, but an increase of tumor cell immunogenicity or uptake by dendritic cells was not observed. By comparison, the argon-driven single jet kINPen, known to mediate anticancer effects in vitro, in vivo, and in patients, generated less ROS/RNS and terminal cell death in spheroids. In the TUM-CAM model, however, the kINPen was equivalently effective and induced a stronger expression of immunogenic cancer cell death (ICD) markers, leading to increased phagocytosis of kINPen but not Vjet plasma-treated tumor cells by dendritic cells. Moreover, the Vjet was characterized according to the requirements of the DIN-SPEC 91315. Our results highlight the plasma device-specific action on cancer cells for evaluating optimal discharges for plasma cancer treatment.

Left ventricle, LV wringing wall motion relies on physiological muscle fiber orientation, fibrotic status, and electromechanics (EM). The loss of proper EM activation can lead to rigid-body-type (RBT) LV rotation, which is associated with advanced heart failure (HF) and challenges in resynchronization. To describe the EM coupling and scar tissue burden with respect to rotational patterns observed on the LV in patients with ischemic heart failure with reduced ejection fraction (HFrEF) left bundle branch block (LBBB). Thirty patients with HFrEF/LBBB underwent EM analysis of the left ventricle using an invasive electro-mechanical catheter mapping system (NOGA XP, Biosense Webster). The following parameters were evaluated: rotation angle; rotation velocity; unipolar/bipolar voltage; local activation time, LAT; local electro-mechanical delay, LEMD; total electro-mechanical delay, TEMD. Patients underwent late-gadolinium enhancement cMRI when possible. The different LV rotation pattern served as sole parameter for patients’ grouping into two categories: wringing rotation (Group A, n = 6) and RBT rotation (Group B, n = 24). All parameters were aggregated into a nine segment, three sector and whole LV models, and compared at multiple scales. Segmental statistical analysis in Group B revealed significant inhomogeneities, across the LV, regarding voltage level, scar burdening, and LEMD changes: correlation analysis showed correspondently a loss of synchronization between electrical (LAT) and mechanical activation (TEMD). On contrary, Group A (relatively low number of patients) did not present significant differences in LEMD across LV segments, therefore electrical (LAT) and mechanical (TEMD) activation were well synchronized. Fibrosis burden was in general associated with areas of low voltage. The rotational behavior of LV in HF/LBBB patients is determined by the local alteration of EM coupling. These findings serve as a strong basic groundwork for a hypothesis that EM analysis may predict CRT response.Clinical trial registration: SUM No. KNW/0022/KB1/17/15.

In pharmacological research the development of promising lead compounds requires a detailed understanding of the dynamics of disease progression. However, for many diseases, such as kidney fibrosis, gaining such understanding requires complex real-time, multi-dimensional analysis of diseased and healthy tissue. To allow for such studies with increased throughput we established a dextran hydrogel-based in vitro 3D co-culture as a disease model for kidney fibrosis aimed at the discovery of compounds modulating the epithelial/mesenchymal crosstalk. This platform mimics a simplified pathological renal microenvironment at the interface between tubular epithelial cells and surrounding quiescent fibroblasts. We combined this 3D technology with epithelial reporter cell lines expressing fluorescent biomarkers in order to visualize pathophysiological cell state changes resulting from toxin-mediated chemical injury. Epithelial cell damage onset was robustly detected by image-based monitoring, and injured epithelial spheroids induced myofibroblast differentiation of co-cultured quiescent human fibroblasts. The presented 3D co-culture system therefore provides a unique model system for screening of novel therapeutic molecules capable to interfere and modulate the dialogue between epithelial and mesenchymal cells.

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes. publishedVersion

Low correlations of cell culture data with clinical outcomes pose major medical challenges with costly consequences. While the majority of biomaterials are tested using in vitro cell monocultures, the importance of synergistic interactions between different cell types on paracrine signalling has recently been highlighted. In this proof-of-concept study, we asked whether the first contact of surfaces with whole human blood could steer the tissue healing response. This hypothesis was tested using alkali-treatment of rough titanium (Ti) surfaces since they have clinically been shown to improve early implant integration and stability, yet blood-free in vitro cell cultures poorly correlated with in vivo tissue healing. We show that alkali-treatment, compared to native Ti surfaces, increased blood clot thickness, including platelet adhesion. Strikingly, blood clots with entrapped blood cells in synergistic interactions with fibroblasts, but not fibroblasts alone, upregulated the secretion of major factors associated with fast healing. This includes matrix metalloproteinases (MMPs) to break down extracellular matrix and the growth factor VEGF, known for its angiogenic potential. Consequently, in vitro test platforms, which consider whole blood-implant interactions, might be superior in predicting wound healing in response to biomaterial properties.

A set of algorithms is presented that facilitates the evaluation of super continuum laser absorption spectroscopy (SCLAS) measurements with respect to temperature, pressure and species concentration without the need for simultaneous background intensity measurements. For this purpose a non-linear model fitting approach is employed. A detailed discussion of the influences on the instrument function of the spectrometer and a method for the in-situ determination of the instrument function without additional hardware are given. The evaluation procedure is supplemented by a detailed measurement precision assessment by applying an error propagation through the non-linear model fitting approach. While the algorithms are tailored to SCLAS, they can be transferred to other spectroscopic methods, that similarly require an instrument function. The presented methods are validated using gas cell measurements of methane in the near infrared region at pressures up to 8.7 bar.

We have analyzed the pathway networks of ischemia-affected and remote myocardial areas after repetitive ischemia/reperfusion (r-I/R) injury without ensuing myocardial infarction (MI) to elaborate a spatial- and chronologic model of cardioprotective gene networks to prevent left ventricular (LV) adverse remodeling. Domestic pigs underwent three cycles of 10/10 min r-I/R by percutaneous intracoronary balloon inflation/deflation in the mid left anterior descending artery, without consecutive MI. Sham interventions (n = 8) served as controls. Hearts were explanted at 5 h (n = 6) and 24 h (n = 6), and transcriptomic profiling of the distal (ischemia-affected) and proximal (non-affected) anterior myocardial regions were analyzed by next generation sequencing (NGS) and post-processing with signaling pathway impact and pathway network analyses. In ischemic region, r-I/R induced early activation of Ca-, adipocytokine and insulin signaling pathways with key regulator STAT3, which was also upregulated in the remote areas together with clusterin (CLU) and TNF-alpha. During the late phase of cardioprotection, antigen immunomodulatory pathways were activated with upregulation of STAT1 and CASP3 and downregulation of neprilysin in both zones, suggesting r-I/R induced intrinsic remote conditioning. The temporo-spatially differently activated pathways revealed a global myocardial response, and neprilysin and the STAT family as key regulators of intrinsic remote conditioning for prevention of adverse remodeling.

Collective cell behaviors in migration and force generation were studied at the mesoscopic-level using cells grown in a 3D extracellular matrix (ECM) simulating tissues. Magnetic resonance imaging (MRI) was applied to investigate dynamic cell mechanics at this level. MDCK, NBT2, and MEF cells were embedded in 3D ECM, forming clusters that then migrated and generated forces affecting the ECM. The cells demonstrated MRI contrast due to iron accumulation in the clusters. Timelapse-MRI enabled the measurement of dynamic stress fields generated by the cells, as well as simultaneous monitoring of the cell distribution and ECM deformation/remodeling. We found cell clusters embedded in the 3D ECM can exert translational forces to pull and push, as well as torque, their surroundings. We also observed that the sum of forces generated by multiple cell clusters may result in macroscopic deformation. In summary, MRI can be used to image cell-ECM interactions mesoscopically.

In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.