Automated Clinical Trial Cohort Definition and Evaluation with CQL and CDS-Hooks.

BACKGROUND: Patient recruitment for clinical trials faces major challenges with current methods being costly and often requiring time-consuming acquisition of medical histories and manual matching of potential subjects. OBJECTIVES: Designing and implementing an Electronic Health Record (EHR) and domain-independent automation architecture using Clinical Decision Support (CDS) standards that allows researchers to effortlessly enter standardized trial criteria to retrieve eligibility statistics and integration into a clinician workflow to automatically trigger evaluation without added clinician workload. METHODS: Cohort criteria are translated into the Clinical Quality Language (CQL) and integrated into Measures and CDS-Hooks for patient- and population-level evaluation. RESULTS: Successful application of simplified real-world trial criteria to Fast Healthcare Interoperability Resources (FHIR®) test data shows the feasibility of obtaining individual patient eligibility and trial details as well as population eligibility statistics and a list of qualifying patients. CONCLUSION: Employing CDS standards for automating cohort definition and evaluation shows promise in streamlining patient selection, aligning with increasing legislative demands for standardized healthcare data.

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users' trust in computer-assisted image classification.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (µMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 µMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

From Authoring to Evaluating an Electronic Health Quality Measure - Applying Logic to FHIR® with CQL for Calculating Immunization Coverage.

BACKGROUND: Current monitoring and evaluation methods challenge the healthcare system. Specifically for the use case of immunization coverage calculation, person-level data retrieval is required instead of inaccurate aggregation methods. The Clinical Quality Language (CQL) by HL7®, has the potential to overcome current challenges by offering an automated generation of quality reports on top of an HL7® FHIR® repository. OBJECTIVES: This paper provides a method to author and evaluate an electronic health quality measure as demonstrated by a proof-of-concept on immunization coverage calculation. METHODS: Five artifact types were identified to transform unstructured input into CQL, to define the terminology, to create test data, and to evaluate the new quality measures. RESULTS: CQL logic and FHIR® test data were created and evaluated by using the different approaches of manual evaluation, unit testing in the HAPI FHIR project, as well as showcasing the functionality with a developed user interface for immunization coverage analysis. CONCLUSION: Simple, powerful, and transparent evaluations on a small population can be achieved with existing open-source tools, by applying CQL logic to FHIR®.

Purification Analysis, Intracellular Tracking, and Colocalization of Extracellular Vesicles Using Atomic Force and 3D Single-Molecule Localization Microscopy.

Extracellular vesicles (EVs) play a key role in cell-cell communication and thus have great potential to be utilized as therapeutic agents and diagnostic tools. In this study, we implemented single-molecule microscopy techniques as a toolbox for a comprehensive characterization as well as measurement of the cellular uptake of HEK293T cell-derived EVs (eGFP-labeled) in HeLa cells. A combination of fluorescence and atomic force microscopy revealed a fraction of 68% fluorescently labeled EVs with an average size of ∼45 nm. Two-color single-molecule fluorescence microscopy analysis elucidated the 3D dynamics of EVs entering HeLa cells. 3D colocalization analysis of two-color direct stochastic optical reconstruction microscopy (dSTORM) images revealed that 25% of EVs that experienced uptake colocalized with transferrin, which has been linked to early recycling of endosomes and clathrin-mediated endocytosis. The localization analysis was combined with stepwise photobleaching, providing a comparison of protein aggregation outside and inside the cells.

The Class IIA Histone Deacetylase (HDAC) Inhibitor TMP269 Downregulates Ribosomal Proteins and Has Anti-Proliferative and Pro-Apoptotic Effects on AML Cells.

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by altered myeloid progenitor cell proliferation and differentiation. As in many other cancers, epigenetic transcriptional repressors such as histone deacetylases (HDACs) are dysregulated in AML. Here, we investigated (1) HDAC gene expression in AML patients and in different AML cell lines and (2) the effect of treating AML cells with the specific class IIA HDAC inhibitor TMP269, by applying proteomic and comparative bioinformatic analyses. We also analyzed cell proliferation, apoptosis, and the cell-killing capacities of TMP269 in combination with venetoclax compared to azacitidine plus venetoclax, by flow cytometry. Our results demonstrate significantly overexpressed class I and class II HDAC genes in AML patients, a phenotype which is conserved in AML cell lines. In AML MOLM-13 cells, TMP269 treatment downregulated a set of ribosomal proteins which are overexpressed in AML patients at the transcriptional level. TMP269 showed anti-proliferative effects and induced additive apoptotic effects in combination with venetoclax. We conclude that TMP269 exerts anti-leukemic activity when combined with venetoclax and has potential as a therapeutic drug in AML.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search.

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data.

BACKGROUND: Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. RESULTS: ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. CONCLUSIONS: IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/ .

Prospective Tracking of Donor-Reactive T-Cell Clones in the Circulation and Rejecting Human Kidney Allografts.

Background: Antigen recognition of allo-peptides and HLA molecules leads to the activation of donor-reactive T-cells following transplantation, potentially causing T-cell-mediated rejection (TCMR). Sequencing of the T-cell receptor (TCR) repertoire can be used to track the donor-reactive repertoire in blood and tissue of patients after kidney transplantation. Methods/Design: In this prospective cohort study, 117 non-sensitized kidney transplant recipients with anti-CD25 induction were included. Peripheral mononuclear cells (PBMCs) were sampled pre-transplant and at the time of protocol or indication biopsies together with graft tissue. Next-generation sequencing (NGS) of the CDR3 region of the TCRbeta chain was performed after donor stimulation in mixed lymphocyte reactions to define the donor-reactive TCR repertoire. Blood and tissue of six patients experiencing a TCMR and six patients without rejection on protocol biopsies were interrogated for these TCRs. To elucidate common features of T-cell clonotypes, a network analysis of the TCR repertoires was performed. Results: After transplantation, the frequency of circulating donor-reactive CD4 T-cells increased significantly from 0.86 ± 0.40% to 2.06 ± 0.40% of all CD4 cells (p < 0.001, mean dif.: -1.197, CI: -1.802, -0.593). The number of circulating donor-reactive CD4 clonotypes increased from 0.72 ± 0.33% to 1.89 ± 0.33% (p < 0.001, mean dif.: -1.168, CI: -1.724, -0.612). No difference in the percentage of donor-reactive T-cells in the circulation at transplant biopsy was found between subjects experiencing a TCMR and the control group [p = 0.64 (CD4+), p = 0.52 (CD8+)]. Graft-infiltrating T-cells showed an up to six-fold increase of donor-reactive T-cell clonotypes compared to the blood at the same time (3.7 vs. 0.6% and 2.4 vs. 1.5%), but the infiltrating TCR repertoire was not reflected by the composition of the circulating TCR repertoire despite some overlap. Network analysis showed a distinct segregation of the donor-reactive repertoire with higher modularity than the overall TCR repertoire in the blood. These findings indicate an unchoreographed process of diverse T-cell clones directed against numerous non-self antigens found in the allograft. Conclusion: Donor-reactive T-cells are enriched in the kidney allograft during a TCMR episode, and dominant tissue clones are also found in the blood. Trial Registration: Clinicaltrials.gov: NCT: 03422224 (https://clinicaltrials.gov/ct2/show/NCT03422224).

MS Annika: A New Cross-Linking Search Engine.

Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.

MS Amanda 2.0: Advancements in the standalone implementation.

RATIONALE: Database search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements. METHODS: In 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high-resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast-growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged. RESULTS: We present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3-5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types. CONCLUSIONS: MS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.

Laser-facilitated epicutaneous immunotherapy with hypoallergenic beta-glucan neoglycoconjugates suppresses lung inflammation and avoids local side effects in a mouse model of allergic asthma.

BACKGROUND: Allergen-specific immunotherapy via the skin targets a tissue rich in antigen-presenting cells, but can be associated with local and systemic side effects. Allergen-polysaccharide neoglycogonjugates increase immunization efficacy by targeting and activating dendritic cells via C-type lectin receptors and reduce side effects. OBJECTIVE: We investigated the immunogenicity, allergenicity, and therapeutic efficacy of laminarin-ovalbumin neoglycoconjugates (LamOVA). METHODS: The biological activity of LamOVA was characterized in vitro using bone marrow-derived dendritic cells. Immunogenicity and therapeutic efficacy were analyzed in BALB/c mice. Epicutaneous immunotherapy (EPIT) was performed using fractional infrared laser ablation to generate micropores in the skin, and the effects of LamOVA on blocking IgG, IgE, cellular composition of BAL, lung, and spleen, lung function, and T-cell polarization were assessed. RESULTS: Conjugation of laminarin to ovalbumin reduced its IgE binding capacity fivefold and increased its immunogenicity threefold in terms of IgG generation. EPIT with LamOVA induced significantly higher IgG levels than OVA, matching the levels induced by s.c. injection of OVA/alum (SCIT). EPIT was equally effective as SCIT in terms of blocking IgG induction and suppression of lung inflammation and airway hyperresponsiveness, but SCIT was associated with higher levels of therapy-induced IgE and TH2 cytokines. EPIT with LamOVA induced significantly lower local skin reactions during therapy compared to unconjugated OVA. CONCLUSION: Conjugation of ovalbumin to laminarin increased its immunogenicity while at the same time reducing local side effects. LamOVA EPIT via laser-generated micropores is safe and equally effective compared to SCIT with alum, without the need for adjuvant.

Nutrients, bioactive compounds, and minerals in the juices of 16 varieties of apple (Malus domestica) harvested in Austria: A four-year study investigating putative correlations with weather conditions during ripening.

This study was conducted to examine putative correlations between weather parameters during April-September and the amounts of nutrients, minerals and bioactive compounds in the juices of 16 apple varieties from four harvest years in Lower Austria. For most sugar-parameters, negative correlations were found with the total precipitation (r between -0.42 and -0.64). Conversely, positive correlations were observed with the mean air temperature (r between 0.32 and 0.66), the global radiation (r between 0.32 and 0.61) and the number of tropical days (r between 0.39 and 0.51). The sum of 14 polyphenols (HPLC quantitation) was positively correlated with the mean air temperature and global radiation (rs 0.44 and 0.42). Negative correlations were observed between the global radiation and potassium, magnesium and calcium contents (correlation coefficients -0.49, -0.68 and -0.69). We conclude that increased temperatures and global radiation can be correlated with enhanced sugar synthesis and polyphenol formation.

Next generation sequencing based assessment of the alloreactive T cell receptor repertoire in kidney transplant patients during rejection: a prospective cohort study.

BACKGROUND: Kidney transplantation is the optimal treatment in end stage renal disease but the allograft survival is still hampered by immune reactions against the allograft. This process is driven by the recognition of allogenic antigens presented to T-cells and their unique T-cell receptor (TCR) via the major histocompatibility complex (MHC), which triggers a complex immune response potentially leading to graft injury. Although the immune system and kidney transplantation have been studied extensively, the subtlety of alloreactive immune responses has impeded sensitive detection at an early stage. Next generation sequencing of the TCR enables us to monitor alloreactive T-cell populations and might thus allow the detection of early rejection events. METHODS/DESIGN: This is a prospective cohort study designed to sequentially evaluate the alloreactive T cell repertoire after kidney transplantation. The TCR repertoire of patients who developed biopsy confirmed acute T cell mediated rejection (TCMR) will be compared to patients without rejection. To track the alloreactive subsets we will perform a mixed lymphocyte reaction between kidney donor and recipient before transplantation and define the alloreactive TCR repertoire by next generation sequencing of the complementary determining region 3 (CDR3) of the T cell receptor beta chain. After initial clonotype assembly from sequencing reads, TCR repertoire diversity and clonal expansion of T cells of kidney transplant recipients in periphery and kidney biopsy will be analyzed for changes after transplantation, during, prior or after a rejection. The goal of this study is to describe changes of overall T cell repertoire diversity, clonality in kidney transplant recipients, define and track alloreactive T cells in the posttransplant course and decipher patterns of expanded alloreactive T cells in acute cellular rejection to find an alternative monitoring to invasive and delayed diagnostic procedures. DISCUSSION: Changes of the T cell repertoire and tracking of alloreactive T cell clones after combined bone marrow and kidney transplant has proven to be of potential use to monitor the donor directed alloresponse. The dynamics of the donor specific T cells in regular kidney transplant recipients in rejection still rests elusive and can give further insights in human alloresponse. TRIAL REGISTRATION: Clinicaltrials.gov: NCT03422224 , registered February 5th 2018.

Hypolipidemic effects of herbal extracts by reduction of adipocyte differentiation, intracellular neutral lipid content, lipolysis, fatty acid exchange and lipid droplet motility.

An increase in adipose tissue is caused by the increased size and number of adipocytes. Lipids accumulate in intracellular stores, known as lipid droplets (LDs). Recent studies suggest that parameters such as LD size, shape and dynamics are closely related to the development of obesity. Berberine (BBR), a natural plant alkaloid, has been demonstrated to possess anti-obesity effects. However, it remains unknown which cellular processes are affected by this compound or how effective herbal extracts containing BBR and other alkaloids actually are. For this study, we used extracts of Coptis chinensis, Mahonia aquifolium, Berberis vulgaris and Chelidonium majus containing BBR and other alkaloids and studied various processes related to adipocyte functionality. The presence of extracts resulted in reduced adipocyte differentiation, as well as neutral lipid content and rate of lipolysis. We observed that the intracellular fatty acid exchange was reduced in different LD size fractions upon treatment with BBR and Coptis chinensis. In addition, LD motility was decreased upon incubation with BBR, Coptis chinensis and Chelidonium majus extracts. Furthermore, Chelidonium majus was identified as a potent fatty acid uptake inhibitor. This is the first study that demonstrates the selected regulatory effects of herbal extracts on adipocyte function.

Limits on Supernova-Associated

We searched for the presence of ^{26}Al in deep-sea sediments as a signature of supernova influx. Our data show an exponential dependence of ^{26}Al with the sample age that is fully compatible with radioactive decay of terrigenic ^{26}Al. The same set of samples demonstrated a clear supernova ^{60}Fe signal between 1.7 and 3.2 Myr ago. Combining our ^{26}Al data with the recently reported ^{60}Fe data results in a lower limit of 0.18_{-0.08}^{+0.15} for the local interstellar ^{60}Fe/^{26}Al isotope ratio. It compares to most of the ratios deduced from nucleosynthesis models and is within the range of the observed average galactic ^{60}Fe/^{26}Al flux ratio of (0.15±0.05).

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction.

Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38-64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.

PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search.

Standard proteomics workflows use tandem mass spectrometry followed by sequence database search to analyze complex biological samples. The identification of proteins carrying post-translational modifications, for example, phosphorylation, is typically addressed by allowing variable modifications in the searched sequences. Accounting for these variations exponentially increases the combinatorial space in the database, which leads to increased processing times and more false positive identifications. The here-presented tool PhoStar identifies spectra that originate from phosphorylated peptides before database search using a supervised machine learning approach. The model for the prediction of phosphorylation was trained and validated with an accuracy of 97.6% on a large set of high-confidence spectra collected from publicly available experimental data. Its power was further validated by predicting phosphorylation in the complete NIST human and mouse high collision-dissociation spectral libraries, achieving an accuracy of 98.2 and 97.9%, respectively. We demonstrate the application of PhoStar by using it for spectra filtering before database search. In database search of HeLa samples the peptide search space was reduced by 27-66% while finding at least 97% of total peptide identifications (at 1% FDR) compared with a standard workflow.

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.