Effects of dietary co-exposure to fungal and herbal functional feed additives on immune parameters and microbial intestinal diversity in rainbow trout (Oncorhynchus mykiss).

Misuse and overuse of antibiotics in aquaculture has proven to be an unsustainable practice leading to increased bacterial resistance. An alternative strategy involves the inclusion of immunostimulants in fish diets, especially fungal and herbal compounds already authorized for human consumption, hence without environmental or public health concerns. In this study, we used a holistic and cross-disciplinary pipeline to assess the immunostimulatory properties of two fungi: Trametes versicolor and Ganoderma lucidum; one herbal supplement, capsaicin in the form of Espelette pepper (Capsicum annuum), and a combination of these fungal and herbal additives on rainbow trout (Oncorhynchus mykiss). We investigated the impact of diet supplementation for 7 weeks on survival, growth performance, cellular, humoral, and molecular immune parameters, as well as the intestinal microbial composition of the fish. Uptake of herbal and fungal compounds influenced the expression of immune related genes, without generating an inflammatory response. Significant differences were detected in the spleen-tlr2 gene expression. Supplementation with herbal additives correlated with structural changes in the fish intestinal microbiota and enhanced overall intestinal microbial diversity. Results demonstrated that the different treatments had no adverse effect on growth performance and survival, suggesting the safety of the different feed additives at the tested concentrations. While the mechanisms and multifactorial interactions remain unclear, this study provides insights not only in regard to nutrition and safety of these compounds, but also how a combined immune and gut microbiota approach can shed light on efficacy of immunostimulant compounds for potential commercial inclusion as feed supplements.

A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora.

BACKGROUND: Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques. METHODS: In this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria. We systematically investigated four pre-trained contextual embedding models for the biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domains (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. In addition, we also investigated the feasibility of data augmentation approaches and evaluated their performance. RESULTS: Our evaluation results using tenfold cross-validation show that domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively). The data augmentation results show that it is feasible to leverage additional corpora to improve NER performance. CONCLUSIONS: Findings from this study not only demonstrate the importance of contextual embeddings trained from domain-specific corpora, but also shed lights on the benefits of leveraging multiple data sources for the challenging NER task in clinical trial eligibility criteria text.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes.

AIM: The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years. METHOD: Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers. RESULTS: CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test. CONCLUSION: CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

From ClinicalTrials.gov trial registry to an analysis-ready database of clinical trial results.

BACKGROUND: The ClinicalTrials.gov web site provides a convenient interface to look up study results, but it does not allow downloading data in a format that can be readily used for quantitative analyses. PURPOSE: To develop a system that automatically downloads study results from ClinicalTrials.gov and provides an interface to retrieve study results in a spreadsheet format ready for analysis. METHODS: Sherlock(®) identifies studies by intervention, population, or outcome of interest and in seconds creates an analytic database of study results ready for analyses. The outcome classification algorithms used in Sherlock were validated against a classification by an expert. CONCLUSIONS: Having a database ready for analysis that can be updated automatically, dramatically extends the utility of the ClinicalTrials.gov trial registry. It increases the speed of comparative research, reduces the need for manual extraction of data, and permits answering a vast array of questions.

Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data.

BACKGROUND: Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD. METHODS: We have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection. RESULTS: We have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587. CONCLUSIONS: Our results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

Plants Dictate Root Microbial Composition in Hydroponics and Aquaponics.

The role of the microbial community in mediating fish and plant co-culture is often considered the black box of aquaponics. Despite widespread recognition regarding the dependency of plants on their rhizosphere, the extent to which upstream aquaculture influences downstream hydroponic root communities has been poorly described in the literature. In this study we performed a taxonomic survey (16S rRNA metabarcoding) of microbial communities originating in the facility water source, hydroponic nutrient solution (HNS) sump, nutrient supplemented biofilter effluent (BF) sump, and recirculating aquaculture system tanks stocked with Nile tilapia (Oreochromis niloticus). Lettuce (Lactuca sativa) was then grown using the HNS and BF effluent under sterilized or mature (prior aquaponics/hydroponics lettuce culture water) conditions, likewise, the influence of probiotic addition or inoculation with soil-grown lettuce rhizosphere was assessed. Compositional similarities across treatments suggest that under soil-less conditions, plants are able to exert a stronger discriminatory influence on their rhizosphere composition than is done by colonization from upstream sources. Furthermore, cluster dendrograms grouped the sterilized and unsterilized treatments more consistently together than hydroponics and aquaponics treatments. These findings contradict conventional beliefs that microbial communities in the water column colonize roots based on their presence alone, ignoring the role that plants play in rhizosphere community selection.

Ecosystem-specific microbiota and microbiome databases in the era of big data.

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge.

Efficient substructure searching is a key requirement for any chemical information management system. In this paper, we describe the substructure search capabilities of ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. The solution consists of several algorithmic components: 1) a pattern mapping algorithm for solving the subgraph isomorphism problem, 2) an indexing scheme that enables very fast substructure searches on large structure files, 3) the incorporation of that indexing scheme into an Oracle cartridge to enable querying large relational databases through SQL, and 4) a cost estimation scheme that allows the Oracle cost-based optimizer to generate a good execution plan when a substructure search is combined with additional constraints in a single SQL query. The algorithm was tested on a public database comprising nearly 1 million molecules using 4,629 substructure queries, the vast majority of which were submitted by discovery scientists over the last 2.5 years of user acceptance testing of ABCD. 80.7% of these queries were completed in less than a second and 96.8% in less than ten seconds on a single CPU, while on eight processing cores these numbers increased to 93.2% and 99.7%, respectively. The slower queries involved extremely generic patterns that returned the entire database as screening hits and required extensive atom-by-atom verification.

Improving Plant Health Through Nutrient Remineralization in Aquaponic Systems.

The exploitation of readily bioavailable fish excreta as a source of plant nutrients lies at the cornerstone of aquaponics farming. Research on nutrient cycling in aquaponic systems has devoted considerable attention to the plant uptake of dissolved nutrients in fish excreta, however, the integration of particulate-bound nutrients into downstream hydroponic farming has remained elusive. The high amount of organic carbon present in fish sludge may lead to biofouling if directly incorporated into hydroponic circulation systems, reducing the utility of incorporating fish solids on a large scale. In this study, we implemented a novel treatment system capable of reducing the carbon and nitrogen load of fish solids to produce a liquid fertilizer for a downstream hydroponics unit. Lettuce (Lactuca sativa) fertilized with exclusively a commercial nutrient solution, the biofilter effluent (coupled aquaponic system), effluent from the solids treatment system, or the latter two combined were grown in nutrient flow technique gutters downstream of a recirculating aquaculture system stocked with rainbow trout (Oncorhynchus mykiss). While crop yields were lower for the aquaponic treatments compared to lettuce grown in a commercial nutrient solution, plant sap analysis demonstrated a contrasting picture with respect to internal nutrient concentrations. Lettuce grown in the commercial hydroponic solution were deficient in several mineral nutrients (Mg, Ca, Na, and Si) nor did they have higher iron concentrations despite the significantly higher EDTA-chelated aqueous iron (460 × greater than other treatments) in the nutrient solution. Nutrient uptake in the rhizosphere was not investigated on a molecular level, although stunted rhizosphere growth in the commercial nutrient solution control suggests a weakened capacity for nutrient uptake in comparison to other treatments. Alongside the remineralization of micronutrients, the solids treatment system addressed the common issue of excess carbon leading to biofouling via a total suspended solids reduction of 87.27% ± 9.95 during the coupled aquaponics cultivation period. Ultimately, these data lead to two important conclusions. Firstly, optimizing nutrient bioavailability is not synonymous to increasing the presence of a nutrient in the water column. Secondly, estimating ideal nutrient solution concentrations involves both preventing nutrient blocking and improving bioavailability.

Xcellerate Investigator Portal: A New Web-Based Tool for Online Delivery of Central Laboratory Data, Reports, and Communications to Clinical Sites.

Covance Drug Development produces more than 55 million test results via its central laboratory services, requiring the delivery of more than 10 million reports annually to investigators at 35,000 sites in 89 countries. Historically, most of these data were delivered via fax or electronic data transfers in delimited text or SAS transport file format. Here, we present a new web portal that allows secure online delivery of laboratory results, reports, manuals, and training materials, and enables collaboration with investigational sites through alerts, announcements, and communications. By leveraging a three-tier architecture composed of preexisting data warehouses augmented with an application-specific relational database to store configuration data and materialized views for performance optimizations, a RESTful web application programming interface (API), and a browser-based single-page application for user access, the system offers greatly improved capabilities and user experience without requiring any changes to the underlying acquisition systems and data stores. Following a 3-month controlled rollout with 6,500 users at early-adopter sites, the Xcellerate Investigator Portal was deployed to all 240,000 of Covance's Central Laboratory Services' existing users, gaining widespread acceptance and pointing to significant benefits in productivity, convenience, and user experience.

Enhanced SAR maps: expanding the data rendering capabilities of a popular medicinal chemistry tool.

We recently introduced SAR maps, a new interactive method for visualizing structure-activity relationships targeted specifically at medicinal chemists. A SAR map renders an R-group decomposition of a congeneric series as a rectangular matrix of cells, each representing a unique combination of R-groups color-coded by a user-selected property of the corresponding compound. In this paper, we describe an enhanced version that greatly expands the types of visualizations that can be displayed inside the cells. Examples include multidimensional histograms and pie charts that visualize the biological profiles of compounds across an entire panel of assays, forms that display specific fields on user-defined layouts, aligned 3D structure drawings that show the relative orientation of different substituents, dose-response curves, images of crystals or diffraction patterns, and many others. These enhancements, which capitalize on the modular architecture of its host application Third Dimension Explorer (3DX), allow the medicinal chemist to interactively analyze complex scaffolds with multiple substitution sites, correlate substituent structure and biological activity at multiple simultaneous dimensions, identify missing analogs or screening data, and produce information-dense visualizations for presentations and publications. The new tool has an intuitive user interface that makes it appealing to experts and nonexperts alike.

A late-binding, distributed, NoSQL warehouse for integrating patient data from clinical trials.

Clinical trial data are typically collected through multiple systems developed by different vendors using different technologies and data standards. That data need to be integrated, standardized and transformed for a variety of monitoring and reporting purposes. The need to process large volumes of often inconsistent data in the presence of ever-changing requirements poses a significant technical challenge. As part of a comprehensive clinical data repository, we have developed a data warehouse that integrates patient data from any source, standardizes it and makes it accessible to study teams in a timely manner to support a wide range of analytic tasks for both in-flight and completed studies. Our solution combines Apache HBase, a NoSQL column store, Apache Phoenix, a massively parallel relational query engine and a user-friendly interface to facilitate efficient loading of large volumes of data under incomplete or ambiguous specifications, utilizing an extract-load-transform design pattern that defers data mapping until query time. This approach allows us to maintain a single copy of the data and transform it dynamically into any desirable format without requiring additional storage. Changes to the mapping specifications can be easily introduced and multiple representations of the data can be made available concurrently. Further, by versioning the data and the transformations separately, we can apply historical maps to current data or current maps to historical data, which simplifies the maintenance of data cuts and facilitates interim analyses for adaptive trials. The result is a highly scalable, secure and redundant solution that combines the flexibility of a NoSQL store with the robustness of a relational query engine to support a broad range of applications, including clinical data management, medical review, risk-based monitoring, safety signal detection, post hoc analysis of completed studies and many others.

A dimensional warehouse for integrating operational data from clinical trials.

Timely, consistent and integrated access to clinical trial data remains one of the pharmaceutical industry's most pressing needs. As part of a comprehensive clinical data repository, we have developed a data warehouse that can integrate operational data from any source, conform it to a canonical data model and make it accessible to study teams in a timely, secure and contextualized manner to support operational oversight, proactive risk management and other analytic and reporting needs. Our solution consists of a dimensional relational data warehouse, a set of extraction, transformation and loading processes to coordinate data ingestion and mapping, a generalizable metrics engine to enable the computation of operational metrics and key performance, quality and risk indicators and a set of graphical user interfaces to facilitate configuration, management and administration. When combined with the appropriate data visualization tools, the warehouse enables convenient access to raw operational data and derived metrics to help track study conduct and performance, identify and mitigate risks, monitor and improve operational processes, manage resource allocation, strengthen investigator and sponsor relationships and other purposes.

A new risk and issue management system to improve productivity, quality, and compliance in clinical trials.

OBJECTIVE: We present a new system to track, manage, and report on all risks and issues encountered during a clinical trial. MATERIALS AND METHODS: Our solution utilizes JIRA, a popular issue and project tracking tool for software development, augmented by third-party and custom-built plugins to provide the additional functionality missing from the core product. RESULTS: The new system integrates all issue types under a single tracking tool and offers a range of capabilities, including configurable issue management workflows, seamless integration with other clinical systems, extensive history, reporting, and trending, and an intuitive web interface. DISCUSSION AND CONCLUSION: By preserving the linkage between risks, issues, actions, decisions, and outcomes, the system allows study teams to assess the impact and effectiveness of their risk management strategies and present a coherent account of how the trial was conducted. Since the tool was put in production, we have observed an increase in the number of reported issues and a decrease in the median issue resolution time which, along with the positive user feedback, point to marked improvements in quality, transparency, productivity, and teamwork.

Adsorption of nitrogen-containing compounds on hydroxylated α-quartz surfaces.

Adsorption energies of various nitrogen-containing compounds (specifically, 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), 2,4-dinitroanisole (DNAn), and 3-nitro-1,2,4-triazole-5-one (NTO)) on the hydroxylated (001) and (100) α-quartz surfaces are computed. Different density functionals are utilized and both periodic as well as cluster approaches are applied. From the adsorption energies, partition coefficients on the considered α-quartz surfaces are derived. While TNT and DNT are preferably adsorbed on the (001) surface of α-quartz, NTO is rather located on both α-quartz surfaces.

A cross-source, system-agnostic solution for clinical data review.

Assembly of complete and error-free clinical trial data sets for statistical analysis and regulatory submission requires extensive effort and communication among investigational sites, central laboratories, pharmaceutical sponsors, contract research organizations and other entities. Traditionally, this data is captured, cleaned and reconciled through multiple disjointed systems and processes, which is resource intensive and error prone. Here, we introduce a new system for clinical data review that helps data managers identify missing, erroneous and inconsistent data and manage queries in a unified, system-agnostic and efficient way. Our solution enables timely and integrated access to all study data regardless of source, facilitates the review of validation and discrepancy checks and the management of the resulting queries, tracks the status of page review, verification and locking activities, monitors subject data cleanliness and readiness for database lock and provides extensive configuration options to meet any study's needs, automation for regular updates and fit-for-purpose user interfaces for global oversight and problem detection.

Benefits of micronized purified flavonoid fraction as adjuvant therapy on inflammatory response after sclerotherapy.

BACKGROUND: Sclerotherapy is the treatment of choice for telangiectasias. The aim of this study was to investigate the systemic and local inflammatory response after sclerotherapy in women with chronic venous disease (CVD) and to assess the effects of micronized purified flavonoid fraction (MPFF) in combination with sclerotherapy on markers of inflammation and endothelial dysfunction. METHODS: Sixty women with primary CVD CEAP class C1 were randomly assigned sclerotherapy in combination with MPFF treatment (N=30) or sclerotherapy alone (control) group (N.=30). In the treatment group, patients received MPFF tablets 1000 mg daily for 2 weeks before the scheduled sclerotherapy and for at least 2 months after the procedure. Microsclerotherapy was performed according to standard protocol using modern sclerosing agents. Blood samples were collected from the central vein of the target vascular cluster before and 10 days after sclerotherapy to evaluate markers of inflammation and endothelial dysfunction including: C-reactive protein within the high-sensitivity range, histamine, interleukin-1, tumor necrosis factor-alpha, and vascular endothelial growth factor. To measure the systemic inflammatory response, blood samples were also collected from the forearm vein in 15 control patients. RESULTS: Baseline levels of markers of inflammation and endothelial dysfunction obtained from the central vein of the treatment site showed no statistically significant differences between treatment groups. In both groups, all markers were significantly increased 10 days after sclerotherapy. In the control group, the same markers obtained from the forearm were not modified 10 days after sclerotherapy indicating the inflammatory reaction was local and not systemic. MPFF treatment reduced all markers of inflammation and endothelial dysfunction compared with the control group. CONCLUSIONS: Prescription of MPFF starting prior to sclerotherapy and throughout the post-operative period reduced the vein-specific pro-inflammatory reaction and may thereby reduce the unwanted side effects of sclerotherapy and improve treatment outcomes.

Risk-based Monitoring of Clinical Trials: An Integrative Approach.

PURPOSE: Clinical trial monitoring is an essential component of drug development aimed at safeguarding subject safety, data quality, and protocol compliance by focusing sponsor oversight on the most important aspects of study conduct. In recent years, regulatory agencies, industry consortia, and nonprofit collaborations between industry and regulators, such as TransCelerate and International Committee for Harmonization, have been advocating a new, risk-based approach to monitoring clinical trials that places increased emphasis on critical data and processes and encourages greater use of centralized monitoring. However, how best to implement risk-based monitoring (RBM) remains unclear and subject to wide variations in tools and methodologies. The nonprescriptive nature of the regulatory guidelines, coupled with limitations in software technology, challenges in operationalization, and lack of robust evidence of superior outcomes, have hindered its widespread adoption. METHODS: We describe a holistic solution that combines convenient access to data, advanced analytics, and seamless integration with established technology infrastructure to enable comprehensive assessment and mitigation of risk at the study, site, and subject level. FINDINGS: Using data from completed RBM studies carried out in the last 4 years, we demonstrate that our implementation of RBM improves the efficiency and effectiveness of the clinical oversight process as measured on various quality, timeline, and cost dimensions. IMPLICATIONS: These results provide strong evidence that our RBM methodology can significantly improve the clinical oversight process and do so at a lower cost through more intelligent deployment of monitoring resources to the sites that need the most attention.

SAR maps: a new SAR visualization technique for medicinal chemists.

We present structure-activity relationship (SAR) maps, a new, intuitive method for visualizing SARs targeted specifically at medicinal chemists. The method renders an R-group decomposition of a chemical series as a rectangular matrix of cells, each representing a unique combination of R-groups and thus a unique compound. Color-coding the cells by chemical property or biological activity allows patterns to be easily identified and exploited. SAR maps allow the medicinal chemist to interactively analyze complicated datasets with multiple R-group dimensions, rapidly correlate substituent structure and biological activity, assess additivity of substituent effects, identify missing analogs and screening data, and create compelling graphical representations for presentation and publication. We believe that this method fills a long-standing gap in the medicinal chemist's toolset for understanding and rationalizing SAR.

Combinatorial informatics in the post-genomics ERA.

The multitude of potential drug targets emerging from genome sequencing demands new approaches to drug discovery. A chemogenomics strategy, which involves the generation of small-molecule compounds that can be used both as tools to probe biological mechanisms and as leads for drug-property optimization, provides a highly parallel, industrialized solution. Key to the success of this strategy is an integrated suite of chemi-informatics applications that can allow the rapid and directed optimization of chemical compounds with drug-like properties using 'just-in-time' combinatorial chemical synthesis. An effective embodiment of this process requires new computational and data-mining tools that cover all aspects of library generation, compound selection and experimental design, and work effectively on a massive scale.