The Effect of PGF2α on the Expression of Sodium Dependent Vitamin C Transporters in Early vs. Mid-Cycle Corpora Lutea of Sheep

The corpus luteum (CL) is an ovarian structure responsible for progesterone secretion and the maintenance of early pregnancy. In the absence of pregnancy, the CL regresses in response to uterine prostaglandin PGF2α. Vitamin C is an antioxidant that scavenges free radicals generated during normal metabolic functions. Sodium dependent vitamin C transporters (SVCT) are responsible for maintaining high concentrations of vitamin C within the cell. Release of vitamin C from the CL is one of the first events to occur prior to luteolysis (regression). Exogenous PGF2 causes a transient (less than 24h) depletion of vitamin C from CL in the early luteal phase (day 1-4 after estrus), and these CL do not regress. Exogenous PGF2 in mid-luteal phase CL (day 10) causes an irreversible depletion of vitamin C, which is followed by luteal regression. Thus, the day 10 CL has acquired luteolytic capacity. The objectives of this research were to determine if exogenous PGF2α affected the concentrations of mRNA for SVCT 1 & 2 in sheep corpora lutea, and to determine if the effect was dependent upon the luteolytic capacity of the CL. We also examined if there was an acute (2h) or sustained (24h) effect of PGF2α on concentrations of SVCT mRNA. Mature ewes were randomly separated into two groups: early luteal phase (day 3) and mid luteal phase (day 10). Each group was further divided into two treatments, saline treated (control) or PGF2α treated. From each animal, two CL were harvested, one at 2h and the other 24h after treatment. Using real time polymerase chain reaction (RT-PCR) we were able to estimate relative concentrations of SVCT mRNA within the cells. We found that SVCT1 did not amplify to measureable levels during standard curve validation while SVCT2 did. As such, only SVCT2 mRNA concentrations were analyzed in this study. We found that in early cycle CL, treatment with PGF2α did not alter SVCT2 mRNA concentrations at 2 or 24hrs post treatment. As the CL aged from the early cycle to the mid luteal cycle, SVCT2 mRNA concentrations increased significantly within the corpus luteum. Finally, in mid cycle CL, SVCT2 mRNA concentrations did not change 2hrs after treatment with PGF2α, but decreased sharply 24hrs after treatment. Maintenance of SVCT2 mRNA concentrations in early cycle CL is likely critical in enabling those cells to recover their initial vitamin C concentrations 24hrs after treatment with PGF2α. The significant increase in vitamin C concentrations as the CL ages from the early cycle to the mid cycle, corresponds to an increase in transporter mRNA. With the increase in membrane transporters, the cell is able to take in more vitamin C which would account for the increased vitamin C concentrations seen in the mid cycle corpora lutea. The inability of mid cycle CL to recover their vitamin C concentrations 24hrs after treatment with PGF2α, is also related to the decrease in SVCT2 mRNA seen in these cells, because there were fewer transporters available with which to replenish cellular vitamin C concentrations. As we continue to increase our knowledge of the mechanisms involved in luteal function, we come closer to developing new reproductive techniques that can benefit both animals and humans in the future.Agriculture Honors ProgramWill C. Hauk Endowment FundNo embarg

Receipt from The India Rubber & Gutta Percha Insulating Co.

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Networking the way towards antimicrobial combination therapies

Publicado em "8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014)"The exploration of new antimicrobial combinations is a pressing concern for Clinical Microbiology due to the growing number of resistant strains emerging in healthcare settings and in the general community. Researchers are screening agents with alternative modes of action and interest is rising for the potential of antimicrobial peptides (AMPs). This work presents the first ever network reconstruction of AMP combinations reported in the literature fighting Pseudomonas aeruginosa infections. The network, containing 193 combinations of AMPs with 39 AMPs and 154 traditional antibiotics, is expected to help in the design of new studies, notably by unveiling different mechanisms of action and helping in the prediction of new combinations and synergisms. The challenges faced in the attempted text-mining approaches and other considerations regarding the manual curation of the data are pointed out, reflecting about the future automation of this type of reconstruction as means to widen the scope of analysis

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

In-roads to the spread of antibiotic resistance: regional patterns of microbial transmission in northern coastal Ecuador

The evolution of antibiotic resistance (AR) increases treatment cost and probability of failure, threatening human health worldwide. The relative importance of individual antibiotic use, environmental transmission and rates of introduction of resistant bacteria in explaining community AR patterns is poorly understood. Evaluating their relative importance requires studying a region where they vary. The construction of a new road in a previously roadless area of northern coastal Ecuador provides a valuable natural experiment to study how changes in the social and natural environment affect the epidemiology of resistant Escherichia coli. We conducted seven bi-annual 15 day surveys of AR between 2003 and 2008 in 21 villages. Resistance to both ampicillin and sulphamethoxazole was the most frequently observed profile, based on antibiogram tests of seven antibiotics from 2210 samples. The prevalence of enteric bacteria with this resistance pair in the less remote communities was 80 per cent higher than in more remote communities (OR = 1.8 [1.3, 2.3]). This pattern could not be explained with data on individual antibiotic use. We used a transmission model to help explain this observed discrepancy. The model analysis suggests that both transmission and the rate of introduction of resistant bacteria into communities may contribute to the observed regional scale AR patterns, and that village-level antibiotic use rate determines which of these two factors predominate. While usually conceived as a main effect on individual risk, antibiotic use rate is revealed in this analysis as an effect modifier with regard to community-level risk of resistance

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions are welcome

Translational biomedical informatics and pharmacometrics approaches in the drug interactions research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research

Characterization of Functional and Structural Integrity in Experimental Focal Epilepsy: Reduced Network Efficiency Coincides with White Matter Changes

BACKGROUND: Although focal epilepsies are increasingly recognized to affect multiple and remote neural systems, the underlying spatiotemporal pattern and the relationships between recurrent spontaneous seizures, global functional connectivity, and structural integrity remain largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Here we utilized serial resting-state functional MRI, graph-theoretical analysis of complex brain networks and diffusion tensor imaging to characterize the evolution of global network topology, functional connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model. Epileptic networks exhibited a more regular functional topology than controls, indicated by a significant increase in shortest path length and clustering coefficient. Interhemispheric functional connectivity in epileptic brains decreased, while intrahemispheric functional connectivity increased. Widespread reductions of fractional anisotropy were found in white matter regions not restricted to the vicinity of the epileptic focus, including the corpus callosum. CONCLUSIONS/SIGNIFICANCE: Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that, despite the locality of the epileptogenic area, epileptic brains differ in their global network topology, connectivity and structural integrity from healthy brains