12 research outputs found

    Current ecotoxicity testing needs among selected U.S. federal agencies

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    U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies’ regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations

    Faster Identification of Good Quality Data: The Semi-Automated Study Quality Assessment Reporting and Evaluation (SQuARE) Tool

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    Poster for SETAC on Nov. 12-16, 2023 in Louisville, KYScience Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25</p

    A Curated Database of Rodent Uterotrophic Bioactivity

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    Tema ovog rada je prikaz osnova, rada, dijelova, podjele i trendova u pogledu automatiziranih skladišnih sustava. Rad je orijentiran prema prikazu inovativnih sustava automatiziranog skladištenja, a posebna će pažnja biti posvećena sustavu s tračnim vozilima (engl. Shuttle based storage and retrieval system). Navedeni sustav će biti opisan i bit će napravljeni proračuni kako bi se sustav mogao usporediti s nekim od tradicionalnih automatiziranih skladišnih sustava. Analiza potonjeg sustava rezultirat će zaključcima koji će biti temelj za vrednovanje.This paper is about the basics, operation, parts, division and new trends in terms of automated storage and retrieval systems. Lead orientation will be one towards inovative systems of automated storage and biggest attention will be given to shuttle based system (Shuttle based storage and retrieval system). Shuttle based system will be described and some calculations will be made in order to compare it with some traditional automated storage and retrieval systems. Analaysis will give us some conclusions which will help to properly evaluate mentioned system

    Development and Validation of a Computational Model for Androgen Receptor Activity

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    Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (<i>n</i> = 29) and 97.5% for antagonist (<i>n</i> = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity

    Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway

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    U ovom radu ću pojasniti što je strojno učenje, umjetna neuronska mreža, te konvolucijska neuronska mreža i kako je ona drukčija od obične neuronske mreže. U praktičnom dijelu ću razviti svoju konvolucijsku neuronsku mrežu u programskom jeziku Python, za razvrstavanje slika iz skupa podataka CIFAR-10, u 10 kategorija

    Therapeutic trials for long COVID-19: A call to action from the interventions taskforce of the RECOVER initiative.

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    Although most individuals recover from acute SARS-CoV-2 infection, a significant number continue to suffer from Post-Acute Sequelae of SARS-CoV-2 (PASC), including the unexplained symptoms that are frequently referred to as long COVID, which could last for weeks, months, or even years after the acute phase of illness. The National Institutes of Health is currently funding large multi-center research programs as part of its Researching COVID to Enhance Recover (RECOVER) initiative to understand why some individuals do not recover fully from COVID-19. Several ongoing pathobiology studies have provided clues to potential mechanisms contributing to this condition. These include persistence of SARS-CoV-2 antigen and/or genetic material, immune dysregulation, reactivation of other latent viral infections, microvascular dysfunction, and gut dysbiosis, among others. Although our understanding of the causes of long COVID remains incomplete, these early pathophysiologic studies suggest biological pathways that could be targeted in therapeutic trials that aim to ameliorate symptoms. Repurposed medicines and novel therapeutics deserve formal testing in clinical trial settings prior to adoption. While we endorse clinical trials, especially those that prioritize inclusion of the diverse populations most affected by COVID-19 and long COVID, we discourage off-label experimentation in uncontrolled and/or unsupervised settings. Here, we review ongoing, planned, and potential future therapeutic interventions for long COVID based on the current understanding of the pathobiological processes underlying this condition. We focus on clinical, pharmacological, and feasibility data, with the goal of informing future interventional research studies
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