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Record W2264453529 · doi:10.2172/1105981

Multiscale Toxicology- Building the Next Generation Tools for Toxicology

2013· report· en· W2264453529 on OpenAlexaboutno aff
Scott T. Retterer, Michael P. Holsapple

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryBrookhaven National LaboratoryOak Ridge National LaboratoryLawrence Livermore National LaboratoryBattelle
KeywordsOak Ridge National LaboratoryNational laboratoryComputer scienceNanotechnologyData scienceEngineeringMaterials sciencePhysics

Abstract

fetched live from OpenAlex

A Cooperative Research and Development Agreement (CRADA) was established between Battelle Memorial Institute (BMI), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), Brookhaven National Laboratory (BNL), Lawrence Livermore National Laboratory (LLNL) with the goal of combining the analytical and synthetic strengths of the National Laboratories with BMI's expertise in basic and translational medical research to develop a collaborative pipeline and suite of high throughput and imaging technologies that could be used to provide a more comprehensive understanding of material and drug toxicology in humans. The Multi-Scale Toxicity Initiative (MSTI), consisting of the team members above, was established to coordinate cellular scale, high-throughput in vitro testing, computational modeling and whole animal in vivo toxicology studies between MSTI team members. Development of a common, well-characterized set of materials for testing was identified as a crucial need for the initiative. Two research tracks were established by BMI during the course of the CRADA. The first research track focused on the development of tools and techniques for understanding the toxicity of nanomaterials, specifically inorganic nanoparticles (NPs). ORNL"s work focused primarily on the synthesis, functionalization and characterization of a common set of NPs for dissemination to the participating laboratories. These particles were synthesized to retain the same surface characteristics and size, but to allow visualization using the variety of imaging technologies present across the team. Characterization included the quantitative analysis of physical and chemical properties of the materials as well as the preliminary assessment of NP toxicity using commercially available toxicity screens and emerging optical imaging strategies. Additional efforts examined the development of high-throughput microfluidic and imaging assays for measuring NP uptake, localization, and toxicity in vitro. The second research track within the MSTI CRADA focused on the development of ex vivo animal models for examining druginduced cardiotoxicity. ORNL's role in the second track was limited initially, but was later expanded to include the development of microfluidic platforms that might facilitate the translation of Cardiac 'Microwire' technologies developed at the University of Toronto into a functional platform for drug screening and predictive assessment of cardiotoxicity via highthroughput measurements of contractility. This work was coordinated by BMI with the Centre for the Commercialization of Regenerative Medicine (CCRM) and the University of Toronto (U Toronto). This partnership was expanded and culminated in the submission of proposal to Work for Others (WFO) agencies to explore the development of a broader set of microphysiological systems, a so call human-on-a-chip, that could be used for toxicity screening and the evaluation of bio-threat countermeasures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0050.007
Open science0.0040.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.224
GPT teacher head0.377
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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