Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
1 citing paper in PubMed, 12 citations in OpenAlex.
Chun-Yun ZhangDepartment of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States.
Xueshu LiDepartment of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States.ORCID 0000-0002-0881-5381
Kimberly P Keil StietzDepartment of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States.
Sunjay SethiDepartment of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States.
Weizhu YangDepartment of Pharmacology and Toxicology, College of Pharmacy, University of Arizona, Tucson, Arizona 85721, United States.
Rachel F MarekDepartment of Civil and Environmental Engineering and IIHR Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa 52242, United States.ORCID 0000-0002-7898-2900
Xinxin DingDepartment of Pharmacology and Toxicology, College of Pharmacy, University of Arizona, Tucson, Arizona 85721, United States.
Pamela J LeinDepartment of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, California 95616, United States.ORCID 0000-0001-7665-7584
Keri C HornbuckleDepartment of Civil and Environmental Engineering and IIHR Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa 52242, United States.ORCID 0000-0002-3478-3221
Hans-Joachim LehmlerDepartment of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States.ORCID 0000-0001-9163-927X
University of Iowa · USUniversity of California, Davis · USUniversity of Arizona · US
Funding
Training CoreP42ES013661 · NIEHS · UNIVERSITY OF IOWA · PI Rachel Frances Marek · 2006 to 2026
$60.3M
Pulmonary Toxicology Facility CoreP30ES005605 · NIEHS · UNIVERSITY OF IOWA · PI Jong Sung Kim · 1990 to 2026
$40.5M
UC Davis Environmental Health Sciences Core CenterP30ES023513 · NIEHS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Ameer Taha · 2015 to 2026
$26.0M
Molecular and Cellular Basis of PCB Developmental Neurotoxicity: ViCTER supplementR01ES014901 · NIEHS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI HANS-JOACHIM LEHMLER, Pamela J Lein · 2009 to 2026
$9.3M
PCB-mediated dysbiosis of the gut microbiome: A missing link in PCB-mediated neurodevelopmental disorders?R01ES031098 · NIEHS · UNIVERSITY OF IOWA · PI CUI, JULIA YUE, LEHMLER, HANS-JOACHIM · 2020 to 2024
$3.1M
PCB Enantiomers Implicated in Neurodevelopmental Disorders: Identification of Individual Metabolic Factors that Determine Risk and VulnerabilityR21ES027169 · NIEHS · UNIVERSITY OF IOWA · PI LEHMLER, HANS-JOACHIM · 2017 to 2018
$410k
Molecular and Cellular Basis of PCB Developmental NeurotoxicityR56ES014901 · NIEHS · OREGON HEALTH & SCIENCE UNIVERSITY · PI LEIN, PAMELA J · 2007 to 2007
Laboratory studies of the disposition and toxicity of hydroxylated polychlorinated biphenyl (OH-PCB) metabolites are challenging because authentic analytical standards for most unknown OH-PCBs are not available. To assist with the characterization of these OH-PCBs (as methylated derivatives), we developed machine learning-based models with multiple linear regression (MLR) or random forest regression (RFR) to predict the relative retention times (RRT) and MS/MS responses of methoxylated (MeO-)PCBs on a gas chromatograph-tandem mass spectrometry system. The final MLR model estimated the retention times of MeO-PCBs with a mean absolute error of 0.55 min (
Indexed as
Polychlorinated BiphenylsAnimalsGas Chromatography-Mass SpectrometryHydroxylationMachine LearningMiceTandem Mass SpectrometryPolychlorinated BiphenylsGC-MS/MS methodmodel predictionOH-PCBsrelative response factorrelative retention time
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples. · full record | OpenQuestion