Evidence map›Paper›PMID 35944286›Full record

ArticleEnvironment international2022

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity.

Yajun Hong, Chenglian Feng, Xiaowei Jin, Huiyu Xie, Na Liu, Yingchen Bai, Fengchang Wu, Sandy Raimondo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environment international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
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  5. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yajun HongState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Chenglian FengState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. Electronic address: wufengchang@vip.skleg.cn.
Xiaowei JinChina National Environmental Monitoring Centre, Beijing, 100012, China. Electronic address: jinxw@cnemc.cn.
Huiyu XieState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Na LiuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Yingchen BaiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Fengchang WuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. Electronic address: fengchenglian@163.com.
Sandy RaimondoUnited States Environmental Protection Agency, Gulf Ecosystem Measurement and Modeling Division, Gulf Breeze, Florida 32561, United States.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R

Indexed as

RiversWater Pollutants, ChemicalAquatic OrganismsQuantitative Structure-Activity RelationshipReproducibility of ResultsRisk AssessmentSpecies SpecificityWater Pollutants, ChemicalEndocrine disrupting chemicalsHC(5)In silico modelsSurface waterToxicity threshold

Identifiers

PMID35944286
PMCPMC10015408

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.