Evidence map›Paper›PMID 34834075›Full record

ArticleMolecules (Basel, Switzerland)2021

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

Cosimo Toma, Claudia I Cappelli, Alberto Manganaro, Anna Lombardo, Jürgen Arning, Emilio Benfenati

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
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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

6 authors.

Cosimo TomaLaboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
Claudia I CappelliLaboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
Alberto ManganaroKode Chemoinformatics s.r.l.-Via Nino Pisano 14, 56122 Pisa, Italy.ORCID 0000-0002-0280-7401
Anna LombardoLaboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.ORCID 0000-0002-8364-4797
Jürgen ArningUmweltbundesamt-German Federal Environment Agency, Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany.
Emilio BenfenatiLaboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.

Funding

LIFE programme LIFE17 GIE/IT/000461Ministerium für Klimaschutz, Umwelt, Landwirtschaft, Natur- und Verbraucherschutz des Landes Nordrhein-Westfalen FKZ 3716 65 4140
6 · The paper itself

Abstract

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for

Indexed as

Machine LearningModels, BiologicalAnimalsChlorophyceaeDaphniaEcotoxicologyFishesWater Pollutants, ChemicalWater Pollutants, Chemicalapplicability domainbiological databasesDaphnia magnafishquantitative structure-activity relationship (QSAR)random forestRaphidocelis subcapitata

Identifiers

PMID34834075
PMCPMC8618112

What OpenQuestion holds

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LicenceCC BY
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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.