Evidence map›Paper›PMID 32166484›Full record

ArticleMolecular diversity2021

Modeling and insights into molecular basis of low molecular weight respiratory sensitizers.

Xueyan Cui, Rui Yang, Siwen Li, Juan Liu, Qiuyun Wu, Xiao Li

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Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Xueyan Cui *Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Rui Yang *Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Siwen LiDepartment of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Juan LiuDepartment of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Qiuyun WuDepartment of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Xiao LiDepartment of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China. lixiao1688@163.com.ORCID http://orcid.org/0000-0002-1148-9898

Funding

National Natural Science Foundation of China 81803433
6 · The paper itself

Abstract

Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.

Indexed as

Models, MolecularAllergensMachine LearningMolecular WeightRespiratory HypersensitivityAllergensConsensus modelMachine learningMolecular propertyRespiratory sensitizerStructural alert

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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.