Evidence map›Paper›PMID 42239641›Full record

ArticleChina CDC weekly2025

A Multi-omics Framework Combining Genomics and Proteomics for Silicosis Prediction in Chinese Workers - Jiangsu Province, China, 2023-2024.

Furu Wang, Qianqian Gao, Chenjie Li, Lei Han, Chuanfeng Zhang, Zhengdong Zhang, Baoli Zhu

Abstract read
In one paragraph

Article in China CDC weekly, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Furu WangDepartment of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Qianqian GaoJiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Chenjie LiDepartment of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Lei HanJiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Chuanfeng ZhangJiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Zhengdong ZhangDepartment of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Baoli ZhuJiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: Currently, the detection of silicosis relies on imaging and pulmonary function tests, which are effective only at identifying the advanced stages. Additionally, no effective protein biomarkers or genetic risk models exist for the early detection or targeted intervention of silicosis. What is added by this report?: This study integrates genomics and proteomics to identify new genetic loci associated with susceptibility to silicosis. Using Mendelian randomization and protein quantitative trait loci (pQTL) analysis, 2 functionally significant genetic variants [rs6677666 (WLS) and rs2272528 (COL4A4)] and 5 protein biomarkers (MMP12, EGF, Gal_9, GZMA, and ICOSLG) mechanistically linked to silicosis pathogenesis were identified. A diagnostic causal protein risk score (CPRS) model was then constructed to provide a robust tool for early detection in high-risk populations. What are the implications for public health practice?: These findings provide new insights into the early diagnosis of silicosis, and support the development of preventive and screening strategies for populations at risk, enhancing public health policies for the control and management of silicosis.

Indexed as

Mendelian randomizationPredictive modelProteomicsSilicosis

Identifiers

PMID42239641
PMCPMC13227063

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.