Evidence map›Paper›PMID 38765608›Full record

ArticleComputational and structural biotechnology journal2024

NTCdb: Single-cell transcriptome database of human inflammatory-associated diseases.

Chaochao Wang, Ting Huyan, Wuli Guo, Qi Shu, Qi Li, Jianyu Shi

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chaochao WangSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Ting HuyanSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Wuli GuoSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Qi ShuSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Qi LiSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Jianyu ShiSchool of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With both the advancement of technology and the decline in costs, single-cell transcriptomics sequencing has become widespread in the biomedical area in recent years. It can facilitate the pathogenic characteristics at the single-cell level, which will assist clinical researchers in exploring the mechanism of diseases. As a result, single-cell transcriptome data based on clinical samples grew exponentially. However, there is still a lack of a comprehensive database about immunocytes in inflammatory-associated diseases. To address this deficiency, we propose a human inflammatory-associated disease-based single-cell transcriptome database, NTCdb (www.ntcdb.org.cn). NTCdb integrates the open-source data of 1,023,166 cells derived from 11 tissues of 17 inflammatory-associated diseases in a uniform pipeline. It provides a set of analyzing results, including cell communication analysis, enrichment analysis, and Pseudo-Time analysis, to obtain various characteristics of immune cells in inflammatory-associated disease. Taking COVID-19 as a case study, NTCdb displays important information including potentially significant functions of certain cells, genes, and signaling pathways, as well as the commonalities of specific immunocytes between different inflammatory-associated disease.

Indexed as

Cell communicationDatabaseImmunocytesInflammatory-associated diseaseSingle cell transcriptome

Identifiers

PMID38765608
PMCPMC11098674

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LicenceCC BY-NC-ND
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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.