Evidence map›Paper›PMID 35539618›Full record

ArticleRSC advances2018

Privileged substructures for anti-sickling activity

Chuleeporn Phanus-Umporn, Watshara Shoombuatong, Veda Prachayasittikul, Nuttapat Anuwongcharoen, Chanin Nantasenamat

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in RSC advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
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

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 3 countries.

Chuleeporn Phanus-UmpornCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand chanin.nan@mahidol.edu.ORCID https://orcid.org/0000-0001-5439-8462
Watshara ShoombuatongCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand chanin.nan@mahidol.edu.ORCID https://orcid.org/0000-0002-3394-8709
Veda PrachayasittikulCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand chanin.nan@mahidol.edu.ORCID https://orcid.org/0000-0001-6338-3721
Nuttapat AnuwongcharoenCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand chanin.nan@mahidol.edu.ORCID https://orcid.org/0000-0001-5361-5314
Chanin NantasenamatCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University Bangkok 10700 Thailand chanin.nan@mahidol.edu.ORCID https://orcid.org/0000-0003-1040-663X
University of Medical Sciences and Technology · SD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure-activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models

Identifiers

PMID35539618
PMCPMC9078244
OpenAlexW2786553003

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.