Evidence map›Paper›PMID 38305483›Full record

ReviewJournal of the Chinese Medical Association : JCMA2024

Structure-based approaches against COVID-19.

Ta-Chou Huang, Kung-Hao Liang, Tai-Jay Chang, Kai-Feng Hung, Mong-Lien Wang, Yen-Fu Cheng, Yi-Ting Liao, De-Ming Yang

Abstract readReview
In one paragraph

Review in Journal of the Chinese Medical Association : JCMA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. [Innate immune response to interferon gamma in severe Covid-19 positive patients].Revista medica del Instituto Mexicano del Seguro Social · 2025
    Observational
  3. Article
  4. Role ofCentral-European journal of immunology · 2025
    Article
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

8 authors.

Ta-Chou HuangDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Kung-Hao LiangDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Tai-Jay ChangDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Kai-Feng HungDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Mong-Lien WangDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Yen-Fu ChengDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Yi-Ting LiaoDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
De-Ming YangDepartment of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on human life. This review highlights the versatile roles of both classical and modern structure-based approaches for COVID-19. X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryogenic electron microscopy are the three cornerstones of classical structural biology. These technologies have helped provide fundamental and detailed knowledge regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the related human host proteins as well as enabled the identification of its target sites, facilitating the cessation of its transmission. Further progress into protein structure modeling was made using modern structure-based approaches derived from homology modeling and integrated with artificial intelligence (AI), facilitating advanced computational simulation tools to actively guide the design of new vaccines and the development of anti-SARS-CoV-2 drugs. This review presents the practical contributions and future directions of structure-based approaches for COVID-19.

Indexed as

COVID-19Artificial IntelligenceComputer SimulationCOVID-19 VaccinesHumansSARS-CoV-2COVID-19 Vaccines

Identifiers

PMID38305483
PMCPMC12718949

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.