Evidence map›Paper›PMID 38409702›Full record

ArticleCurrent drug discovery technologies2024

Short Fragmented Peptides from

Yong Hui Wong, Sau Har Lee

Abstract read
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In one paragraph

Article in Current drug discovery technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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, 2 citations in OpenAlex.

  1. Article
  2. Cancer-Targeting Applications of Cell-Penetrating Peptides.International journal of molecular sciences · 2024
    Review
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

2 authors at 1 institution in 1 country.

Yong Hui WongSchool of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, 47500, Malaysia.
Sau Har LeeSchool of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, 47500, Malaysia.
Taylor's University · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is a worldwide issue. It has been observed that conventional therapies face many problems, such as side effects and drug resistance. Recent research reportedly used marine-derived products to treat various diseases and explored their potential in treating cancers.

objectiveThis study aims to discover short-length anticancer peptides derived from pardaxin 6 through an

methodsFragmented peptides ranging from 5 to 15 amino acids were derived from the pardaxin 6 parental peptide. These peptides were further replaced with one residue and, along with the original fragmented peptides, were predicted for their SVM scores and physicochemical properties. The top 5 derivative peptides were further examined for their toxicity, hemolytic probability, peptide structures, docking models, and energy scores using various web servers. The trend of

resultsResults showed that when the amino acids were increased, SVM scores of the original fragmented peptides were also increased. Designed peptides had increased SVM scores, which was aligned with previous studies where the single residue replacement transformed the non-anticancer peptide into an anticancer agent. Moreover,

conclusionSingle residue replacement in fragmented pardaxin 6 was found to produce stronger anticancer agents through

Indexed as

Antineoplastic AgentsComputer SimulationAnimalsCell Line, TumorHemolysisHumansMolecular Docking SimulationNeoplasmsPeptide FragmentsPeptidesAntineoplastic AgentsPeptide FragmentsPeptidesanticancer peptideIn silicomarine-derived peptidepardachirus marmoratus.pardaxinpeptide designpeptide length

Identifiers

PMID38409702
OpenAlexW4392195476

What OpenQuestion holds

Textmetadata
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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.