ArticleAnalytical cellular pathology (Amsterdam)2026
Identification of Potential Therapeutic Agents for Primary Amebic Meningoencephalitis Using Text Mining and Bioinformatics Analyses.
Article in Analytical cellular pathology (Amsterdam), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Identification of Potential Therapeutic Agents for Primary Amebic Meningoencephalitis Using Text Mining and Bioinformatics Analyses.Analytical cellular pathology (Amsterdam) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Naegleria fowleri, the brain-eating ameba, causes primary amebic meningoencephalitis (PAM), a fatal infectious disease that affects the central nervous system (CNS). We aimed to evaluate the functions and potential drugs targeting PAM using text mining and bioinformatics analyses. PAM-associated genes were identified using a disease database and mined from literature. To identify candidate drugs targeting PAM, 218 genes were analyzed using PanDrugs, drug Manually Annotated Targets and Drugs Online Resource (MATADOR), and the drug Comparative Toxicogenomics Database (CTD) by text mining. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional analyses were performed to examine the mechanism of action of PAM. The GO functions of genes involved in PAM identified by text mining were leukocyte differentiation and the regulation of cytokine production. Disease-related PAM analyses indicated association with Zellweger syndrome, peroxisomal disease, periodontitis, and leishmaniasis. KEGG enrichment included pathways related to inflammatory bowel disease, malaria, interleukin (IL)-17 signaling pathway, Yersinia infection, Chagas disease, amebiasis, rheumatoid arthritis, pathogenic Escherichia coli infection, lipids, atherosclerosis, and peroxisomes. In addition, arsenic trioxide, bortezomib, dasatinib, bosutinib, bevacizumab, paclitaxel, midostaurin, tamoxifen, copanlisib, and pazopanib were identified as potential drugs targeting PAM using PanDrugs software. Our analyses revealed that text mining-related PAM genes were enriched in several pathways, such as peroxisomes and protein localization. We suggest that PAM is linked to other diseases, such as Zellweger's syndrome, leishmaniasis, and periodontitis, and provide potential drugs for effective treatment.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.