ArticlePloS one2024
Evaluation of the cervical liquid-based cytology sample as a microbiome resource for dual diagnosis.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer, which is mainly caused by oncogenic human papillomavirus subtypes, remains a significant global health challenge. Recent studies have indicated a connection between cervical cancer and the uterine microbiome, underscoring its importance. This study explored the potential of liquid-based cytology (LBC) samples, which are typically used for cytological analysis, in investigating the cervical microbiome. Thirty women participated in the study and provided clinical information. Three samples were obtained from each participant: one for clinical purposes using LBC, another for microbiome sampling using LBC, and a third using the SWAB Microbiome kit. The LBC and traditional swab (SWAB) samples were subjected to high-throughput 16S rRNA gene sequencing for microbiome analysis. The results revealed a consistent dominance of key taxa, particularly Lactobacillus spp. The analysis of differential abundance highlighted variations in microbial abundance among individuals, which were more prominent than those resulting from the sampling methods. Functional analysis identified arachidonic acid and alpha-linolenic acid metabolism, along with a cautionary note regarding the low mean proportion values. The network analysis revealed positive correlations between indicators of structure among the networks, highlighting the robustness of microbiome similarities despite the diversity of sampling methods. Supervised machine learning has revealed challenges in distinguishing LBC and SWAB samples based on their microbiome features. Weighted co-expression network analysis revealed that the correlation between microbial clusters and the sampling method with clinical data was not significant. This study emphasizes the similarity in microbial communities observed using the LBC and SWAB methods, highlighting the potential of using dual diagnostic approaches. Additionally, the use of residual LBC samples in large-scale microbiological studies can provide comprehensive insights into cervical health and disease.
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