ReviewBiomedicines2025
Exploring the Role of Transcriptomics, Proteomics, and Machine Learning in HPV Infection and Cardiovascular Disease.
Review in Biomedicines, 2025. 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHuman papillomavirus (HPV) is a serious disease caused by a viral infection that can lead to various types of cancers in both women and men. Nearly all cases of cervical cancer (99.7%) develop as a result of an HPV infection, ranging from low to high grade, with a 5-year mortality rate ranging from 8 to 81% depending on the timeliness of diagnosis. Recent studies have further shown that HPV significantly increases the risk of cardiovascular disease, including coronary artery disease (CAD). However, the mechanism and impact of HPV on CVD from a proteomics and transcriptomics perspective are not well understood.
objectivesThe purpose of this work is to provide the evidence framework for using machine learning to further advance knowledge on the interplay of HPV and CVD in relation to proteomic and transcriptomic changes. KEY
findingsIn addition to existing known relationships between HPV and atherosclerosis and CAD, dilated cardiomyopathy (DCM) is identified as an important cardiovascular disease modified by HPV infections. A more comprehensive understanding of the cholesterol-modifying mechanisms underpinning HPV's influence on CVD has been identified. Downstream ML has been used to selectively identify key proteins for subsequent bioinformatic mining across a range of public and in-house curated databases. IMPLICATIONS: By further understanding the mechanisms underlying HPV-induced cardiovascular pathogenesis, machine learning models can be developed in a more targeted manner, stratifying patients that will have an optimal response to emerging probiotic-based therapies.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.