Evidence map›Paper›PMID 37723566›Full record

ArticleBMC veterinary research2023

Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models.

Avika Ruparell, Matthew Gibbs, Alison Colyer, Corrin Wallis, Stephen Harris, Lucy J Holcombe

Open access · goldAbstract read
In one paragraph

Article in BMC veterinary research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

6 authors at 1 institution in 1 country.

Avika RuparellWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK. Avika.Ruparell@effem.com.
Matthew GibbsWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
Alison ColyerWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
Corrin WallisWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
Stephen HarrisWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
Lucy J HolcombeWaltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
UK Health and Environment Research Institute · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease.

resultsOver 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (r

conclusionsA qPCR-based approach is an accurate, sensitive, and cost-effective method for detection of microbial biomarkers associated with periodontal health and disease. Taken together, the correlation between qPCR and high-throughput sequencing outputs, and early accuracy insights, indicate the strategy offers a prospective route to the development of diagnostic tools for canine periodontal disease.

Indexed as

Dog DiseasesGingivitisPeriodontal DiseasesPeriodontitisAnimalsDogsMachine LearningProspective StudiesSulfonamidesnimesulideSulfonamidesBiomarkersCanineDiagnosisMicrobiomeMicrobiotaPeriodontal diseasePeriodontitisqPCRQuantitative polymerase chain reaction

Identifiers

PMID37723566
PMCPMC10507867
OpenAlexW4386830317

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.