Evidence map›Paper›PMID 33805090›Full record

ArticleGenes2021

Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks.

Katarzyna Zaorska, Tomasz Szczapa, Maria Borysewicz-Lewicka, Michał Nowicki, Karolina Gerreth

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Review
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  4. Article
  5. Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024
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  9. Article
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  14. MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning.International journal of environmental research and public health · 2022
    Article
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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

5 authors at 1 institution in 1 country.

Katarzyna ZaorskaDepartment of Histology and Embryology, Poznan University of Medical Sciences, 6 Swiecickiego Street, 60-781 Poznan, Poland.ORCID 0000-0002-3894-781X
Tomasz SzczapaDepartment of Neonatology, Biophysical Monitoring and Cardiopulmonary Therapies Research Unit, Poznan University of Medical Sciences, 33 Polna Street, 60-535 Poznan, Poland.ORCID 0000-0002-5214-2719
Maria Borysewicz-LewickaDepartment of Risk Group Dentistry, Chair of Pediatric Dentistry, Poznan University of Medical Sciences, 70 Bukowska Street, 60-812 Poznan, Poland.
Michał NowickiDepartment of Histology and Embryology, Poznan University of Medical Sciences, 6 Swiecickiego Street, 60-781 Poznan, Poland.ORCID 0000-0002-7265-8181
Karolina GerrethDepartment of Risk Group Dentistry, Chair of Pediatric Dentistry, Poznan University of Medical Sciences, 70 Bukowska Street, 60-812 Poznan, Poland.ORCID 0000-0001-5740-0855
Poznan University of Medical Sciences · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral genes and single nucleotide polymorphisms (SNPs) have been associated with early childhood caries. However, they are highly age- and population-dependent and the majority of existing caries prediction models are based on environmental and behavioral factors only and are scarce in infants.

methodsWe examined 6 novel and previously analyzed 22 SNPs in the cohort of 95 Polish children (48 caries, 47 caries-free) aged 2-3 years. All polymorphisms were genotyped from DNA extracted from oral epithelium samples. We used Fisher's exact test, receiver operator characteristic (ROC) curve and uni-/multi-variable logistic regression to test the association of SNPs with the disease, followed by the neural network (NN) analysis.

resultsThe logistic regression (LogReg) model showed 90% sensitivity and 96% specificity, overall accuracy of 93% (

conclusionsNeural network prediction model might be a substantial tool for screening/early preventive treatment of patients at high risk of caries development in the early childhood. The knowledge of potential risk status could allow early targeted training in oral hygiene and modifications of eating habits.

Indexed as

Gene Regulatory NetworksPolymorphism, Single NucleotideAmelogeninCase-Control StudiesChild, PreschoolComputational BiologyDental Caries SusceptibilityDental Enamel ProteinsExtracellular Matrix ProteinsFemaleGenetic Predisposition to DiseaseGenotyping TechniquesHumansMaleMatrix Metalloproteinase 16Neural Networks, ComputerAmelogeninAMELX protein, humanDental Enamel ProteinsENAM protein, humanExtracellular Matrix ProteinsMatrix Metalloproteinase 16MMP16 protein, humantuftelinartificial neural networkcomplex traitearly childhood cariesearly prediction modelsingle nucleotide polymorphisms

Identifiers

PMID33805090
PMCPMC8064067
OpenAlexW3137899374

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.