Evidence map›Paper›PMID 42339261›Full record

ReviewFrontiers in dental medicine2026

Advances in oral disease models: a mini-review of developments from 2015 to 2025.

Jia Huang, Justin Le-Tran, Nika S Kobayashi, Yoshifumi Kobayashi, Emi Shimizu

Abstract readReview
In one paragraph

Review in Frontiers in dental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jia HuangSchool of Graduate Studies, Newark Health Science Campus, Rutgers, Newark, NJ, United States.
Justin Le-TranDepartment of Oral Biology, Rutgers School of Dental Medicine, Rutgers, Newark, NJ, United States.
Nika S KobayashiDepartment of Oral Biology, Rutgers School of Dental Medicine, Rutgers, Newark, NJ, United States.
Yoshifumi KobayashiDepartment of Oral Biology, Rutgers School of Dental Medicine, Rutgers, Newark, NJ, United States.
Emi ShimizuDepartment of Oral Biology, Rutgers School of Dental Medicine, Rutgers, Newark, NJ, United States.

Funding

The novel functions of matrix metalloproteinase 13 supporting dentin-pulp reparative processesR56DE033709 · NIDCR · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI SHIMIZU, EMI · 2024 to 2024
$400k
NIDCR NIH HHS R56 DE033709
6 · The paper itself

Abstract

Oral diseases represent one of the most widespread global health burdens, affecting billions of people worldwide, causing pain, disability, and substantial treatment costs. Despite their prevalence, progress in prevention and therapy has been limited, in part, by experimental models that do not fully capture the complexity of the oral biological and environmental landscape. Over the past decade, however, major advances in model development have expanded the possibilities for studying oral disease. This mini-review summarizes advances from 2015 to 2025, focusing on caries and endodontic infections, gingivitis and periodontitis, peri-implantitis, mucosal disorders, oral and oropharyngeal cancers, and salivary gland diseases. Recent innovations include saliva-derived biofilm systems that reproduce ecological transitions, organ-on-chip systems that replicate fluid dynamics, and patient-derived organoids and xenografts that preserve clinical characteristics. In parallel, immune-integrated models now allow direct interrogation of host responses to pathogens. Separate from these experimental platforms, advanced analytical and computational approaches, including single-cell profiling, spatial transcriptomics, radiomics, and artificial intelligence (AI)-assisted image analysis, are increasingly linking molecular signatures with structural and functional disease outcomes. Together, these experimental models and complementary analytical tools mark a shift from reductionist approaches toward dynamic, patient-relevant frameworks that better capture the complexity of oral diseases. Remaining challenges include modeling chronic disease progression, incorporating viral and autoimmune components, and improving reproducibility through standardization across platforms. Addressing these limitations will be important for translating next-generation experimental models into clinically meaningful advances in oral health care.

Indexed as

artificial intelligenceepithelial-immune crosstalkmultispecies biofilmsoral disease modelsorgan-on-chippatient-derived organoidsprecision medicinetranslational research

Identifiers

PMID42339261
PMCPMC13284156

What Socratic 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 Socratic graph.