Immunohistochemical Profiling in Temporomandibular Disorders: A Systematic Review of Biomarker Patterns in Synovial Tissue
Bibliographic record
Abstract
BACKGROUND: Temporomandibular disorders (TMD) are multifactorial conditions involving biomechanical dysfunction and progressive degeneration of the temporomandibular joint (TMJ) and surrounding tissues. Although affecting up to 36% of the population, their underlying molecular mechanisms remain incompletely understood. Immunohistochemical analysis of synovial biomarkers may help clarify processes involved in tissue degeneration and symptom development. OBJECTIVE: This systematic review investigates associations between immunohistochemical biomarkers in synovial tissue and morphological or symptomatic findings in TMD, with a focus on internal derangement and disc displacement. METHODS: A comprehensive literature search was conducted in MEDLINE/PubMed, EMBASE, Web of Science, and selected journals. Studies were screened according to PRISMA guidelines, and methodological quality was assessed using the Newcastle-Ottawa Scale. Due to heterogeneity across studies, a narrative synthesis was performed. RESULTS: Thirteen studies involving 493 patients (mean age: 42.2 years; 82% women) were included, analyzing 23 different biomarkers. Frequently examined markers included MMPs, COX-2, iNOS, interleukins, and VEGF. Several of these showed statistically significant correlations with histological, radiological, or clinical findings, suggesting roles in joint homeostasis, inflammation, and tissue remodeling associated with TMD. CONCLUSION: Based on the reported associations with histological, radiological, and clinical findings, biomarkers were categorised into stage-specific and functional groups, underscoring their relevance for disease stratification and prognosis. By revealing disease-related patterns in progression and severity, synovial biomarkers have the potential to empower our understanding of the underlying mechanisms of TMD and contribute to more precise diagnostic and therapeutic approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".