The extracellular matrix (ECM) is a dynamic network providing mechanical and biochemical cues that regulate cellular behavior. ECM stiffness critically influences fibroblasts, the primary ECM producers, particularly in inflammation and fibrosis. This review explores the role of ECM stiffness in fibroblast-driven inflammation and tissue remodeling, focusing on the physicochemical and biological mechanisms involved. Engineered materials, hydrogels, and polydimethylsiloxane (PDMS) are highlighted for replicating tissue-specific stiffness, enabling precise control over cell–matrix interactions. The surface functionalization of substrate materials, including collagen, polydopamine, and fibronectin, enhances bioactivity and fibroblast adhesion. Key mechanotransduction pathways, such as integrin signaling and YAP/TAZ activation, are related to regulating fibroblast behaviors and inflammatory responses. The role of fibroblasts in driving chronic inflammatory diseases emphasizes their therapeutic potentials. Advances in ECM-modifying strategies, including tunable biomaterials and hydrogel-based therapies, are explored for applications in tissue engineering, drug delivery, anti-inflammatory treatments, and diagnostic tools for the accurate diagnosis and prognosis of ECM stiffness-related inflammatory diseases. This review integrates mechanobiology with biomedical innovations, providing a comprehensive prognosis of fibroblast responses to ECM stiffness and outlining future directions for targeted therapies.

Objectives: To investigate the prevalence of self-reported pain-related temporomandibular disorders (TMDs) and its association with psychological status in a dental clinic setting. Methods: In this cross-sectional study, patients were asked to complete a TMD pain screener and the Patient Health Questionnaire-4 (PHQ-4). Correlations between symptoms of pain-related TMDs and PHQ-4 scores were analysed using Spearman’s correlation test. Symptoms of pain-related TMDs were compared between four groups of participants with different psychological profiles using the Kruskal–Wallis test followed by multiple comparisons. The level of significance was adjusted using the Dunn–Bonferroni test. Results: The prevalence of self-reported pain-related TMDs was 22.2%. TMD pain score was positively correlated with PHQ-4 score. The high anxiety and the comorbidity groups had significantly higher TMD pain scores than the controls. Conclusion: There was a high prevalence of self-reported pain-related TMDs, which was correlated with scores on all psychological assessment scales. Symptoms of pain-related TMDs were significantly greater in patients with high anxiety scores, regardless of depression level.

Objectives: The aims of this study were to explore the use of unsupervised machine learning in clustering the population based on reports of oral pain, psychological distress, and sleep problems and to compare demographic and socio-economic characteristics as well as levels of functional domains (work, social, and leisure) between clusters. Materials and Methods: In this cross-sectional study, a total of 1613 participants from the National Health and Nutrition Examination Survey in 2017-2018 were analyzed. Five variables, including oral pain, depression, anxiety, sleep apnea, and excessive daytime sleepiness, were selected for cluster analysis using the k-medoids clustering algorithm. The distribution of categorical variables between clusters was assessed using chi 2 test. One-way analysis of variance and Kruskal-Wallis H test were used to compare numerical variables as appropriate. Results: Five distinct clusters were identified: healthy, norm, anxiety, apnea-comorbid, and pain-comorbid. The apnea-comorbid cluster had mean age of 59 years and higher proportion of men. The pain-comorbid cluster had mean age of 56 years and higher proportion of women. Whites constituted a majority of both comorbid clusters. The pain-comorbid cluster demonstrated the least percentage of individuals with college degree, the lowest income, and significant impairment in all functional domains. Conclusion: Through the use of unsupervised machine learning, the clusters with comorbidity of oral pain, psychological distress, and sleep problems have emerged. Major characteristics of the comorbid clusters included mean age below 60 years, White, and low levels of education and income. Functional domains were significantly impaired. The comorbid clusters thus call for public health intervention.

Non-odontogenic oral pain is prevalent among the older people and significantly impacts their quality of life. Non-odontogenic oral pain is usually persistent and accompanied by comorbidities such as psychosocial distress and sleep-related problems, which further complicate pain management. The relationship between non-odontogenic oral pain and comorbidities in the older people, however, has not been well documented. This study aimed to identify the factors associated with non-odontogenic oral pain in an edentulous older population and to subgroup this population based on the patterns of oral pain and its associated factors. In this cross-sectional study, data from completely edentulous individuals in the National Health and Nutrition Examination Survey for the period from 2017 to 2020 March (pre-pandemic) were analysed. Associations and correlations between oral pain and 46 other variables, including demographic, questionnaire, examination and laboratory data, were investigated using Pearson’s chi-squared test and Spearman’s rank correlation test. A p value of < 0.05 was considered statistically significant. Clustering of the data was performed using the k-prototypes algorithm, an unsupervised machine learning. Approximately 42% of the edentulous older people experienced oral pain. ‘Having been told to take daily low-dose aspirin’ was significantly associated with oral pain. Oral pain was positively correlated with depressive symptoms and excessive daytime sleepiness (EDS), and negatively correlated with diastolic blood pressure, red blood cell count, haemoglobin level and haematocrit. The k-prototypes algorithm identified a cluster characterised by frequent oral pain, depression and EDS. This study identified distinct patterns of comorbidities among edentulous older people living with oral pain.

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor and non-motor symptoms, including pain. Temporomandibular disorder (TMD)-like pain, defined as self-reported pain modified by jaw activities, has been suggested to be more prevalent in PD, but its association with PD severity remains unclear. This study aimed to investigate the association between pain modified by jaw activities and PD severity and the temporal stability of such pain in PD. Methods: This prospective study recruited 28 individuals with PD. Motor symptom severity and disability levels were evaluated using the modified Hoehn and Yahr (mHY) staging and modified Rankin Scale (mRS), respectively. Based on the diagnostic criteria for TMD, a questionnaire assessing pain modified by jaw activities and clinical examination were utilised. Pain modified by jaw activities was reassessed at one, two, and three months. Statistical analyses included Spearman’s rank correlation test and Friedman test, with p < 0.05 considered significant. Results: The participants’ mean age was 69.2 ± 9.6 years; 53.6% were male. Eight participants reported pain modified by jaw activities. Clinical examination identified painful palpation sites in 14 participants, most commonly in the masseter muscle body. Pain modified by jaw activity count showed significant positive correlations with mHY stage (rho = 0.48, p = 0.015) and mRS score (rho = 0.41, p = 0.04). Twenty-four participants completed follow-up, with no significant changes in pain reports over three months. Conclusions: Some individuals with PD may experience persistent TMD-like pain, which is correlated with motor symptom severity and disability levels, highlighting the importance of routine TMD screening in PD.