Impaired Tertiary Dentin Secretion after Shallow Injury in Tgfbr2-Deficient Dental Pulp Cells Is Rescued by Extended CGRP Signaling.

The transforming growth factor ß (TGFß) superfamily is a master regulator of development, adult homeostasis, and wound repair. Dysregulated TGFß signaling can lead to cancer, fibrosis, and musculoskeletal malformations. We previously demonstrated that TGFß receptor 2 (Tgfbr2) signaling regulates odontoblast differentiation, dentin mineralization, root elongation, and sensory innervation during tooth development. Sensory innervation also modulates the homeostasis and repair response in adult teeth. We hypothesized that Tgfbr2 regulates the neuro-pulpal responses to dentin injury. To test this, we performed a shallow dentin injury with a timed deletion of Tgfbr2 in the dental pulp mesenchyme of mice and analyzed the levels of tertiary dentin and calcitonin gene-related peptide (CGRP) axon sprouting. Microcomputed tomography imaging and histology indicated lower dentin volume in Tgfbr2cko M1s compared to WT M1s 21 days post-injury, but the volume was comparable by day 56. Immunofluorescent imaging of peptidergic afferents demonstrated that the duration of axon sprouting was longer in injured Tgfbr2cko compared to WT M1s. Thus, CGRP+ sensory afferents may provide Tgfbr2-deficient odontoblasts with compensatory signals for healing. Harnessing these neuro-pulpal signals has the potential to guide the development of treatments for enhanced dental healing and to help patients with TGFß-related diseases.

Effects of extraoral storage time on autologous gingival graft early healing: A split-mouth randomized study.

AIMS: Despite the established use of palatal tissue grafts for mucogingival procedures, there are no studies on the effect of extraoral storage time on graft outcomes. This prospective split-mouth randomized experimental clinical trial aimed to assess whether gingival graft extraoral storage time affects graft healing. METHODS: Standardized grafts were harvested from the palate and stored extraorally for 2 (Control) or 40 (Test) minutes before being placed at recipient beds. Intraoral scans, clinical photographs, and tissue blood perfusion were obtained preoperatively, postoperatively, and at follow-up visits (Days 2 (PO2), 3 (PO3), 7 (PO7), and 14 (PO14)). Healing Score Index (HSI) and wound fluid (WF) biomarkers (angiogenin, IL-6, IL-8 (CXCL8), IL-33, VEGF-A, and ENA-78 (CXCL5)) were also assessed. RESULTS: Twenty-three participants completed all study visits. Extraoral storage time was 2.3 ± 1.1 min and 42.8 ± 3.4 min for C and T grafts, respectively (p < .0001). Recipient beds remained open for 21.4 ± 1.7 min. No graft underwent necrosis or failed to heal by PO14. Minimal volumetric changes were observed, without significant intergroup differences (p ≥ .11). Graft perfusion initially decreased post-harvesting before peaking on PO7 for both C and T grafts, with no significant intergroup differences (p ≥ .14). HSI values progressively increased, with no significant intergroup differences (p ≥ .22). WF analysis revealed detectable levels for all biomarkers tested, without significant intergroup differences (p ≥ .23). CONCLUSION: Extraoral storage time of 40 min has neither statistically significant nor clinically discernible effects on autologous graft revascularization, early healing, or survival, as determined by physiological, wound healing, and molecular parameters.

Scale mixture of skew-normal linear mixed models with within-subject serial dependence.

In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew-normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM-type algorithm for parameters estimation via maximum likelihood and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and some simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm.