Stability of biomaterials used in adjunct to coronally advanced flap: A systematic review and network meta-analysis

Soft Tissue Substitutes in Periodontal and Peri-Implant Soft Tissue Augmentation: A Systematic Review

BACKGROUND

Different extracellular matrix (ECM)-based technologies in periodontal and peri-implant soft tissue augmentation have been proposed in the market. The present review compared the efficacy of soft tissue substitutes (STSs) and autogenous free gingival grafts (FGGs) or connective tissue grafts (CTGs) in mucogingival procedures to increase keratinized tissue (KT) width around teeth and implants.

METHODS

Two independent examiners performed an electronic search on MEDLINE and the Cochrane Library based on the following PICOS format: (P) adult patients; (I) soft tissue substitutes and FGGs/CTGs; (C) STSs vs. CTGs; STSs vs. FGGs; STSs vs control; (O) KT width gain; (S) systematic reviews, randomized controlled trials. Studies published before November 2023 were included.

RESULTS

Around teeth, all biomaterials showed superior performance compared to a coronally advanced flap (CAF) alone for treating gingival recessions. However, when compared to CTGs, acellular dermal matrices (ADMs) yield the most similar outcomes to the gold standard (CTGs), even though in multiple recessions, CTGs continue to be considered the most favorable approach. The use of STSs (acellular matrix or tissue-engineered) in combination with apically positioned flaps (APF) resulted in significantly less gain in KT width compared to that achieved with FGGs and APFs. Around dental implants, free gingival grafts were deemed more effective than soft tissue substitutes in enhancing keratinized mucosa width.

CONCLUSIONS

Based on the available evidence, questions remain about the alternative use of soft tissue substitutes for conventional grafting procedures using free gingival grafts or connective tissue grafts around teeth and implants.

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t -SNE) algorithm. The reduced dataset for t -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on "unseen" meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.

Methodological quality of network meta-analysis in dentistry: a meta-research

This meta-research aimed to provide an overview of the methodological quality and risk of bias of network meta-analyses (NMA) in dentistry. Searches for NMA of randomized clinical trials with clinical outcomes in dentistry were performed in databases up to January 2022. Two reviewers independently screened titles/abstracts, selected full texts, and extracted the data. The adherence to PRISMA-NMA reporting guideline, the AMSTAR-2 methodological quality tool, and the ROBIS risk of bias tool were assessed in the studies. Correlation between the PRISMA-NMA adherence and the AMSTAR-2 and ROBIS results was also investigated. Sixty-two NMA studies were included and presented varied methodological quality. According to AMSTAR-2, half of the NMA presented moderate quality (n = 32; 51.6%). The adherence to PRISMA-NMA also varied. Only 36 studies (58.1%) prospectively registered the protocol. Other issues lacking of reporting were data related were data related to the NMA geometry and the assessment of results consistency, and the evaluation of risk of bias across the studies. ROBIS assessment showed a high risk of bias mainly for domains 1 (study eligibility criteria) and 2 (identification and selection of studies). Correlation coefficients between the PRISMA-NMA adherence and the AMSTAR-2 and ROBIS results showed moderate correlation (rho < 0.6). Overall, NMA studies in dentistry were of moderate quality and at high risk of bias in several domains, especially study selection. Future reviews should be better planned and conducted and have higher compliance with reporting and quality assessment tools.

A New Matrix for Soft Tissue Management

Gingival recession is a mucogingival defect defined as the apical shifting of the gingival margin in relation to the CEJ. The use of connective tissue autografts allows for the obtention of very satisfactory results but is associated with undoubted disadvantages. The aim of the present work is to carry out a systematic review of the literature using a meta-analysis to investigate the clinical efficacy of xenogeneic collagen matrix (XCM) in the treatment of gingival recessions. This revision was carried out strictly following the guidelines published in the Cochrane Handbook. Thus, a meta-analysis was performed to calculate relative risks and standardized mean differences for each of the variables considered. The results of the meta-analysis show that CAF + CTG was statistically better than CAF + XCM in almost all the variables analyzed: complete root coverage (RR 0.46), mean root coverage (SMD −0.89), recession reduction (SMD −0.98), clinical attachment level (SMD −0.63) and gingival thickness (SMD −1.68). Meanwhile, CAF + XCM was slightly better than CAF alone in regard to: mean root coverage (SMD 0.51), recession reduction (SMD 0.47) and gingival thickness (SMD 0.56). It is possible to conclude that CAF + CTG still remains the gold standard in radicular coverage.