Immediate rehabilitation of a rheumatoid arthritis patient with single-piece implants

Challenges and Pitfalls of Research Designs Involving Magnesium-Based Biomaterials: An Overview

Magnesium-based biomaterials hold remarkable promise for various clinical applications, offering advantages such as reduced stress-shielding and enhanced bone strengthening and vascular remodeling compared to traditional materials. However, ensuring the quality of preclinical research is crucial for the development of these implants. To achieve implant success, an understanding of the cellular responses post-implantation, proper model selection, and good study design are crucial. There are several challenges to reaching a safe and effective translation of laboratory findings into clinical practice. The utilization of Mg-based biomedical devices eliminates the need for biomaterial removal surgery post-healing and mitigates adverse effects associated with permanent biomaterial implantation. However, the high corrosion rate of Mg-based implants poses challenges such as unexpected degradation, structural failure, hydrogen evolution, alkalization, and cytotoxicity. The biocompatibility and degradability of materials based on magnesium have been studied by many researchers in vitro; however, evaluations addressing the impact of the material in vivo still need to be improved. Several animal models, including rats, rabbits, dogs, and pigs, have been explored to assess the potential of magnesium-based materials. Moreover, strategies such as alloying and coating have been identified to enhance the degradation rate of magnesium-based materials in vivo to transform these challenges into opportunities. This review aims to explore the utilization of Mg implants across various biomedical applications within cellular (in vitro) and animal (in vivo) models.

Survival and Success of Dental Implants in Patients with Autoimmune Diseases: a Systematic Review

Objectives

The purpose of this systematic review is to disclose the impact of autoimmune diseases and their medical treatment on dental implant survival and success.

Material and Methods

A literature search was conducted using MEDLINE (PubMed), The Cochrane Library and Embase up to December 6th, 2021. Any clinical study on patients with an autoimmune disease in whom implant therapy was performed was eligible. The quality of included studies was assessed using the Newcastle-Ottawa Scale. For each autoimmune disease group, data synthesis was divided into three groups: 1) overall results of the autoimmune disease, 2) overall results of corresponding control groups and 3) overall results of the autoimmune disease with a concomitant autoimmune disease (a subgroup of group 1). Descriptive statistics were used.

Results

Of 4,865 identified articles, 67 could be included and mainly comprising case reports and retrospective studies with an overall low quality. Implant survival rate was 50 to 100% on patient and implant level after a weighted mean follow-up of 17.7 to 68.1 months. Implant success was sporadically reported. Data on immunosuppressive medication were too heterogeneously reported to allow detailed analysis.

Conclusions

Overall, a high implant survival rate was reported in patients with autoimmune diseases. However, the identified studies were characterized by a low quality. No conclusions could be made regarding implant success and the effect of immunosuppressants due to heterogeneous reporting.

Implant-supported prostheses in patients with special needs: A systematic literature review of protocols and outcomes

STATEMENT OF PROBLEM

Implant-supported prostheses for individuals with special needs have been considered high-risk because of the prevalence of poor oral hygiene and parafunction in this population; however, systematic evidence on protocols and outcomes is scarce.

PURPOSE

The purpose of this systematic review was to summarize implant procedures and outcomes for the special needs population.

MATERIAL AND METHODS

A systematic literature review was conducted using PubMed, Embase, and Scopus databases in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodological framework. Data collected included patient information, location and number of implants, surgical and prosthetic procedures, behavior support techniques, postoperative follow-up, complications, and criteria for outcome assessment. Survival rates were pooled for quantitative analysis, and a separate analysis pooling data from groups of special needs patients that present similar risks was undertaken.

RESULTS

The qualitative analysis included 79 studies involving 759 patients and 2269 implants. The quantitative analysis included 65 studies concerning 666 patients with special needs and 1886 implants. The overall weighted implant survival rate was 95.4%, with a mean follow-up duration of 40.7 ±29.5 months.

CONCLUSIONS

In individuals with special needs considered suitable for implant-supported prostheses by their clinician, implant survival rates were similar to those reported in the general population. (J Prosthet Dent xxxx;xxx:xxx-xxx).

Design of patient specific basal dental implant using Finite Element method and Artificial Neural Network technique

The bone conditions of mandibular bone vary from patient to patient, and as a result, a patient-specific dental implant needs to be designed. The basal dental implant is implanted in the cortical region of the bone since the top surface of the bone narrows down because of aging. Taguchi designs of experiments technique are used in which 25 optimum solid models of basal dental implants are modeled with variable geometrical parameters, viz. thread length, diameter, and pitch. In the solid models the implants are placed in the cortical part of the 3D models of cadaveric mandibles, that are prepared from CT data using image processing software. Patient-specific bone conditions are varied according to the strong, weak, and normal basal bone. A compressive force of 200 N is applied on the top surface of these implants and using finite element analysis software, the microstrain on the peri-implant bone ranges from 1000 to 4000 depending on the various bone conditions. According to the finite element data, it can be concluded that weak bone microstrain is comparatively high compared with normal and strong bone conditions. A surrogate artificial neural network model is prepared from the finite element analysis data. Surrogate model assisted genetic algorithm is used to find the optimum patient-specific basal dental implant for a better osseointegration-friendly mechanical environment.