Keratinized mucosa changes around one-stage implants: A prospective case series

Designing stimuli-responsive upconversion nanoparticles based on a mimetic immunoassay for potential accurate diabetic nephropathy diagnosis

Diabetic nephropathy (DN) is the most common microvascular complication associated with incurable diabetes. The gold standard diagnostic method for DN is based on the detection of proteinuria but it overlooks cases of non-proteinuria (NP-DN). To address this limitation, urinary sialic acid (SA) has been confirmed as an effective biomarker for various DNs. Herein, we constructed an ultrasensitive non-proteinuria assay platform to accurately diagnose DN within 20 min. This platform utilized the ninhydrin reaction between acidic ninhydrin and urinary sialic acid (SA) as an effective biomarker for various DNs. A compound with a maximum absorption peak at 470 nm was produced in this reaction and contributed to the fluorescence decrease of the blue-emission core-shell upconverting nanoparticles through the inner filter effect (IFE). By integrating the inner filter effect (IFE) with a mimetic immunoassay, the imperceptible color was converted into highly sensitive fluorescence signals. This protocol shows a stable and high sensitivity with a detection limit of 20 nmol L-1 and provides 100% positive prediction for urine samples, demonstrating its potential for clinical diagnosis and long-term monitoring of DN.

Scanning accuracy and scanning area discrepancies of intraoral digital scans acquired at varying scanning distances and angulations among 4 different intraoral scanners

STATEMENT OF PROBLEM

The accuracy of intraoral scanners (IOSs) can be affected by operator handling; however, the scanning area and accuracy discrepancies acquired at different scanning distances and angulations among IOSs remain uncertain.

PURPOSE

The objective of this in vitro study was to compare the scanning area and scanning accuracy of the intraoral digital scans obtained at 3 scanning distances with 4 different scanning angulations among 4 different IOSs.

MATERIAL AND METHODS

A reference device (reference file) was designed with 4 inclinations (0, 15, 30, and 45 degrees) and printed. Four groups were created based on the IOS: i700, TRIOS4, CS 3800, and iTero scanners. Four subgroups were generated depending on the scanning angulation (0, 15, 30, and 45 degrees). Each subgroup was divided into 3 subgroups based on the scanning distance: 0, 2, and 4 mm (N=720, n=15). The reference devices were positioned in a z-axis calibrated platform for standardizing the scanning distance. In the i700-0-0 subgroup, the 0-degree reference device was positioned in the calibrated platform. The wand of the IOS was positioned in a supporting framework with a 0-mm scanning distance, and the scans were acquired. In the i700-0-2 subgroup, the platform was lowered for a 2-mm scanning distance followed by the specimen acquisition. In the i700-0-4 subgroup, the platform was further lowered for a 4-mm scanning distance, and the scans were obtained. For the i700-15, i700-30, and i700-45 subgroups, the same procedures were carried out as in the i700-0 subgroups respectively, but with the 10-, 15-, 30-, or 45-degree reference device. Similarly, the same procedures were completed for all the groups with the corresponding IOS. The area of each scan was measured. The reference file was used to measure the discrepancy with the experimental scans by using the root mean square (RMS) error. Three-way ANOVA and post hoc Tukey pairwise comparison tests were used to analyze the scanning area data. Kruskal-Wallis and multiple pairwise comparison tests were used to analyze the RMS data (α=.05).

RESULTS

IOS (P<.001), scanning distance (P<.001), and scanning angle (P<.001) were significant factors of the scanning area measured among the subgroups tested. A significant group×subgroup interaction was found (P<.001). The iTero and the TRIOS4 groups obtained higher scanning area mean values than the i700 and CS 3800 groups. The CS 3800 obtained the lowest scanning area among the IOS groups tested. The 0-mm subgroups obtained a significantly lower scanning area than the 2- and 4-mm subgroups (P<.001). The 0- and 30-degree subgroups obtained a significantly lower scanning area than the 15- and 45-degree subgroups (P<.001). The Kruskal-Wallis test revealed significant median RMS discrepancies (P<.001). All the IOS groups were significantly different from each other (P<.001), except for the CS 3800 and TRIOS4 groups (P>.999). All the scanning distance groups were different from each other (P<.001).

CONCLUSIONS

Scanning area and scanning accuracy were influenced by the IOS, scanning distance, and scanning angle selected to acquire the digital scans.

The keratinized mucosa width at partially edentulous molar sites and its associated factors: a pilot study

BACKGROUND

Assessment of the keratinized mucosa width (KMW) at edentulous sites is important for the subsequent implant treatment design. This pilot study aimed to evaluate the characteristics of the KMW at edentulous molar sites and explore the associated factors.

METHODS

A total of 150 patients with 222 edentulous molar sites were included. The buccal KMW of the edentulous molar sites was measured during implant treatment planning. Potentially associated factors, including age, sex, smoking status, location, reasons for tooth loss/extraction, gingival phenotype (GP) and keratinized gingival width (KGW) of the adjacent teeth, were collected and analyzed. The Shapiro‒Wilk test, Student's t test, one-way ANOVA, generalized estimation equations (GEEs) and linear regression analysis were used for data analysis at α = 0.05.

RESULTS

The buccal KMW at edentulous molar sites was 3.97 ± 2.06 mm, and 41.9% of sites presented with KMW < 4 mm. The mean KMWs of the maxillary sites were significantly higher than that those of the mandibular sites (4.96 ± 2.05 mm vs. 3.41 ± 1.85 mm, respectively). In total, 54.7%, 46.5%, 29.8%, and 0.0% of mandibular first and second molar sites and maxillary first and second molar sites, respectively, displayed a KMW of < 4 mm. Statistically significant linear correlations were found between KMW and GP (r = 0.161, p = 0.025) and between KMW and KGW of the adjacent teeth (r = 0.161, p = 0.023), while other factors were found to have no significant association.

CONCLUSION

Within the limitations of the present study, the KMW at edentulous molar site was related to the location of molar tooth loss/extraction. The GP and KGW of the adjacent teeth of edentulous molar sites were also associated with their KMW, which was probably attributed to the continuity of the adjacent soft tissue.

Three interfaces of the dental implant system and their clinical effects on hard and soft tissues

Anatomically, the human tooth has structures both embedded within and forming part of the exterior surface of the human body. When a tooth is lost, it is often replaced by a dental implant, to facilitate the chewing of food and for esthetic purposes. For successful substitution of the lost tooth, hard tissue should be integrated into the implant surface. The microtopography and chemistry of the implant surface have been explored with the aim of enhancing osseointegration. Additionally, clinical implant success is dependent on ensuring that a barrier, comprising strong gingival attachment to an abutment, does not allow the infiltration of oral bacteria into the bone-integrated surface. Epithelial and connective tissue cells respond to the abutment surface, depending on its surface characteristics and the materials from which it is made. In particular, the biomechanics of the implant-abutment connection structure (i.e., the biomechanics of the interface between implant and abutment surfaces, and the screw mechanics of the implant-abutment assembly) are critical for both the soft tissue seal and hard tissue integration. Herein, we discuss the clinical importance of these three interfaces: bone-implant, gingiva-abutment, and implant-abutment.