Background: The main goal of orthodontic debonding is to restore the enamel surface as closely as possible to its pretreatment condition without iatrogenic damage. This study aimed to compare the effects of different adhesive removal burs; zirconia burs, tungsten carbide burs, and white stone burs on enamel surface roughness. Materials and methods: Total sample of 72 extracted premolars was randomly divided into three equal groups (n = 24) depending on the method of adhesive removal: zirconia burs (ZB); tungsten carbide burs (TC); and white stones (WS). The metal brackets were bonded using Transbond XT orthodontic adhesive (3 M Unitek, Monrovia, CA, USA) and debonded after 24 h using a debonding plier, then the ARI was assessed. The adhesive remnants were removed using the different burs and Final polishing was performed using Sof-lex discs and spirals. Thirteen samples from each group were evaluated using a Mitutoyo SJ-210 profilometer to determine average surface roughness (Ra) and three samples from each group were examined under Scanning Electron Microscopy (SEM) to determine EDI score. The evaluations were performed at three time points; before bonding (T0), after adhesive removal (T1) and after polishing (T2) and the time consumed for adhesive removal by burs was recorded in seconds. The data were analyzed statistically by ANOVA, Tukey’s test and Kruskal–Wallis H-test. Results: Kruskal–Wallis H-test showed no statistically significant difference of ARI in all studied groups (p = 0.845) and two-way mixed ANOVA revealed that all burs significantly increased surface roughness at T1 compared to T0 (p < 0.001) in all groups with the lowest Ra values were observed in the ZB group, followed by the TC group, and WS group. The fastest procedure was performed with WS, followed by ZB, then TC bur (p < 0.001). After polishing (T2), Ra values showed no significant difference in ZB group (P = 0.428) and TC group (P = 1.000) as compared to T0, while it was significant in WS group (p < 0.001). Conclusion: zirconia bur was comparable to tungsten carbide bur and can be considered as alternative to white stone which caused severe enamel damage. The polishing step created smoother surface regardless of the bur used for resin removal.

During the current pandemic of COVID-19, numerous manifestations and complications have developed. Patients with COVID-19 are at high risk of fungal infections, such as mucormycosis, that may result directly from COVID-19 infection and/or as a side effect of the drugs used in COVID-19 treatment protocol, such as dexamethasone, hydroxychloroquine, and antibiotics. In this report, we described a series of 14 cases with maxillary mucormycosis osteomyelitis in immediate post-COVID-19 patients.

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

Background: Diabetes is a chronic metabolic disease that causes serious consequences in different organs such as the heart, kidneys, pancreas, and liver. Metformin (MTF) is a common treatment for type 2 diabetes. It controls the blood glucose level by improving insulin sensitivity and glucose absorption. MTF belongs to BCS class III, which is characterized by high solubility and low permeability. Several types of nanoparticles have been developed to overcome the permeability problem of MTF. Methods: In this study, we prepared nanostructured lipid carriers (NLCs) loaded with metformin (MTF) using hot melt homogenization-ultrasonication. To select the best formulation, the prepared MTF-NLCs were evaluated for entrapment efficiency % (EE%), particle size, zeta potential, polydispersity index (PDI), and in vitro drug release. The optimized formulation was selected based on the high EE%, small particle size, high absolute zeta potential, low polydispersity index, and high in vitro drug release. The optimized formulation was evaluated for surface morphology by transmission electron microscope (TEM) and for further biochemical and histological analyses in a high-fat diet-induced type 2 diabetes mellitus (T2DM) in vivo rat model; HFD was administered (44.3-kJ/kg total energy) for four weeks, followed by a single intraperitoneal injection of streptozotocin (STZ). Rats were allocated into four groups; Diabetic (DM), DM+MTF, DM+MTF-NLC, and control group. Serum and tissue samples were processed for inflammatory markers detection and histopathology. Results: The prepared MTF-NLC formulation exhibited high EE% (80.65 ± 1.95% to 99.31 ± 3.25%), small particle size (247.72± 5.74nm-503.23± 7.26nm), high negative zeta potential (from − 31.83‎±‎ 0.98mV to − 51.6‎±‎ 2.64mV), PDI value less than 0.5 for all MTF-NLCs, and controlled drug release. MTF-NLC7 appeared spherical when examined by TEM. MTF and MTF-NLC groups significantly alleviated the degenerative effects of DM in both submandibular glands (SMG) and pancreas. Additionally, treatments improved kidney and liver function reduced serum inflammatory cytokines, and tissue SMG and pancreatic immunostaining of inflammatory cytokines with favorable effects of MTF-NLCs. Moreover, the MTF-NLCs showed a significant reduction of serum inflammatory cytokines, including (TNF-α and IL-1β) and pancreatic TNF-α expression, in addition to ameliorating liver and renal functions compared to MTF alone. Conclusion: The preparation of MTF as NLCs improved its permeability, enhancing its anti-inflammatory activity and providing more protection against diabetes-induced organ injury.