Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and after 1hr, 1day, 7days, 14days, 21days and 28days post force application. Periostrips were used for GCF collection and subsequent phosphate buffered saline (PBS) was used for immediate protein elution with centrifugal speed of 10000rpm for 5min and stored at -80°C. Protein concentration was estimated using Bradford colorimetric assay. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) was carried out to resolve the purity of proteins in the collected samples and the method of collection was validated by western immuno-blotting of alpha amylase salivary enzyme. The current collection, storage and protein extraction protocol showed the best protein recovery and purity with validated collection free of salivary contamination. In conclusion, tiny GCF volume from healthy sites and evaporation issues of such promising non-invasive fluid motivate us to investigate a standardized protocol enabling optimal preservation of GCF sample and the currently followed protocol may serve as a reference for future proteomic studies searching for GCF biomarkers in diagnosing and monitoring orthodontic tooth movement.
In this paper the process of metal ions extraction (Zn(II) and Cu(II)) was studied in PEG-KCl aqueous two phase system was investigated without using an extracting agent. The experimental runs were performance at constant temperature (25 oC), constant mixing time (30 min), and constant PH of the solution (about 3). The effect of KCl salt concentration (from 10% to 25%), volumetric phase ratio of PEG solution to KCl solution (from 0.5 to 2), and the initial metal ion concentration (from 0.25 ml to 2 ml of 1 gm/L solution) were investigated on the percent extraction of Zn(II) and Cu(II). The results indicated that the percent extraction of metal ions increase with increasing of salt concentration and phase ratio, and slightly de
... Show MoreBackground and Aim: Canine parvovirus 2 (CPV-2) is a highly contagious virus that infects wild and domestic canines. Despite the use of a routine vaccination protocol, it is endemic in Iraq. The genetic drift of CPV-2 is a major issue worldwide because it abrogates virus control. In Iraq, there is a knowledge gap regarding the genetic sequences of asymptomatic and symptomatic CPV-2 cases. Therefore, this study aimed to perform a genetic analysis of viral capsid protein 1 (VP1) and viral capsid protein 2 (VP2), two major capsid-encoding genes, to demonstrate the possible role of certain mutations in triggering infection. Materials and Methods: Symptomatic and asymptomatic cases (n = 100/each) were tested by a polymerase chain reacti
... Show MoreDiode lasers are becoming popular in periodontal surgery due to their highly absorption by pigments such as melanin and hemoglobin their weak absorption by water and hydroxyapatite makes them safe to be used around dental hard tissues. Objective: The aim of the present study was to evaluate the efficiency of diode laser in performing gingivectomy in comparison to conventional scalpel technique in patients with chronic inflammatory enlargement. Materials and methods: Thirty patients were selected for this study. All of them required surgical treatment of gingival enlargements and were randomly divided into two groups: Control group (treated by scalpel and include sixteen patients) and study group (treated with diode laser 940nm and includ
... Show MoreTo determine the expression of key epithelial–mesenchymal transition (EMT) markers in gingival tissue samples collected from patients with periodontitis.
Epithelial–mesenchymal transition is a process responsible for shifting epithelial‐phenotype to mesenchymal‐phenotype leading to loss of epithelial‐barrier function. Thus, EMT could be involved as a pathogenic mechanism in periodontitis as both conditions share common promoters and signalling pathways.
Gingival tissue samples were collected fro
Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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