Tehran University Medical Journal

Background: Association of Addison's disease with connective tissue diseases such as scleroderma and Sjogren have been rarely reported. Anti-centromere antibody (ACA) has been associated with exocrine gland dysfunction in anti-Ro, anti-La negative Sjogren’s syndrome and may be one of the causes of xerostomia in community. The purpose of this article was to introduce a rare case of scleroderma-Sjogren intermediate phenotype with positive anti-centromere antibody in a known case of Addison’s disease admitted for dental caries and xerostomia.
Case Presentation: A 29-year-old woman with Addison’s disease referred to a dental clinic due to recurrent dental caries. Addison’s disease was confirmed by low basal serum cortisol level and unresponsive serum cortisol level to adrenocorticotropin hormone (rapid ACTH stimulation test). Signs of xerostomia, xerophthalmia, Raynaud’s phenomenon, gastro-esophageal reflux, masked face, osteoporosis, positive anti-centromere antibody, negative anti-Ro and anti-La antibodies and failure to match the pathology of the minor salivary gland of lip with Sjogren's disease were found. The diagnosis of scleroderma-Sjogren intermediate phenotype was raised with considering some of the symptoms of scleroderma and Sjogren and not the exact classification criteria for each of these two diseases. Hydroxychloroquine, fluoride and Biotene® mouthwash (Laclede, Inc., CA, USA) (oral moisturizing saliva), chewing gum containing xylitol plus artificial tear droplet was prescribed. Drinking plenty of fluids was recommended. Due to gastroesophageal reflux and osteoporosis, Pantoprazole and CinnoPar® (Cinnagen, Iran) (parathyroid hormone analogue) plus calcium and vitamin D supplements was administered. Regarding adrenal insufficiency, Prednisolone and Fludrocortisone were continued.
Conclusion: For evaluation of recurrent dental caries, especially in patients with autoimmune disease, anti-centromere antibody may be useful to identify the cause of dry mouth, as well as early detection of limited scleroderma or scleroderma-Sjogren intermediate phenotype.

Background: One of the most significant factors in the success of dental implant procedures, can be mentioned by the quality and quantity of jaw bone. The occurrence of some problems such as trauma, infection, pathological lesions and the long-term absence of teeth in patients, it causes irregularities in the jaw bone and can get bone resorption. Sever defects after trauma or tumor resection needs bone reconstruction. Sticky bone is a new biological agent that provides stabilization of bone graft in the defect, and therefore, ameliorates tissue repairing and decreases bone loss during healing period. In this study, the evaluation of sticky bone performance to reconstruction of defects in two patients jaw's bone was considered.
Case Presentation: Two patients (1 male, 1 female) with an average age of 50 years underwent surgery, due to the history of tooth extraction. They had resorption of jaw bone for implant surgery. The teeth were 11 and 37. This study was performed in Al-Mortaza's Clinic, Qom province, Iran, from May 2016 to January 2017. 10 ml of blood were taken individually and centrifuged at 1300 revolutions per minute (rpm) for 8 minutes to separate the platelet-rich fibrin. Then, platelet-rich fibrin was combined with allogeneic bone to form sticky bone. Sticky bone prepared during implant surgery, with implant inside the patient's jaw bone was used.
Conclusion: The use of sticky bone to stimulate and induce bone resorption in toothless area was associated with increased implant's success. Sticky bone due to multiple growth factors, such as TGF-β1 and VEGF, usability is an appropriate and efficiency method for stimulation of bone resorption.

With the rapid expansion of artificial intelligence across clinical disciplines, a variety of artificial neural networks (ANNs) have become indispensable tools for endowing computer systems with advanced analytical power. Dentistry, as an information‑rich branch of medicine, routinely generates and must interpret large, complex datasets from imaging and diagnostic records. Consequently, researchers have increasingly directed their attention toward intelligent, automated techniques for analyzing dental data. This study therefore surveys and synthesizes the methods that have been applied to the intelligent and automated analysis of such data, highlighting the prevailing trends in current literature.The majority of the examined investigations relied on panoramic radiographic images of the teeth orthopantomograms (OPG) as their primary source material. Three overarching technical objectives repeatedly emerged: first, tooth diagnosis, meaning the reliable separation and identification of each individual tooth from its neighbors; second, sample segmentation, that is, the piece‑by‑piece analysis of visual information within the image; and third, semantic segmentation, namely, the contextual interpretation of information extracted from the radiograph. Depending upon which of these objectives was pursued, researchers selected different neural‑network architectures and configurations. Across the reviewed corpus, input images were typically subjected to preprocessing steps such as normalization, noise reduction, and contrast enhancement before being supplied to a neural network for training, thereby preparing the data for subsequent machine interpretation. In several instances, the raw output produced by the neural network underwent additional post‑processing, a stage designed to refine the preliminary results and enhance overall accuracy. The comparative analysis presented here concentrates on how effectively the various neural‑network models fulfilled the three technical objectives described above. The surveyed articles reveal two dominant analytical approaches. In the intelligent problem‑solving paradigm, convolutional neural networks (CNNs) overwhelmingly predominate. Conversely, in the automated paradigm, investigators favor classical, non‑learning algorithmic techniques. Work employing ANNs consistently emphasizes image comprehension, segmentation, feature extraction, feature classification, network modeling, and careful variable tuning to promote effective learning that aligns with each study’s stated objectives.