Many factors may affect periodontal changes during the physiologic conditions of woman(e.g. puberty, menstrual cycle, pregnancy, menopause). Recently many research has focused on the immunological changes of host, but the exact mechanism is not clear. Collagen is a major constituent of periodontium, and collagenase specifically digests the collagen and plays a role in destruction of periodontal tissue. So, I suppose that it participates with the cytokines in the inflammation of gingiva and vascular response during the changes of female sex hormones. Because there are some evidences of the existence of the receptors of estrogen and progesterone in the gingiva, it may be a target tissue of female sex hormones. In this experiment, gingival fibroblast and periodontal ligament cell were cultured in the presence of various concentrations of estrogen or progesterone corresponding to the menstrual cycle and pregnancy. Collagenase activity of the supernatant of culture media was determined by Spectrophotometric collagenase assay. The enzyme activity was calculated by the % decrease of the coated collagen. 1. The estrogen at both concentrations had no effect on the activity of collagenase of the gingival fibroblast. 2. The progesterone had some effect on the collagenase activity of the gingival fibroblast at low and high concentration of menstrual cycle, and elevated the enzyme activity at all range of pregnancy concentrations. 3. In periodontal ligament cells, estrogen elevated the enzyme activity at the early pregnancy concentration and progesterone elevated at the concentration just before menstruation. In this experiment, progesterone elevated the collagenase activity of gingival fibroblast and periodontal ligament cells. But the mechanism of the up-regulation of the enzyme activity was not confirmed. The more experiments of direct effect of progesterone on gingival at the molecular level(e.g. northern blot analysis) can reveal the exact mechanism.
Adolescent ; Blotting, Northern ; Collagen ; Collagenases* ; Culture Media ; Cytokines ; Estrogens ; Female* ; Fibroblasts* ; Gingiva ; Gonadal Steroid Hormones* ; Humans ; Inflammation ; Menstrual Cycle ; Menstruation ; Periodontal Ligament* ; Periodontium ; Pregnancy ; Progesterone ; Puberty ; Up-Regulation

The oral cavity is easily accessible for direct exposure of a malignant disease. 1 percent of the oral malignant tumors are of metastatic origin and approximately 10 percent to 25 percent of the 1 percent fraction originate from the lungs. A case of metastatic lung carcinoma to the gingiva in a 88-year-old male is reported. He complained of pain and swelling between right maxillary 1st premolar and 2nd molar. Although surgical excision of the lesion has been done, the gingival lesion developed as a quickly growing mass and recurred 2 weeks after surgical excision. The gingival mass was histopathologically diagnosed as an undifferentiated carcinoma. Epithelial layer was continuous without ulceration and it seems that the cancer cells are originated from primary tumor. Infiltrated cancer cells were pleomorphic and dyskeratotic. The cells had 2 or more nuclei, not showing squamous or glandular differentiation. Immunohistochemical study revealed the cells originated from the epithelial cells. The prognosis is poor, because prognosis depends on surgical elimination of the primary tumor.
Aged, 80 and over ; Bicuspid ; Carcinoma ; Epithelial Cells ; Gingiva ; Humans ; Lung Neoplasms ; Lung* ; Male ; Molar ; Mouth ; Neoplasm Metastasis ; Periodontium* ; Prognosis ; Ulcer

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning.RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively.CONCLUSIONS: ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.
Classification ; Dataset ; Diagnosis ; Humans ; Lacrimal Apparatus Diseases ; Learning ; Logistic Models ; Machine Learning ; Nuclear Medicine ; Programming Languages ; Radionuclide Imaging

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. CONCLUSIONS ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.