Detection of human papillomavirus DNA sequences in oral lesions using polymerase chain reaction

The purpose of the present study was to estimate the frequency of HPV DNA in four groups of oral lesions, including oral squamous cell carcinoma. Sixty paraffin-embedded oral tissue samples were examined for the presence of HPV DNAs using the PCR technique. These specimens were obtained from patients with oral squamous cell carcinoma (OSCC), leukoplakia, oral lichen planus (OLP), and pyogenic granuloma (PG). Consensus primers for L1 region (MY09 and MY11) and specific primers were used for detection of HPV DNA sequences in this study. we detected HPV DNA in 60% (9 out of 15) of OSCCs, 26.7% (4 out of 15) of leukoplakia, 13.3% (2 out of 15) of OLPs, and 6.7% (1 out of 15) of PGs. Statistical analysis showed that the prevalence of HPV in OSCC was significantly higher than other groups (P < 0.05). The frequency of HPV-16 and 18 detection in OSCC samples were 40% and 20%, respectively. The prevalence of these high risk HPVs was significantly higher in OSCC group (P < 0.05). The results of the present study show a successive increase of detection rate of HPV-16 and 18 DNAs from low level in samples of pyogenic granuloma and non-premalignant or questionably premalignant lesions of OLP to premalignant leukoplakia and to OSCC. © 2007 Tehran University of Medical Sciences. All rights reserved

Indexing of Iranian Publications in Well-known Endodontic Textbooks: A Scientometric Analysis

Introduction: Quoting an article in well-known textbooks is held as a credit for that paper. The numbers of Iranian publications mentioned in endodontic textbooks have increased during recent years. The aim of this investigation was to evaluate the number of Iranian articles quoted in eminent endodontic text books. Methods and Materials: Three known textbooks (Ingle’s Endodontics, Seltzer and Bender’s Dental Pulp and Cohen’s Pathways of the Pulp) were chosen and all the editions of the textbooks since 2000 were investigated for quoted Iranian publications. Only Iranian authors with affiliations from a domestic university were chosen. All references at the end of each chapter were read by hand searching, and results were noted. The trend and percentage of Iranian publications in different editions of the textbooks were also calculated. The number of citations of these publications in Google Scholar and Scopus databases were also obtained. Results: The number of Iranian publications in all well-known textbooks have notably increased since 2000. The number and percentage of Iranian publications in the latest edition of Cohen’s Pathways of the Pulp was higher compared to other textbooks as well as the previous edition of the same text. Conclusion: Number and percentage of Iranian publications in the field of endodontics in all three textbooks have remarkably increased since 2000.Keywords: Dental Pulp; Endodontics; Index; Ingle; Iranian Publications; Pathways of the Pulp; Quote; Scientometric; Textbook

DESIGN AND f-ABRIC AT! ON OF A SURFACE pH SENSOR FOR A REALTIME CORROSION MONITORING

The aim of this research is to design and fabricate a new pi I sensor that can be used to measure hydrogen ion activity on corroding metal surface in bulk solution or under deposit. Iridium oxide (lrOx) is used for proposed design as pH sensitive material. IrOx is a potentiometric sensor which its open circuit potential regarding a reference electrode is representative of pi I. The electrodcposition method of cyclic voltammetry approach is used for coating of IrOx on stainless steel substrate. The effects of scan rate. temperature. and number of cycles on the coating thickness of IrOx elcctrodeposited on a stainless steel substrate v.cre investigated in a statistical system. The central composite design. combined with response surface methodology, was used to study condition of electrodeposition

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Prevalence of Toothache and Associated Factors: A Population-Based Study in Southeast Iran

Introduction: This study was carried out to estimate toothache prevalence among adult residents in Kerman. Materials and Methods: This cross-sectional, population-based study was conducted among individuals aged over 18 years (n=1800). The relevant data on the prevalence of toothache and associated factors were collected by interviewing the individuals in their homes and filling out a questionnaire designed by the examiners. Prevalence of toothache and associated factors that patients recalled previous to their interview were analyzed by chi-square test and multivariate logistic regression analysis. Results: Nine hundred ninety-one individuals (55.1%) reported toothache during the 6 months before the interview. The participants who flossed daily, had regular dental visits, and had higher education showed a significantly lower prevalence of toothache (P&lt;0.05), whereas regular tooth brushing and economic level of residency had no significant effect on the prevalence of toothache. Individuals between the ages of 26 and 45 [odds ratio (OR)=2.0], with a family size of more than 4 (OR=1.5), not using dental floss (OR=1.5), or having a mental or psychological illness (OR=1.5) were more likely to have a history of toothache. Conclusion: High prevalence of toothache (more than half) among residents of Kerman shows a serious and major public health problem. Toothache prevalence in middle aged adults, lower education, bigger family size, lower dental hygiene habit and/or those having mental or psychological illness were more common in the city of Kerman

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Distribution of Total Volatile Organic Compounds at taxi drivers in Tehran

Air pollution is currently the most serious environmental health threat worldwide. Volatile Organic Compounds (VOC) are considered as the main effective factors in causing air pollution. Vehicles are among the major sources which emit these compounds, so it seems that automobiles’ microenvironment is one of the places where people are exposed to high concentration of VOC. Evaluating the exposure amount of Total Volatile Organic Compounds (TVOC) can indeed be used as an indicator to estimate the amount of exposure to every individual VOC. This study was conducted on the concentration of TVOC inside Tehran taxies for a period of one year. For this purpose, a real time instrument equipped with photo-ionization detector (PID) was used. Consequently, the highest and the lowest measured TVOC in taxies equaled 3.33 ppm and 0.72 ppm, respectively. In addition, the arithmetic mean of TVOC concentration was 1.77±0.53 ppm inside the examined taxies. In this study, the parameters like measurement time, climate and vehicle conditions were found to have significant effect on the amount of exposure to TVOC

The Frequency of Medically Compromised Patients in Endodontic Offices in Iran

Introduction: As the result of epidemiological transition and aging of Iranian population, the frequencies of systemic diseases among patients in of need endodontic treatment has increased, especially within developed cities. However, there have been no concise reports of systemic diseases in Iranian patients. Based on this need, the present investigation was conducted to assess the frequency of systemic disease among patients referred to endodontic private practice in three main cities in Iran. Materials and Methods: In a retrospective study, the frequency of systematic diseases were abstracted from the health records of patients who were referred to three private practices limited to endodontics in Kerman, Mashhad, and Tehran between 1994 to 2011. Results: Overall, 15,413 records of patients were assessed. The patterns of systematic diseases among endodontic patients in these three cities were different. The overall frequency of systemic disease in Kerman was significantly higher than two other cities (Kerman: 55.03%, Mashhad: 24.32%, Tehran: 22.16%; P&lt;0.001). The most commonly occurring diseases were cardiac disease, hypertension, allergy and neurological disorders. Conclusion: Since the number of endodontic patients with systematic diseases is considerably significant and varied, special training and educations for treatment of medically compromised patient should be considered at both post- and undergraduate training

ELULC-10, a 10 m European land use and land cover map using Sentinel and landsat data in Google Earth Engine

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.Peer ReviewedPostprint (published version