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Rikke Nørgaard Pedersen,1 Buket Öztürk,1 Lene Mellemkjær,2 Søren Friis,2 Trine Tramm,3 Mette Nørgaard,1 Deirdre P Cronin-Fenton1 1Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 2Danish Cancer Society Research Center, Copenhagen, Denmark; 3Department of Pathology, Aarhus University Hospital, Aarhus, DenmarkCorrespondence: Rikke Nørgaard PedersenDepartment of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkTel +4587167212Email rikp@clin.au.dkPurpose: About 70% of women with breast cancer survive at least 10 years after diagnosis. We constructed an algorithm to ascertain late breast cancer recurrence—which we define as breast cancer that recurs 10 years or more after primary diagnosis (excluding contralateral breast cancers)—using Danish nationwide medical registries. We used clinical information recorded in medical records as a reference standard.Methods: Using the Danish Breast Cancer Group clinical database, we ascertained data on 21,134 women who survived recurrence-free 10 years or more after incident stage I–III breast cancer diagnosed in 1987– 2004. We used a combination of Danish registries to construct the algorithm—the Danish National Patient Registry for information on diagnostic, therapeutic and procedural codes; and cancer diagnoses from the Danish Pathology Registry, the Danish Cancer Registry and the Contralateral Breast Cancer database. To estimate the positive predictive value (PPV), we selected 105 patients who, according to our algorithm, had late recurrence diagnosed at Aarhus University Hospital. To estimate the sensitivity, specificity and negative predictive value (NPV), we selected 114 patients diagnosed with primary breast cancer at Aalborg University Hospital. We abstracted clinical information on late recurrence for patients with medical record-confirmed late recurrence at Aarhus University Hospital.Results: Our algorithm had a PPV of late recurrence of 85.7% (95% CI: 77.5– 91.3%), a sensitivity of 100.0% (95% CI, 39.8– 100.0%), a specificity of 97.3 (95% CI, 92.2– 99.4) and a NPV of 100% (95% CI, 96.6– 100.0%).Conclusion: Our algorithm for late recurrence showed a moderate to high PPV and high sensitivity, specificity and negative predictive value. The algorithm could be an important tool for future studies of late breast cancer recurrence.Keywords: algorithm, late breast cancer recurrence, breast cancer neoplasm, PPV, sensitivity

Jakob Kristian Jakobsen,1 Buket Öztürk,2 Mette Søgaard2 1Department of Urology, 2Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus Nord, Denmark Aim of database: The Danish National Penile Cancer Quality database (DaPeCa-data) aims to improve the quality of cancer care and monitor the diagnosis, staging, and treatment of all incident penile cancer cases in Denmark. The aim is to assure referral practice, guideline adherence, and treatment and development of the database in order to enhance research opportunities and increase knowledge and survival outcomes of penile cancer. Study population: The DaPeCa-data registers all patients with newly diagnosed invasive squamous cell carcinoma of the penis in Denmark since June 2011. Main variables: Data are systematically registered at the time of diagnosis by a combination of automated data-linkage to the central registries as well as online registration by treating clinicians. The main variables registered relate to disease prognosis and treatment morbidity and include the presence of risk factors (phimosis, lichen sclerosus, and human papillomavirus), date of diagnosis, date of treatment decision, date of beginning of treatment, type of treatment, treating hospital, type and time of complications, date of recurrence, date of death, and cause of death. Descriptive data: Registration of these variables correlated to the unique Danish ten-digit civil registration number enables characterization of the cohort, individual patients, and patient groups with respect to age; 1-, 3-, and 5-year disease-specific and overall survival; recurrence patterns; and morbidity profile related to treatment modality. As of August 2015, more than 200 patients are registered with ~65 new entries per year. Conclusion: The DaPeCa-data has potential to provide meaningful, timely, and clinically relevant quality data for quality maintenance, development, and research purposes. Keywords: penile cancer, squamous cell carcinoma of the penis, clinical quality, national database, epidemiology, treatment

Wineries always try different types of marketing strategies such as tasting rooms, promotion through the social media or websites. The main goal is to establish a way to win the consumers' loyalty and to keep them coming back or consuming their brand. In the last decades, the influences of the front and back label on consumers have largely been started to investigate. In this study, the wine bottles produced in the wineries located on Urla vineyard road were investigated in terms of their visual codes. The visual codes were identified in four classes: layouts and composition, typography, illustrations, and colours. Each label investigated based on the semiotics analyses. The findings suggested that the wine labels convey the feeling of tradition, seniority, elegance and history.

Micronized zircon (ZrSiO4) is used as an opacifier in the production of engobes, opaque glazes, and floor tile bodies. Elevated price levels incentivize researchers to develop alternative engobe formulations with reduced zircon ratios. In this study, new engobes with low zircon content were developed with varying SiO2/Al2O3 mixtures. The effect of the SiO2/Al2O3 ratio on the performance and opacity of the wall and floor tile engobes was investigated. It has been observed that the amount of zircon in the sample compositions could be reduced up to 40% by optimizing the refractoriness-water absorption balance of the engobe with the SiO2/Al2O3 ratio while keeping the opacity in the preferred levels. The color difference (E) value relative to the standard surface was below 1, which is acceptable for visual perception for all the samples. Other measurements such as water absorption and firing shrinkage of the floor tile samples were found to be compatible with the standard composition. [ABSTRACT FROM AUTHOR]

Air pollution is extremely harmful to human health, ecosystems and the climate. In this context, the level of particulate matter at high concentrations is one of the most important factors in determining air quality. Numerous studies have been carried out to solve the air quality problem related to PM concentrations. It is important to note that the sources of pollution have a local character for each urban area and depend on many factors such as meteorological, transportation and other air quality criteria. In this study, particulate matter with an aerodynamic diameter smaller than 10 μm (PM10) is mathematically modeled with MARS and XGBoost algorithms in Iğdır Province of Turkey. For this purpose, in addition to meteorological data such as wind speed, wind direction, relative humidity, temperature, atmospheric pressure, precipitation, evaporation and global solar radiation, air quality criteria such as SO2, NOx and O3 were used and modeled with the aforementioned algorithms. As a result, the XGBoost algorithm can be recommended for modeling the atmospheric PM10 concentration. [ABSTRACT FROM AUTHOR]

Background/Aim: The maxillofacial region is a complex of anatomical structures that are in close proximity and connection with each other. The aim of this study was to determine the shape, depth, width, length and palatal vault angle of the palatal arch on cone beam computed tomography (CBCT) images taken for various reasons and to determine the relationship between these factors and maxillary sinus volume. Material and Methods: CBCT images of a total of 150 male and female patients over the age of 18, taken for different diagnostic purposes, were evaluated by an oral radiologist with two years of experience. The shape of the palatal arch was determined on each image, and palatal depth, width, length and palatal vault angle were measured. ITK-SNAP (version 3.8.0) software was used for maxillary sinus measurement. Statistical analysis of the study was performed using SPSS v.21 software. Results: All of the measured parameters were found to be statistically significant between genders, except for the palatal vault angle. As a result of the correlation analysis, it was shown that palatal length was 0.4% (R2=0.004), palatal depth was 42.3% (R2=0.423), and palatal width was 2%(R2=0.020) effective in explaining the palatal vault angle. A high level of correlation was detected between the right (1) and left (2) sinus maxillaris volumes and total sinus maxillaris volumes (r1=0.959, r2=0.961). Conclusions: There is a significant difference in palatal arch parameters between different genders. In addition, these dimensions also play a role on maxillary sinus volumes.

Background/Aim: The temporomandibular joint (TMJ) is a complex anatomical region composed of the mandibular condyle located in the glenoid fossa of the temporal bone and covered with fibrous connective tissue. Excessive and continuous forces lead to progressive degeneration of the bony surfaces of the TMJ. The aim of this study is to determine the success of automatic detection of degenerative changes detected on panoramic radiographs in the TMJ region with deep learning method. Material and Methods: Panoramic images of 1068 patients (1000 with normal TMJ appearance and 68 with TMJ degeneration) over 18 years of age were included in the study. CVAT, open-source annotation tool (https://www.cvat.ai/) was used for labeling image data. All images were resized using the bilinear interpolation method. With the using data augmentation techniques, the number of images data reached 1480. BSRGAN model was applied to the data to increase the resolution of the data. YOLOv5, YOLOv7 and YOLOv8 algorithms were used for TMJ degeneration detection. TP, FP, TN, FN, accuracy, precision, recall, F1-score and AUC (Area Under the Curve) metrics were used for statistical analysis. Results: YOLOv5s training resulted in 94.40% accuracy, 81.63% precision, 86.96% sensitivity, 84.21% F1 score and 91.45% AUC. YOLOv7 training resulted in 99.63% accuracy, 97.87% precision, 100% sensitivity, 98.92% F1 Score and 99.77% AUC. YOLOv8 training resulted 96.64% accuracy, 91.11% precision, 89.13% sensitivity, 90.11% F1 Score and 93.66% AUC. Conclusions: All three algorithms have high success rates, with the best results obtained in YOLOv7.