Tritium contents in drinking and surface seawaters before the nuclear power plant planned in Sinop (Türkiye) and their radiological risks on human population.

This study aims to determine the background levels of tritium radioisotope in drinking and seawater samples of Sinop province before the nuclear power plant was established in Sinop. In this context, a total of 174 water samples were collected, these are as follows: nine drinking water samples from the Sinop center and districts and 165 seawater samples from the seacoast from Samsun to Kastamonu. Tritium concentrations in the collected water samples were measured by the liquid scintillation counter. The minimum detectable activity for the method used was found to be 1.48 Bq/L. The tritium concentrations of the seawater and drinking water samples were found in the range of

Deep learning for diagnosis of malign pleural effusion on computed tomography images.

BACKGROUND: The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results. METHODS: The authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test. RESULTS: Among the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%). CONCLUSION: Advances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.

Deep learning for diagnosis of malign pleural effusion on computed tomography images

Abstract Background The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results. Methods The authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test. Results Among the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%). Conclusion Advances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.

A comparison of the physicochemical properties of chestnut and highland honey: The case of Senoz Valley in the Rize province of Turkey.

The aim of this study was to determine the physicochemical parameter changes, aroma, melissopalynological properties, and heavy metal content of honey produced from different types of flora (chestnut and highland) in the Senoz Valley. For this purpose, the distribution of beehives at different elevation levels in the research area was determined by a layered random sampling method. Some characteristics of the honey samples were analyzed by standard laboratory methods. The highest average color (L and b) and the glucose, sucrose, Brix, Cd, Pb, Ni, Zn, and Cr values were found in the highland honeys; the highest color (a) and fructose, F/G ratio, proline, pH, conductivity, Fe, Cu, Al, and Mn values were found in the chestnut honeys. The difference between highland and chestnut honeys was statistically significant in terms of color (L and a), F/G ratio, proline, pH, electrical conductivity, Pb, Cu, and Mn. A total of 146 aromatic components were isolated in the chestnut and highland honeys.

Can artificial intelligence distinguish between malignant and benign mediastinal lymph nodes using sonographic features on EBUS images?

AIMS: This study aimed to develop a new intelligent diagnostic approach using an artificial neural network (ANN). Moreover, we investigated whether the learning-method-guided quantitative analysis approach adequately described mediastinal lymphadenopathies on endobronchial ultrasound (EBUS) images. METHODS: In total, 345 lymph nodes (LNs) from 345 EBUS images were used as source input datasets for the application group. The group consisted of 300 and 45 textural patterns as input and output variables, respectively. The input and output datasets were processed using MATLAB. All these datasets were utilized for the training and testing of the ANN. RESULTS: The best diagnostic accuracy was 82% of that obtained from the textural patterns of the LNs pattern (89% sensitivity, 72% specificity, and 78.2% area under the curve). The negative predictive values were 81% compared to the corresponding positive predictive values of 83%. Due to the application group's pattern-based evaluation, the LN pattern was statistically significant (p = .002). CONCLUSIONS: The proposed intelligent approach could be useful in making diagnoses. Further development is required to improve the diagnostic accuracy of the visual interpretation.

A case study on pollution and a human health risk assessment of heavy metals in agricultural soils around Sinop province, Turkey.

In the present study, the concentration levels of heavy metals such as Cr, Fe, Ni, Cu, Zn, As and Pb in soil samples collected from 88 sampling locations around Sinop Province, Turkey were measured using energy dispersive X-ray fluorescence spectroscopy (EDXRF). To interpret and to evaluate the pollution status and distribution of heavy metals in soil, metal pollution parameters such as enrichment factor (EF), geo-accumulation index (Igeo), pollution factor (CF) and pollution load index (PLI) and geo-spatial distribution patterns were used. The mean concentrations of Cr, Fe, Ni, Cu, Zn, As, and Pb were found to be 194.73, 39,848.57, 85.02, 43.19, 65.10, 5.66, and 17.01 mg/kg, respectively. Results indicated that the mean concentrations of Cr, Ni, As, and Pb exceeded the world crustal average, with the exception of Fe, Cu, and As. Multivariate analysis results showed that Cr, Ni, Zn, As, and Pb levels in the investigated region were highly influenced by anthropogenic inputs such as agricultural practices. According to the health risk assessment model introduced by USEPA to evaluate the human health risks, the non-carcinogenic risk for children was above the threshold level, but low for adults. Total potential carcinogenic health risks for both children and adults in the study area were in acceptable range. Overall, when health risks are evaluated, it shows that children are more susceptible to non-carcinogenic and carcinogenic health effects of trace metals compared to adults.

Regional distribution of genetic mutation in lung cancer in Turkey (REDIGMA).

INTRODUCTION: The results of standard chemotherapy in lung cancer are not very satisfactory, so it is important to identify genetic mutations that provide targeted therapies. Recent reports have suggested influences of racial difference on the frequency of mutation in lung cancer. We aimed to determine the frequency and regional distribution of genetic mutations of non-small cell lung cancer (NSCLC) in Turkey. MATERIALS AND METHODS: Regional distribution of genetic mutations in lung cancer in Turkey (REDIGMA) study was carried out as a prospective, cross-sectional, observational study in a large number of centers in which lung cancer patients were followed and could perform genetic mutation analysis on patients' biopsy materials. RESULT: The 703 patients (77.7% male, mean age 63.3 ± 12.5 years) who were diagnosed as NSCLC from 25 different centers were included in the study. Tumor samples from patients were reported as 87.1% adenocarcinoma, 6.4% squamous cell carcinoma and 6.5% other. Mutation tests were found to be positive in 18.9% of these patients. The mutations were 69.9% EGFR, 26.3% ALK, 1.6% ROS and 2.2% PDL. Mutations were higher in women and non-smokers (p<0.000, p<0.001). Again, the frequency of mutations in adenocarcinoma was higher in metastatic disease. There was no difference between the patient's age, area of residence, comorbidity and clinical stage and mutation frequency. CONCLUSIONS: Our study revealed that the EGFR mutation rate in Turkey with NSCLC was similar to East European, African-American and Caucasian patients, and was lower than in East Asia.