Psychological Problems Experienced by Women with Gynecological Cancer and How They Cope with It: A Phenomenological Study in Turkey

This study was carried out to reveal, in detail, the psychosocial problems faced by women in Turkey during their illness with gynecological cancer, and how they cope with these problems. The phenomenological approach used for the methodology is consistent with that described by Clark Moustakas's transcendental phenomenology. The sample included 17 married women. A semistructured, in-depth question directive was used to collect the data. The psychological problems found in the women in the study included frustration and despair, depression, inability to control anger, disruption in body image, and problems with their sex lives. The women in the study stated that, among other activities, they prayed frequently. They also emphasized that social support from family and others was important in coping. The majority said that they were able to cope through denial. Women under treatment for gynecological cancer should be evaluated from a psychosocial standpoint, and spiritual care and social support should be provided as they frequently use these to cope with their illness. It is recommended that a team be created, consisting of nurses, psychiatrists or psychologists, and religious staff to meet these needs

Restless legs syndrome and quality of life in pregnant women

OBJECTIVE: In this study, we aimed to determine the extent of restless legs syndrome (RLS) in pregnant women and evaluate the relationship between the syndrome and quality of life. METHODS: This is a cross-sectional descriptive study. A questionnaire developed by the researcher, the Short Form 36 (SF-36) Questionnaire to measure the quality of life, the International Restless Legs Syndrome Study Group (IRLSSG) Diagnostic Criteria for RLS and the Restless Legs Syndrome Rating Scale were applied to the women to collect the data. A total of 250 pregnant women were included in the study. RESULTS: The mean age of the women was 28.11 +/- 5.59 years and the mean gestational time was 26.26 +/- 10.72 weeks. Symptoms of RLS were seen in 46.4 \% of the women. The mean for the RLS Violence Rating Score was 20.82 +/- 6.61 for the women with RLS. RLS was found to be mild in 5.2 \% of the women, moderate in 45.7 \%, severe in 40.5 \% and very severe in 8.6 \%. A statistically significant effect of RLS survival on quality of life was observed. CONCLUSION: These results indicate that almost half of the pregnant women in this study experienced RLS, and about half of those with RLS experienced severe or very severe RLS. There is a significant relationship between RLS and six domains of SF-36 (physical, role limitations, pain, general health perception, energy/ vitality, and mental health)

Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about >2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application

Serotype distribution of Streptococcus pneumoniae in children with invasive diseases in Turkey: 2008-2014

WOS: 000371745700019PubMed ID: 26325175Successful vaccination policies for protection from invasive pneumococcal diseases (IPD) dependent on determination of the exact serotype distribution in each country. We aimed to identify serotypes of pneumococcal strains causing IPD in children in Turkey and emphasize the change in the serotypes before and after vaccination with 7-valent pneumococcal conjugate vaccine (PCV-7) was included and PCV-13 was newly changed in Turkish National Immunization Program. Streptococcus pneumoniae strains were isolated at 22 different hospitals of Turkey, which provide healthcare services to approximately 65% of the Turkish population. Of the 335 diagnosed cases with S. pneumoniae over the whole period of 2008-2014, the most common vaccine serotypes were 19F (15.8%), 6B (5.9%), 14 (5.9%), and 3 (5.9%). During the first 5y of age, which is the target population for vaccination, the potential serotype coverage ranged from 57.5 % to 36.8%, from 65.0% to 44.7%, and from 77.4% to 60.5% for PCV-7, PCV-10, and PCV-13 in 2008-2014, respectively. The ratio of non-vaccine serotypes was 27.2% in 2008-2010 whereas was 37.6% in 2011-2014 (p=0.045). S. penumoniae serotypes was less non-susceptible to penicillin as compared to our previous results (33.7vs 16.5 %, p=0.001). The reduction of those serotype coverage in years may be attributed to increasing vaccinated children in Turkey and the increasing non-vaccine serotype may be explained by serotype replacement. Our ongoing IPD surveillance is a significant source of information for the decision-making processes on pneumococcal vaccination.PfizerPfizerThis study was supported by Pfizer

Serotypes of Streptococcus pneumoniae Isolates from Children with Invasive Pneumococcal Disease in Turkey: Baseline Evaluation of the Introduction of the Pneumococcal Conjugate Vaccine Nationwide ▿ ‖

Before use of the pneumococcal conjugate vaccine PCV7 became widespread in Turkey, 202 invasive pneumococcus isolates were analyzed. The most common serotypes were 19F and 6B. In children ≤2 years of age, the potential coverage rate of PCV7 was 69.5%. The most frequent non-PCV7 serotypes were 19A, 3, 1, 6A, and 8

Immunization status in chronic obstructive pulmonary disease: A multicenter study from Turkey

OBJECTIVE: The purpose of this study is to detect the prevalence and the factors associated with influenza and pneumococcal vaccination and outcomes of vaccination during 2013-2014 season in patients with chronic obstructive pulmonary disease (COPD) in Turkey