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Purpose: The study was conducted to examine the experiences of operating room nurses during the COVID-19 pandemic., Design: This study was designed as a phenomenological qualitative research method., Methods: This study was conducted with 10 volunteer operating room nurses who met the criteria for participation in the study between February 2021 and March 2021 in a public hospital. Data were collected using a personal information form and a semistructured interview from using the in-depth interview technique. Two researchers and one expert created the themes and codes using the thematic analysis method., Findings: As a result of the analysis, four themes and 29 codes were identified. The following codes were created for the theme "Changing systems and practices in the operating room": Personal protective equipment and sterility, workload/time, lack of communication between patient and nurse, decrease in the number of cases, change in the use of emergencies and elective procedures, flexible working methods. On the theme of the impact of the pandemic, anxiety/anxiety, psychological distress, difficulty with personal protective equipment, lack of nurses, longing/distance from family, sleep disturbances, family problems, and difficulty working in another department were noted. On the theme of coping strategies for the pandemic, the codes found were; communication with family, breathing/sporting exercises, spirituality, regular/healthy diet, online shopping, watching TV series/movies, and acceptance of the process. On the theme of "learning from the pandemic," the codes of the importance of life/health, the importance of family, worthlessness of the caring profession, financial injustice, gaining work experience, the importance of personal protection, lack of union support, and job satisfaction were produced., Conclusions: The study found that the nursing in the operating room has changed due to the COVID-19 pandemic, that nurses have experienced many positive/negative impacts, and that they have gained many benefits from the pandemic through various coping methods., (Copyright © 2022 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.)

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

Active growth hormone (GH) signaling triggers cellular growth and invasion-metastasis in colon, breast, and prostate cancer. Curcumin, an inhibitor of NF-ҡB pathway, is assumed to be a promising anti-carcinogenic agent. Atiprimod is also an anti-inflammatory, anti-carcinogenic agent that induces apoptotic cell death in hepatocellular carcinoma, multiple myeloma, and pituitary adenoma. We aimed to demonstrate the potential additional effect of atiprimod on curcumin-induced apoptotic cell death via cytokine expression profiles in MCF-7 and MDA-MB-231 cells with active GH signaling. The effect of curcumin and/or atiprimod on IL-2, IL-4, and IL-17A levels were measured by ELISA assay. MTT cell viability, trypan blue exclusion, and colony formation assays were performed to determine the effect of combined drug exposure on cell viability, growth, and colony formation, respectively. Alteration of the NF-ҡB signaling pathway protein expression profile was determined following curcumin and/or atiprimod exposure by RT-PCR and immunoblotting. Finally, the effect of curcumin with/without atiprimod treatment on Reactive Oxygen Species (ROS) generation and apoptotic cell death was examined by DCFH-DA and Annexin V/PI FACS flow analysis, respectively. Autocrine GH-mediated IL-6, IL-8, IL-10 expressions were downregulated by curcumin treatment. Atiprimod co-treatment increased the inhibitory effect of curcumin on cell viability, proliferation and also increased the curcumin-triggered ROS generation in each GH + breast cancer cells. Combined drug exposure increased apoptotic cell death through acting on IL-2, IL-4, and IL-17A secretion. Forced GH-triggered curcumin resistance might be overwhelmed by atiprimod and curcumin co-treatment via modulating NF-ҡB-mediated inflammatory cytokine expression in MCF-7 and MDA-MB-231 cells.

Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease., Competing Interests: Declaration of competing interest The authors declare that there is no conflict to interest related to this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channels, is the most common type of breast cancer in women. Approximately, 80% of breast cancer patients have invasive ductal carcinoma and roughly 66.6% of these patients are older than 55 years. This situation points out a powerful relationship between the type of breast cancer and progressed woman age. In this study, the classification of invasive ductal carcinoma breast cancer is performed by using deep learning models, which is the sub-branch of artificial intelligence. In this scope, convolutional neural network models and the autoencoder network model are combined. In the experiment, the dataset was reconstructed by processing with the autoencoder model. The discriminative features obtained from convolutional neural network models were utilized. As a result, the most efficient features were determined by using the ridge regression method, and classification was performed using linear discriminant analysis. The best success rate of classification was achieved as 98.59%. Consequently, the proposed approach can be admitted as a successful model in the classification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brain tumor. Recently, models of deep learning in the diagnosis and treatment of diseases in the biomedical field have gained intense interest. In this study, we propose a new convolutional neural network model named BrainMRNet. This architecture is built on attention modules and hypercolumn technique; it has a residual network. Firstly, image is preprocessed in BrainMRNet. Then, this step is transferred to attention modules using image augmentation techniques for each image. Attention modules select important areas of the image and the image is transferred to convolutional layers. One of the most important techniques that the BrainMRNet model uses in the convolutional layers is hypercolumn. With the help of this technique, the features extracted from each layer of the BrainMRNet model are retained by the array structure in the last layer. The aim is to select the best and the most efficient features among the features maintained in the array. Accessible magnetic resonance images were used to detect brain tumor with the BrainMRNet model. BrainMRNet model is more successful than the pre-trained convolutional neural network models (AlexNet, GoogleNet, VGG-16) used in this study. The classification success achieved with the BrainMRNet model was 96.05%., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objectives: The aim of the present study was to evaluate the internal auditory canal (IAC) and the nerves inside it to define possible structural differences in cases with subjective tinnitus of unknown origin., Methods: Cases applying to the ear, nose and throat department with the complaint of tinnitus with unknown origin and having normal physical examination and test results were included in the study (n=78). Patients admitted to the radiology clinic for routine cranial magnetic resonance imaging (MRI) and whose MRI findings revealed no pathologies were enrolled as the control group (n=79). Data for the control group were obtained from the radiology department and informed consent was obtained from all the patients. Diameters of the IAC and the nerves inside it were measured through enhanced images obtained by routine temporal bone MRIs in all cases. Statistical evaluations were performed using Student t-test and statistical significance was defined as P<0.05., Results: Measurements of IAC diameters revealed statistically significant differences between the controls and the tinnitus group (P<0.05). Regarding the diameters of the cochlear nerve, facial nerve, inferior vestibular nerve, superior vestibular nerve, and total vestibular nerve, no statistically significant difference was found between the controls and the tinnitus group., Conclusion: Narrowed IAC has to be assessed as an etiological factor in cases with subjective tinnitus of unknown origin.

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

Purpose: It is widely accepted that although valuable in the diagnosis of the discoid meniscus and tears, magnetic resonance imaging (MRI) can be insufficient in determining the type of the tear. This study calculates the sensitivity and specificity of MRI in determining the presence and absence of tears and how these values differ for different types of tears., Methods: This study is a retrospective review of 10 years of our experience with arthroscopic discoid meniscus treatment between 1999 and 2009. MRI findings were compared with the intraoperative arthroscopic findings in 52 patients with 50 lateral and two medial discoid menisci of which 24 were complete and 28 were incomplete. Tears were classified into six groups: (1) no tear, (2) simple horizontal tear, (3) radial tear, (4) combined horizontal tear, (5) complex tear and (6) longitudinal tear. Sensitivity, specificity, positive and negative predictive values of MRI were calculated for each group separately and for the presence and absence of tears in general. In addition, the effect of age, type of discoid meniscus, and presence and absence of shift on the distribution of tear types were analysed., Results: MRI was found to be 100 % specific and 97.8 % sensitive for determining the presence or absence of a tear with a negative predictive value of 85.7 % and a positive predictive value of 100 %. The specificities were 80 % for simple horizontal, 50 % for radial, 66.7 % for combined horizontal, 55.6 % for complex and 14.3 % for longitudinal tears, whereas the sensitivities were 66.7 % for simple horizontal, 96.9 % for radial, 87.5 % for combined horizontal, 94.6 % for complex and 100 % for longitudinal tears. The presence and absence of shift and type of the discoid were found to affect the distribution of the tear type., Conclusions: MRI is successful in determining the presence or absence of tears in discoid menisci; however, its ability to determine the tear type is questionable. Complete discoid menisci were found to have tendency towards having a simple horizontal or longitudinal tear, whereas incomplete discoid menisci tend to have radial or combined horizontal tears. Determination of the shift prior to surgery is important since it alters the surgical technique.

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

Purpose: The geometry of an articular surface is an important determinant of joint function. Although the geometry of the trochlear groove is considered to be important in the pathogenesis of patellofemoral joint disorders, the effects of the patella during the development of the femoral trochlear groove are unclear. This animal study aimed to investigate the relationship between the position of the patella and development of femoral trochlear groove in growing rabbits., Methods: Twenty-four knees of 12 rabbits were included in this study and were divided into two groups. First group consisted of the left knees and was used as the control group to which no surgical procedures were applied. Second group involved the right knees to which medial soft tissue restraints release was applied before 1 month of age. Computed tomographic (CT) evaluation of both knees of each rabbit was made in their first month of age before medial retinacular release and also during post-op 1-year follow-up. CT measurements included both the angle and depth of the femoral trochlear groove from 3 different parts (proximal, middle and distal) of the distal femur, and then these measurements were averaged., Results: Measurements revealed that while in the control group the groove angle decreased 27.4 degrees and the depth increased 0.11 mm, in the operated counterparts groove angle decreased 16.8 degrees and groove depth increased 0.03 mm, which indicated the flattening of the femoral groove in the operated group. These differences were found to be statistically significant (P < 0.05)., Conclusion: The results indicated that distal femoral groove with inadequate patellar position becomes more flattened and causes predisposition for patellar instability. Consequently, the clinical relevance of this study was that early reconstruction of the patellofemoral joint should be performed in the childhood to prevent the patellofemoral problems that are likely to be encountered in the following years.

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Objective: We sought to investigate the analgesic efficacy of oral dexketoprofen trometamol and intrauterine lidocaine in patients undergoing fractional curettage., Study Design: A randomized, double-blind, placebo-controlled trial was conducted on 111 women. Subjects were randomly assigned into 4 groups to receive either 25 mg of dexketoprofen or similar-appearing placebo tablets and either 5 mL intrauterine 2% lidocaine or saline. The main outcome measure was the intensity of pain measured by a 10-cm visual analog scale. Pain scoring was performed prior to, during, and 30 minutes after the procedure., Results: No statistically significant difference was found among the mean pain scores of women during the procedure in the dexketoprofen and saline, placebo and lidocaine, and dexketoprofen and lidocaine groups. The mean pain scores in all 3 groups revealed significant reduction when compared with placebo and saline combination (P = .001)., Conclusion: Administration of intrauterine lidocaine or oral dexketoprofen appears to be effective in relieving fractional curettage associated pain. However, a combination of them does not work better in further reduction of pain., (Copyright (c) 2010 Mosby, Inc. All rights reserved.)

Essential thrombocythaemia (ET) is a disease characterized by an increased platelet count, megakaryocytic hyperplasia and a hemorrhagic or thrombotic tendency. Pregnancy in patients with ET can have a favorable outcome. However, ET has also been reported to complicate pregnancy by recurrent abortions, intrauterine death, and fetal growth retardation due to placental infarctions. ET has an unusual prevalence of intraabdominal (hepatic, portal and mesenteric) vein thrombosis, especially in young patients, which can lead to portal hypertension. There are ample cases in the literature of both essential thrombocytosis complicating pregnancy and portal hypertension complicating pregnancy, but the coincidence of both conditions appears to be unique. In this case report, we report a successful pregnancy in a patient with a prior diagnosis of essential thrombocytosis with remote secondary portal vein thrombosis and portal hypertension (PH).

Background: An adenomyoma presenting outside the uterus is an extremely rare entity and only three cases of primary ovarian adenomyoma have been reported up to date., Case Report: We report the fourth case of ovarian adenomyoma in a 45-year-old woman with an endometrial polyp. Transvaginal ultrasonography revealed a solitary endometrial polyp with an enlarged left ovary which appeared heterogenous with isoechoic and mildly hyperechoic pattern., Conclusion: Total abdominal hysterectomy and bilateral salpingo-oopherectomy was performed and histologic examination revealed an adenomyoma arising primarily in the ovary.

Pregnancy and cancer is a complex situation. The coincidence of chronic myelogeneous leukemia (CML) and pregnancy is an uncommon event, in part because CML occurs mostly in older age groups. The management of CML during pregnancy is a difficult problem because of the potential effects of the therapy on the mother and fetus. Imatinib is a relatively new drug in this era and it induces dramatic hematologic and cytogenetic responses in CML but it is not recommended for use during pregnancy or if the patient plans to conceive. In the literature there are very few reports of outcome of pregnancy conceived while on imatinib. In this report, we describe a successful pregnancy and labor under treatment of imatinib in a patient who was diagnosed with CML at the beginning of her pregnancy.

Background: Endometrial stromal sarcoma (ESS) is an uncommon malign neoplasm, and its occurrence outside the uterus is extremely rare in the absence of metastasis or extension of a primary uterine neoplasm. When arising in the pelvis or abdominal cavity, ESS is associated with uterine adnexa or serosal surface of various organs., Case: We present the case of a 46-year-old woman with lower abdominal pain and regular menstruation who underwent laparotomy after a diagnosis of pelvic mass mimicking a right adnexial tumor. Exploration of the pelvis revealed a retroperitoneal mass of 15 cm in diameter in the right illiac fossa without accompanying pelvic or paraaortic lymphadenopathy. Uterus and ovaries were bilaterally normal in size. The pathology showed low-grade ESS of the uterus with direct spread to retroperitoneum without serosal metastases., Conclusion: This case shows that despite its well-known good prognostic nature, low-grade ESS may behave as an aggressive malignancy.

Congenital hepatic fibrosis (CHF) is a rare congenital multisystemic disorder, mostly inherited in autosomal recessive fashion, primarily affecting renal and hepatobiliary systems. Main underlying process of the disease is the malformation of the ductal plate, the embryological precursor of the biliary system, and secondary biliary strictures and periportal fibrosis ultimately leading to portal hypertension. The natural course of the disease is highly variable ranging from minimally symptomatic disease to true cirrhosis of the liver. However, in most patients the most common manifestations of the diseases that are related to portal hypertension, particularly splenomegaly and bleeding varices. Many other disease processes may co-exist with the disease including Caroli's disease, choledochal cysts and autosomal recessive polycystic kidney disease (ARPKD) reflecting the mulstisystemic nature of the disease. The associating biliary ductal disease led the authors to think that all these entities are a continuum and different reflections of the same underlying pathophysiological process. Although, conventional method of diagnosis of CHF is the liver biopsy the advent of imaging technologies and modalities, today, may permit the correct diagnosis in a non-invasive manner. Characteristic imaging features are generally present and recognition of these findings may obviate liver biopsy while preserving the diagnostic accuracy. In this article, it is aimed to increase the awareness of the practising radiologists to the imaging findings of this uncommon clinical disorder and trail the blaze for future articles relating to this issue.