Increased expression of YAP1 in prostate cancer correlates with extraprostatic extension.

Objective:Yes associated protein 1 (YAP1) is a member of the Hippo pathway, acting as a transcriptional coactivator. To elucidate the role of YAP1 and phosphorylated (p)YAP1 in prostate cancer (PCa) tumorigenesis, we investigated their expression in clinical samples of PCa and cell lines. Methods:Fifty-four tumor, adjacent nontumor, and prostate intraepithelial neoplasia (PIN) tissues from patients with PCa after radical prostatectomy were selected from a retrospective cohort and studied using immunohistochemistry (IHC). Protein and mRNA expression levels of YAP1 were evaluated by Western blot analysis and quantitative real-time reverse transcription PCR, respectively, in cancer cell lines. Publicly available gene expression datasets were downloaded to analyze YAP1 mRNA and protein levels in PCa tissue samples. Results:IHC analysis of PCa tissues revealed that YAP1 staining intensities were moderate to weak in the nucleus and cytoplasm of tumor cells, whereas adjacent normal epithelia showed strong staining. We observed that benign prostates were characterized by higher expression levels of both nuclear (P=0.004) and cytosolic (P=0.005) YAP1. pYAP1 staining was weak in the cytoplasm and absent in the nucleus of all the tissues investigated. YAP1 expression was an indicator of extraprostatic extension (EPE). The level of YAP1 was negatively correlated with the level of the androgen receptor (AR) in The Cancer Genome Atlas dataset and Western blot analysis of cell lines. Conclusions:Our study suggested that YAP1 expression is heterogeneous in PCa tissue samples; therefore, YAP1 might play different roles in different aspects of PCa progression. This might involve AR-YAP1 interplay in PCa

Image Processing Algorithms for Diagnostic Analysis of Microcirculation

Microcirculation has become a key factor for the study and assessment of tissue perfusion and oxygenation. Detection and assessment of the microvasculature using videomicroscopy from the oral mucosa provides a metric on the density of blood vessels in each single frame. Information pertaining to the density of these microvessels within a field of view can be used to quantitatively monitor and assess the changes occurring in tissue oxygenation and perfusion over time. Automated analysis of this information can be used for real-time diagnostic and therapeutic planning of a number of clinical applications including resuscitation. The objective of this study is to design an automated image processing system to segment microvessels, estimate the density of blood vessels in video recordings, and identify the distribution of blood flow. The proposed algorithm consists of two main stages: video processing and image segmentation. The first step of video processing is stabilization. In the video stabilization step, block matching is applied to the video frames. Similarity is measured by cross-correlation coefficients. The main technique used in the segmentation step is multi-thresholding and pixel verification based on calculated geometric and contrast parameters. Segmentation results and differences of video frames are then used to identify the capillaries with blood flow. After categorizing blood vessels as active or passive, according to the amount of blood flow, quantitative measures identifying microcirculation are calculated. The algorithm is applied to the videos obtained using Microscan Side-stream Dark Field (SDF) imaging technique captured from healthy and critically ill humans/animals. Segmentation results were compared and validated using a blind detailed inspection by experts who used a commercial semi-automated image analysis software program, AVA (Automated Vascular Analysis). The algorithm was found to extract approximately 97% of functionally active capillaries and blood vessels in every frame. The aim of this study is to eliminate the human interaction, increase accuracy and reduce the computation time. The proposed method is an entirely automated process that can perform stabilization, pre-processing, segmentation, and microvessel identification without human intervention. The method may allow for assessment of microcirculatory abnormalities occurring in critically ill and injured patients including close to real-time determination of the adequacy of resuscitation

Dendritic spine shape classification from two-photon microscopy images (Dendritik diken şekillerinin iki foton mikroskopi görüntüleri kullanılarak sınıflandırılması)

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly

Semi-supervised adaptation of motor imagery based BCI systems (Hayali motor hareketleri tabanlı BBA sistemlerinde yarı güdümlü uyarlama)

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well

Dendritic spine shape analysis using disjunctive normal shape models

Analysis of dendritic spines is an essential task to understand the functional behavior of neurons. Their shape variations are known to be closely linked with neuronal activities. Spine shape analysis in particular, can assist neuroscientists to identify this relationship. A novel shape representation has been proposed recently, called Disjunctive Normal Shape Models (DNSM). DNSM is a parametric shape representation and has proven to be successful in several segmentation problems. In this paper, we apply this parametric shape representation as a feature extraction algorithm. Further, we propose a kernel density estimation (KDE) based classification approach for dendritic spine classification. We evaluate our proposed approach on a data set of 242 spines, and observe that it outperforms the classical morphological feature based approach for spine classification. Our probabilistic framework also provides a way to examine the separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the shape analysis problem in this context

Increased expression of YAP1 in prostate cancer correlates with extraprostatic extension.

ObjectiveYes associated protein 1 (YAP1) is a member of the Hippo pathway, acting as a transcriptional coactivator. To elucidate the role of YAP1 and phosphorylated (p)YAP1 in prostate cancer (PCa) tumorigenesis, we investigated their expression in clinical samples of PCa and cell lines.MethodsFifty-four tumor, adjacent nontumor, and prostate intraepithelial neoplasia (PIN) tissues from patients with PCa after radical prostatectomy were selected from a retrospective cohort and studied using immunohistochemistry (IHC). Protein and mRNA expression levels of YAP1 were evaluated by Western blot analysis and quantitative real-time reverse transcription PCR, respectively, in cancer cell lines. Publicly available gene expression datasets were downloaded to analyze YAP1 mRNA and protein levels in PCa tissue samples.ResultsIHC analysis of PCa tissues revealed that YAP1 staining intensities were moderate to weak in the nucleus and cytoplasm of tumor cells, whereas adjacent normal epithelia showed strong staining. We observed that benign prostates were characterized by higher expression levels of both nuclear (P=0.004) and cytosolic (P=0.005) YAP1. pYAP1 staining was weak in the cytoplasm and absent in the nucleus of all the tissues investigated. YAP1 expression was an indicator of extraprostatic extension (EPE). The level of YAP1 was negatively correlated with the level of the androgen receptor (AR) in The Cancer Genome Atlas dataset and Western blot analysis of cell lines.ConclusionsOur study suggested that YAP1 expression is heterogeneous in PCa tissue samples; therefore, YAP1 might play different roles in different aspects of PCa progression. This might involve AR-YAP1 interplay in PCa

Raloxifene decreases serum malondialdehyde and nitric oxide levels in postmenopausal women with end-stage renal disease under chronic hemodialysis therapy

Aim

Prevalence of Anosmia in 10.157 Pediatric COVID-19 Cases: Multicenter Study from Turkey.

Introduction: COVID-19-related anosmia is a remarkable and disease-specific finding. With this multicenter cohort study, we aimed to determine the prevalence of anosmia in pediatric cases with COVID-19 from Turkey and make an objective assessment with a smell awareness questionnaire. Material and Methods: This multicenter prospective cohort study was conducted with pediatric infection clinics in 37 centers in 19 different cities of Turkey between October 2020 and March 2021. The symptoms of 10.157 COVID-19 cases 10-18 years old were examined. Age, gender, other accompanying symptoms, and clinical severity of the disease of cases with anosmia and ageusia included in the study were recorded. The cases were interviewed for the smell awareness questionnaire at admission and one month after the illness. Results: Anosmia was present in 12.5% (1.266/10.157) of COVID-19 cases 10-18 years of age. The complete records of 1053 patients followed during the study period were analyzed. The most common symptoms accompanying symptoms with anosmia were ageusia in 885 (84%) cases, fatigue in 534 cases (50.7%), and cough in 466 cases (44.3%). Anosmia was recorded as the only symptom in 84 (8%) of the cases. One month later, it was determined that anosmia persisted in 88 (8.4%) cases. In the smell awareness questionnaire, the score at admission was higher than the score one month later (P < 0.001). Discussion: With this study, we have provided the examination of a large case series across Turkey. Anosmia and ageusia are specific symptoms seen in cases of COVID-19. With the detection of these symptoms, it should be aimed to isolate COVID-19 cases in the early period and reduce the spread of the infection. Such studies are important because the course of COVID-19 in children differs from adults and there is limited data on the prevalence of anosmia

Picturing asthma in Turkey: results from the Turkish adult asthma registry

Introduction: National data on asthma characteristics and the factors associated with uncontrolled asthma seem to be necessary for every country. For this purpose, we developed the Turkish Adult Asthma Registry for patients with asthma aiming to take a snapshot of our patients, thereby assigning the unmet needs and niche areas of intervention. Methods: Case entries were performed between March 2018 and March 2022. A web-based application was used to record data. Study outcomes were demographic features, disease characteristics, asthma control levels, and phenotypes. Results: The registry included 2053 patients from 36 study centers in Turkey. Female subjects dominated the group (n = 1535, 74.8%). The majority of the patients had allergic (n = 1158, 65.3%) and eosinophilic (n = 1174, 57.2%) asthma. Six hundred nineteen (32.2%) of the patients had obese asthma. Severe asthma existed in 670 (32.6%) patients. Majority of cases were on step 3–5 treatment (n: 1525; 88.1%). Uncontrolled asthma was associated with low educational level, severe asthma attacks in the last year, low FEV1, existence of chronic rhinosinusitis and living in particular regions. Conclusion: The picture of this registry showed a dominancy of middle-aged obese women with moderate-to-severe asthma. We also determined particular strategic targets such as low educational level, severe asthma attacks, low FEV1, and chronic rhinosinusitis to decrease uncontrolled asthma in our country. Moreover, some regional strategies may also be needed as uncontrolled asthma is higher in certain regions. We believe that these data will guide authorities to reestablish national asthma programs to improve asthma service delivery