An analysis of nursing and medical students' attitudes towards and knowledge of Alzheimer's Disease (AD).

OBJECTIVES: Little is known about how Jordanian undergraduate medical and nursing students perceive Alzheimer's disease (AD) care. This study aimed to investigate nursing and medical students' AD knowledge, attitudes, and associated factors with their knowledge to inform reforms to multidisciplinary AD education undergraduate programs in Jordan. METHODS: Cross-sectional research was carried out using a self-administered questionnaire. Students' knowledge was measured using the Alzheimer's Disease Knowledge Scale (ADKS) and attitudes were measured using the Dementia Care Attitude Scale (DCAS). The survey was completed by 423 nursing and medical students. RESULTS: The overall mean score on the ADKS for students' AD knowledge was 17.50 (SD=3.08) out of 30 and the DCAS for students' attitudes toward AD was 26.76 (SD=6.19) out of 40. CONCLUSIONS: Medical students had a higher level of AD knowledge and a lower level of positive attitude than nursing students (p<0.05).

Ligand-directed targeting of lymphatic vessels uncovers mechanistic insights in melanoma metastasis.

Metastasis is the most lethal step of cancer progression in patients with invasive melanoma. In most human cancers, including melanoma, tumor dissemination through the lymphatic vasculature provides a major route for tumor metastasis. Unfortunately, molecular mechanisms that facilitate interactions between melanoma cells and lymphatic vessels are unknown. Here, we developed an unbiased approach based on molecular mimicry to identify specific receptors that mediate lymphatic endothelial-melanoma cell interactions and metastasis. By screening combinatorial peptide libraries directly on afferent lymphatic vessels resected from melanoma patients during sentinel lymphatic mapping and lymph node biopsies, we identified a significant cohort of melanoma and lymphatic surface binding peptide sequences. The screening approach was designed so that lymphatic endothelium binding peptides mimic cell surface proteins on tumor cells. Therefore, relevant metastasis and lymphatic markers were biochemically identified, and a comprehensive molecular profile of the lymphatic endothelium during melanoma metastasis was generated. Our results identified expression of the phosphatase 2 regulatory subunit A, α-isoform (PPP2R1A) on the cell surfaces of both melanoma cells and lymphatic endothelial cells. Validation experiments showed that PPP2R1A is expressed on the cell surfaces of both melanoma and lymphatic endothelial cells in vitro as well as independent melanoma patient samples. More importantly, PPP2R1A-PPP2R1A homodimers occur at the cellular level to mediate cell-cell interactions at the lymphatic-tumor interface. Our results revealed that PPP2R1A is a new biomarker for melanoma metastasis and show, for the first time to our knowledge, an active interaction between the lymphatic vasculature and melanoma cells during tumor progression.

PRUNE2 is a human prostate cancer suppressor regulated by the intronic long noncoding RNA PCA3.

Prostate cancer antigen 3 (PCA3) is the most specific prostate cancer biomarker but its function remains unknown. Here we identify PRUNE2, a target protein-coding gene variant, which harbors the PCA3 locus, thereby classifying PCA3 as an antisense intronic long noncoding (lnc)RNA. We show that PCA3 controls PRUNE2 levels via a unique regulatory mechanism involving formation of a PRUNE2/PCA3 double-stranded RNA that undergoes adenosine deaminase acting on RNA (ADAR)-dependent adenosine-to-inosine RNA editing. PRUNE2 expression or silencing in prostate cancer cells decreased and increased cell proliferation, respectively. Moreover, PRUNE2 and PCA3 elicited opposite effects on tumor growth in immunodeficient tumor-bearing mice. Coregulation and RNA editing of PRUNE2 and PCA3 were confirmed in human prostate cancer specimens, supporting the medical relevance of our findings. These results establish PCA3 as a dominant-negative oncogene and PRUNE2 as an unrecognized tumor suppressor gene in human prostate cancer, and their regulatory axis represents a unique molecular target for diagnostic and therapeutic intervention.

Receptor tyrosine kinase EphA5 is a functional molecular target in human lung cancer.

Lung cancer is often refractory to radiotherapy, but molecular mechanisms of tumor resistance remain poorly defined. Here we show that the receptor tyrosine kinase EphA5 is specifically overexpressed in lung cancer and is involved in regulating cellular responses to genotoxic insult. In the absence of EphA5, lung cancer cells displayed a defective G1/S cell cycle checkpoint, were unable to resolve DNA damage, and became radiosensitive. Upon irradiation, EphA5 was transported into the nucleus where it interacted with activated ATM (ataxia-telangiectasia mutated) at sites of DNA repair. Finally, we demonstrate that a new monoclonal antibody against human EphA5 sensitized lung cancer cells and human lung cancer xenografts to radiotherapy and significantly prolonged survival, thus suggesting the likelihood of translational applications.

Cooperative effects of aminopeptidase N (CD13) expressed by nonmalignant and cancer cells within the tumor microenvironment.

Processes that promote cancer progression such as angiogenesis require a functional interplay between malignant and nonmalignant cells in the tumor microenvironment. The metalloprotease aminopeptidase N (APN; CD13) is often overexpressed in tumor cells and has been implicated in angiogenesis and cancer progression. Our previous studies of APN-null mice revealed impaired neoangiogenesis in model systems without cancer cells and suggested the hypothesis that APN expressed by nonmalignant cells might promote tumor growth. We tested this hypothesis by comparing the effects of APN deficiency in allografted malignant (tumor) and nonmalignant (host) cells on tumor growth and metastasis in APN-null mice. In two independent tumor graft models, APN activity in both the tumors and the host cells cooperate to promote tumor vascularization and growth. Loss of APN expression by the host and/or the malignant cells also impaired lung metastasis in experimental mouse models. Thus, cooperation in APN expression by both cancer cells and nonmalignant stromal cells within the tumor microenvironment promotes angiogenesis, tumor growth, and metastasis.

From combinatorial peptide selection to drug prototype (I): targeting the vascular endothelial growth factor receptor pathway.

Inhibition of blood vessel formation is a viable therapeutic approach in angiogenesis-dependent diseases. We previously used a combinatorial screening on vascular endothelial growth factor (VEGF)-activated endothelial cells to select the sequence CPQPRPLC and showed that the motif Arg-Pro-Leu targets VEGF receptor-1 and neuropilin-1. Here, we evaluated and validated (D)(LPR), a derivative molecule with strong antiangiogenesis attributes. This prototype drug markedly inhibits neovascularization in three mouse models: Matrigel-based assay, functional human/murine blood vessel formation, and retinopathy of prematurity. In addition to its systemic activity, (D)(LPR) also inhibits retinal angiogenesis when administered in an eye-drop formulation. Finally, in preliminary studies, we have showed targeted drug activity in an experimental tumor-bearing mouse model. These results show that drugs targeting extracellular domains of VEGF receptors are active, affect signal transduction, and have potential for clinical application. On a larger context, this study illustrates the power of ligand-directed selection plus retro-inversion for rapid drug discovery and development.

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features.

Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease's characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN-FFNN and CNN-XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet-MobileNet-DenseNet121-XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%.

Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method.

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means.

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features.

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%.

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

c-ABL modulates MAP kinases activation downstream of VEGFR-2 signaling by direct phosphorylation of the adaptor proteins GRB2 and NCK1.

Vascular Endothelial Growth Factor-A (VEGF-A) is a key molecule in normal and tumor angiogenesis. This study addresses the role of c-ABL as a novel downstream target of VEGF-A in primary Human Umbilical Vein Endothelial Cells (HUVEC). On the basis of immunoprecipitation experiments, in vitro kinase assay and RNA interference, we demonstrate that VEGF-A induces the c-ABL kinase activity through the VEGF Receptor-2/Phosphatidylinositol-3-Kinase pathway. By treating HUVEC with the specific tyrosine kinase inhibitor STI571 and over-expressing a dominant negative c-ABL mutant, we show that the VEGF-A-activated c-ABL reduces the amplitude of Mitogen-Activated Protein Kinases (ERK1/2, JNKs and p38) activation in a dose-dependent manner by a negative feedback mechanism. By analysis of the adaptor proteins NCK1 and GRB2 mutants we further show that the negative loop on p38 is mediated by c-ABL phosphorylation at tyrosine 105 of the adaptor protein NCK1, while the phosphorylation at tyrosine 209 of GRB2 down-modulates ERK1/2 and JNKs signaling. These findings suggest that c-ABL function is to establish a correct and tightly controlled response of endothelial cells to VEGF-A during the angiogenic process.

Endothelial cell adhesion to the extracellular matrix induces c-Src-dependent VEGFR-3 phosphorylation without the activation of the receptor intrinsic kinase activity.

RATIONALE: Integrins cooperate with growth factor receptors to promote downstream signaling for cell proliferation and migration. However, the mechanism of receptor activation is still unknown. OBJECTIVE: To analyze the mechanism of phosphorylation of the vascular endothelial growth factor receptor (VEGFR)-3 by cell adhesion. METHODS AND RESULTS: We show that VEGFR-3 phosphorylation, induced by cell attachment to the extracellular matrix, is independent from the intrinsic kinase activity of the receptor, as evidenced from phosphorylation cell adhesion experiments with a mutant kinase dead receptor or in the presence of the specific kinase inhibitor MAZ 51. Cell adhesion experiments in the presence of the c-Src inhibitor PP2 or in fibroblast triple knockout for c-Src, Yes, and Fyn (SYF) demonstrate that VEGFR-3 phosphorylation, induced by extracellular matrix, is mediated by c-Src. Kinase assays in vitro with recombinant c-Src show that VEGFR-3 is a direct c-Src target and mass spectrometry analysis identified the sites phosphorylated by c-Src as tyrosine 830, 833, 853, 1063, 1333, and 1337, demonstrating that integrin-mediated receptor phosphorylation induces a phosphorylation pattern that is distinct from that induced by growth factors. Furthermore, pull-down assays show that integrin-mediated VEGFR-3 phosphorylation activates the recruitment to the receptor of the adaptor proteins CRKI/II and SHC inducing activation of JNK. CONCLUSIONS: These data suggest that cell adhesion to extracellular matrix induces a downstream signaling using the tyrosine kinase receptor VEGFR-3 as scaffold.

An unrecognized extracellular function for an intracellular adapter protein released from the cytoplasm into the tumor microenvironment.

Mammalian cell membranes provide an interface between the intracellular and extracellular compartments. It is currently thought that cytoplasmic signaling adapter proteins play no functional role within the extracellular tumor environment. Here, by selecting combinatorial random peptide libraries in tumor-bearing mice, we uncovered a direct, specific, and functional interaction between CRKL, an adapter protein [with Src homology 2 (SH2)- and SH3-containing domains], and the plexin-semaphorin-integrin domain of beta(1) integrin in the extracellular milieu. Through assays in vitro, in cellulo, and in vivo, we show that this unconventional and as yet unrecognized protein-protein interaction between a regulatory integrin domain (rather than a ligand-binding one) and an intracellular adapter (acting outside of the cells) triggers an alternative integrin-mediated cascade for cell growth and survival. Based on these data, here we propose that a secreted form of the SH3/SH2 adaptor protein CRKL may act as a growth-promoting factor driving tumorigenesis and may lead to the development of cancer therapeutics targeting secreted CRKL.

Growth factor stimulation induces cell survival by c-Jun. ATF2-dependent activation of Bcl-XL.

Growth factor stimulation induces c-Jun-dependent survival of primary endothelial cells. However, the mechanism of c-Jun anti-apoptotic activity has not been identified. We here demonstrate that in response to growth factor treatment, primary human endothelial cells as well as mouse fibroblasts respond with an increased expression of c-Jun that forms a complex with ATF2. This complex activates the expression of the anti-apoptotic protein Bcl-X(L). By site-directed mutagenesis experiments, we identified two AP-1-binding sites located within the proximal promoter of the Bcl-X gene. Site-directed mutagenesis demonstrated that these AP-1 sites are required for the transcriptional activation of the promoter. Chromatin immunoprecipitation experiments show that in response to growth factor treatment, the heterodimer c-Jun.ATF2 binds to these functional AP-1 sites. Silencing of either c-Jun or ATF2 demonstrated that both nuclear factors are required for the activation of the proximal Bcl-X promoter. Taken together, our experiments provide evidence that growth factor-independent signaling pathways converge in the formation of an active c-Jun.AFT2 dimer, which induces the expression of the anti-apoptotic factor Bcl-X(L) that mediates a pro-survival response.

Complementary and alternative medicine use in coronary heart disease patients: a cross-sectional study from Palestine.

BACKGROUND: There is a lack of data on the use of complementary and alternative medicine (CAM) in patients with coronary heart disease (CHD). This study examined the use of CAM among patients with CHD, the reasons and factors influencing their use, the types of CAM used, and the relationship between patient's demographics and the use of CAM. METHODS: In order to determine the prevalence and usage of CAM among Palestinian patients with CHD, a cross-sectional descriptive study was performed from three different hospitals. Using a convenient sampling method, a questionnaire was completed in a face-to-face interview with the patients. Descriptive statistics were used for socio-demographic, and clinical variables. Siahpush scale was used to examine the attitude of CHD patients toward CAM use. RESULTS: Of the 150 patients that were interviewed, 128 (85.3%) of the patients completed the questionnaire. The majority of CAM users reported CAM use for health problems other than CHD, while a total of 59 (45.9%) patients have used CAM for their heart problems. On the other hand, it was found that the place of residency and pattern of CHD were significantly associated with CAM use (p = 0.039 and 0.044, respectively). In addition, religious practices were found to be the most common form of CAM used by patients, while body and traditional alternative methods were the least being used. A significant association between the attitudes of patients with CHD and their use of CAM was found (patients' attitudes towards alternative medicine and natural remedies were p = 0.011 and 0.044, respectively). CONCLUSIONS: CAM use among our respondents is common. Despite a lack of evidence-based research supporting its potential benefits and side effects. Understanding the factors that affect CAM use by CHD patients offers healthcare workers and policymakers an opportunity to better understand CAM use and ultimately improve patient-physician interactions.

Development of a VLP-Based Vaccine Displaying an xCT Extracellular Domain for the Treatment of Metastatic Breast Cancer.

Metastatic breast cancer (MBC) is the leading cause of cancer death in women due to recurrence and resistance to conventional therapies. Thus, MBC represents an important unmet clinical need for new treatments. In this paper we generated a virus-like particle (VLP)-based vaccine (AX09) to inhibit de novo metastasis formation and ultimately prolong the survival of patients with MBC. To this aim, we engineered the bacteriophage MS2 VLP to display an extracellular loop of xCT, a promising therapeutic target involved in tumor progression and metastasis formation. Elevated levels of this protein are observed in a high percentage of invasive mammary ductal tumors including triple negative breast cancer (TNBC) and correlate with poor overall survival. Moreover, xCT expression is restricted to only a few normal cell types. Here, we tested AX09 in several MBC mouse models and showed that it was well-tolerated and elicited a strong antibody response against xCT. This antibody-based response resulted in the inhibition of xCT's function in vitro and reduced metastasis formation in vivo. Thus, AX09 represents a promising novel approach for MBC, and it is currently advancing to clinical development.

Total and Specific Immunoglobulin E for Detection of Most Prevalent Aeroallergens in a Jordanian Cohort.

INTRODUCTION: Allergies are defined as an immune response to non-microbial environmental antigens (allergens) that involve TH2 cells, mast cells, eosinophils and immunoglobulin E (IgE). Atopic disorders such as urticaria, asthma, hay fever, and eczema exhibit a strong familial predisposition and specific IgE-mediated reaction after exposure to the allergens. Aeroallergens involved in the hypersensitivity reactions include pollens, animal dander, fungal spores and house dust mite. Frequency and type of aeroallergens vary in different countries based on climate, vegetation and geographic areas. AIM: Due to increased prevalence of allergic diseases, in vitro diagnostic tests are commonly utilized in our area. The aim of our study is to evaluate the association between total and specific IgE and to study frequency of different aeroallergens in the population. METHODS: The study was conducted in a time period between 1/12/2017 and 15/11/2018 at King Hussein Medical Center, Amman, Jordan. A total of 80 patients with symptoms of allergic disorders were included, ages of individual's ranged between 1 year and 77 years, 58.8 % (n=47) of which male and 41.2 % (n=33) female. Blood samples from all patients were collected into a 10 ml gel separator (with clot activator) tubes and tested for total IgE and specific IgE. RESULTS: A total of 80 patients aged 1-77 years were divided into 4 groups depending on the normal value of total IgE as follow: 1-5 years, 6-9 years, 10-15 years, and adult. A total of 43(53.75%) patients exhibited elevated total IgE level, and 37(46.25%) had normal level. 41(51.2%) patients had elevated total IgE and positive specific IgE. The sensitivity and specificity of total IgE when using specific IgE as standard test was 77.4% and 92.5% respectively. The accuracy rate of the total IgE test was 82.5%. The most common aeroallergens were dermatophagoides pteronyssinus (13.6%), followed by grass mix (12.8). CONCLUSION: Testing of specific IgE is an essential procedure that helps to detect the cause of allergy. Although negative specific IgE could not exclude allergen sensitization due to limitations of detection method and allergen selection, and positive total and specific IgE indicate probability of sensitization.