Molecular characterization of methicillin resistant Staphylococcus aureus isolated from hospitals environments and patients in Northern Palestine

BACKGROUND: Staphylococcus aureus (S. aureus) is considered one of the most common pathogen to humans. Infections caused by this mocroorganism can be acquired through both hospital and community settings. This study was carried out to investigate molecular characterization of MRSA strains isolated from the patients and their environment in two hospitals (Rafidia hospital and Thabet hospital) inNorthern Palestine, and to determine the clonal identity between these strains and their possible contribution to nosocomial infections. METHODS: Two hundred sixty five swabbed samples were collected from these hospitals, S. aureus was isolated,  antibiotic resistant genes were Panton–Valentin leukocidin (PVL) gene were detected and SCCmec and spA were typed by PCR and/or sequencing. RESULTS: The prevalence of MRSA among S. aureus isolates was 29% and 8.2% in Rafidia hospital and Thabet hospital, respectively. All strains resistant to oxacilllin disk were carried mecA gene. Majority of strains (84.6%) carried SCCmec type II (n = 11), type IVa and non-typeable were also detected. In addition, PVL was detected in 2 (14.3%) clinical strains. ERIC PCR patterns revealed that 2 strains recovered from patient bed and nasal swab isolated from Thabet Hospital were nontypeable, spA typing showed that they belonged to type t386 and have identical DNA sequences. Other 2 clinical isolates were spa typed, one belonged to clone t044, while the other is new clone not exist in database. CONCLUSIONS: Results may give evidence that environmental contamination possibly contributing to nosocomial infections

Susceptibility of Candida albicans isolates to Terbinafine and Ketoconazole

 The prevalence of drug resistance has become an important issue in various yeast infections, which have a significant effects on both human animal health. In this study, an attempt has been made to determine susceptibility pattern of two antifungal agents Terbinafine and Ketoconazole against 45 oral and non oral Candida albicans isolates using broth microdilution method. Under in vitro conditions, results showed that (42/45) 93% of the C. albicans isolates had MIC values indicating susceptibility to Ketoconazole (?0.125 ?g/ml) and MICs ranged from ?0.03125-8.0 ?g/ml. According to Terbinafine, (40/45) 88.9% of isolates had MICs less than 4 ?g/ml and MICs ranged from 0.25-8.0 ?g/ml. This is the first report of in vitro antifungal susceptibility data to be published from Palestine against clinical isolates of Candida albicans. Availability of sensitive and highly accurate antifungal susceptibility testing methods, can permit analysis of data in vitro and with outcome in vivo, important to assist physician for making appropriate drug choices and patient management decision. These data indicated that Terbinafine and Ketoconazole are still active against C. albicans and may therefore have clinical applications against some of these organisms. Key words: C. albicans, Antifungal agents, Terbinafine, Ketoconazole, MIC

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

ANTIBIOTIC RESISTANCE AGAINST STAPHYLOCOCCAL ISOLATES RECOVERED FROM SUBCLINICAL MASTITIS IN THE NORTH OF PALESTINE

The antimicrobial resistance to 10 antibiotics was determined in 132 staphylococcal isolates. These representing Staphylococcus aureus (n=66) and Staphylococcus epidermidis (n=66). All isolates were from milk samples obtained from subclinical mastitis from Awassi ewes, local goats and Fresian cows. Results indicated that among all the antimicrobial agents tested the highest resistance of staphylococcal isolates was to ampicillin. The frequency of resistance to ampicillin was 75.8 and 66.7% against S. aureus and S. epidermidis isolates, respectively. Resistance to amikacin, cefepime, vancomycin, tobramycin or chloramphenicol was rare. None of staphylococcal isolates was susceptible to all tested antibiotics. Resistance to at least 3 drugs was found in (35) 53% and (28) 42.4 % of S. aureus and S. epidermidis isolates, respectively

Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%