Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

Internet Of Things (IOT) is a network of various devices that are connected over the internet, and they can collect and exchange data with each other. These IOT devices generate a lot of data that needs to be collected and mined for actionable results through use artificial intelligence (AI) to manage huge data flows and storage in the IOT network. In this paper we briefly discussed about what IOT is, what AI is, Algorithm of AI, Challenge AI with IOT, application of artificial intelligence system in the IOT. The self-optimizing network and software defined network are parts of the important parameters in the Artificial Intelligence IoT System. This paper provides a general discussion about importance of the IoT in different applications. The paper covers different applications of IoT and shows the relationship between AI and IoT. The role of the AI in IoT applications is extensively discussed. In the future work, we are planning to work on improving the performance of IoT applications using advanced AI methods and algorithms such as Machine Learning and Deep Learning.

Background: Ventilator-associated pneumonia (VAP) caused by carbapenem-resistant gram-negative bacteria has been proven to be an escalating public health challenge in Egypt owing to its high mortality rate and raised health care costs. Purpose: Detection of carbapenem-resistant gram-negative bacilli among VAP patients, genotypic identification of carbapenemase genes in the isolated strains with evaluation of their impact on patient outcome and detection of carbapenemase-producing enterobacterales by MASTDISCS combi Carba plus disc system. Methods: Broncho-alveolar lavage fluid (BALF) and endotracheal aspirate were collected aseptically from clinically suspected VAP patients. Pathogen identification and antibiotic sensitivity testing were done. Carbapenemase-encoding genes (bla KPC , bla NDM , and bla OXA-48 ) were tested by PCR in all carbapenem-resistant gram-negative isolates. Performance of MASTDISCS combi Carba plus in isolated Enterobacterales was assessed in relation to the PCR results. Results: Eighty-three carbapenem-resistant gram-negative isolates were detected. The most frequent pathogens were Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa representing 34.9%, 20.5% and 18.1%, respectively. bla KPC was the predominant gene. Patients with persistent mechanical ventilation less than 15 days and Pseudomonas aeruginosa infection were significantly associated with a higher death rate. MAST-Carba plus had the highest sensitivity, specificity, positive and negative predictive values for detecting OXA-48 carbapenemases representing 81.8%, 92.5%, 75% and 94.9%, respectively. Conclusion: Worse outcome in VAP patients was associated with carbapenem-resistant gram-negative bacilli. MASTDISCS combi Carba plus is an efficient simple method for identification of different carbapenemases among enterobacterales.

EALTHCARE textiles are a major segment of medical textiles because they are one of the markets for technical textiles that are growing the fastest. To provide textiles an antibacterial, blood-and water-repellent finish, numerous classes of compounds have been created.. Our aim here is to treat pure cotton and polyester/cotton blended fabrics with perfluoro-heptyl-methacrylate (PFHMA) as well as magnesium oxide nanoparticles (MgONPs) to impart multifunctional properties to the fabrics. The treated substrates are tested by measuring the contact angle, microbial activity, and scanning electron microscopy to analyze the results obtained.

Continuous monitoring of Listeria spp., particularly Listeria monocytogenes, in foods is a mandatory task for food safety and microbiology sectors. This study aimed to determine the prevalence and antimicrobial resistance patterns of L. monocytogenes in milk and dairy products retailed in Egypt. Furthermore, an experimental trial was conducted to investigate the antilisterial effects of some phytochemicals. A total of 200 samples (market raw milk, Kareish cheese, Damietta cheese, and plain yoghurt, 50 each) were collected and examined for detection of Listeria spp. The results revealed that 8, 12, 1, and 0 samples of market raw milk, Damietta cheese, Kareish cheese, and plain yoghurt were contaminated with Listeria spp., respectively. Antimicrobial sensitivity testing revealed that all L. monocytogenes isolates (15) were resistant to streptomycin and erythromycin. Molecular analysis revealed that 86.67% of L. monocytogenes harbored hylA virulent gene. Use of rosmarinic acid, ascorbic acid, thyme, and clove essential oils significantly ( P < 0.05 ) reduced L. monocytogenes growth in soft cheese—artificially contaminated with L. monocytogenes throughout a 4-week incubation period. In conclusion, strict hygienic conditions should be adopted during the preparation and distribution of dairy products. In addition, rosmarinic acid, ascorbic acid, clove, and thyme essential oils are good candidates as food preservatives with antilisterial activities.