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Objective To determine the microbiota of mobile phones used during the ophthalmological consultation by medical personnel, patients, and family members. Methods An analysis was made on the mobile phones of the medical staff and of patients and/or family members in the area of clinical specialty. A survey was conducted to evaluate the pattern of use and disinfection of mobile phones. A smear sample was taken from the mobile phones. The specimens obtained were inoculated in culture media and incubated at 37 °C for 24 h. Genus and species were identified in the positive cultures and the results obtained were analyzed using descriptive statistics. Results An analysis was made on 71 mobile phones of medical personnel and 52 from patients and/or family members. The microorganisms isolated in the mobile phones of the ophthalmologists were: coagulase-negative staphylococci 50%, Staphylococcus aureus 32.4%, enterobacteria 4.2%, Actinomycetes 4.2%, and 9.8% were negative. On the other hand, in the phones of patients and relatives, the isolated microorganisms were S. aureus 75%, coagulase-negative staphylococci 24%, and enterobacteria 1%. Conclusions The results obtained show that mobile phones, both of the medical staff and of the patients and their relatives, contain bacteria considered pathogenic that could cause an infection. It is important to establish a routine practice of cleaning mobile phones and to make the population aware of hygiene habits, since they are responsible for the care of their eyes after consultation.

Context: The ordinary morphologic diagnosis of ALL by pathologists depends on examining patient peripheral blood together with the bone marrow blood films. However, this manual aspect for diagnosis is susceptible to discrepancies. We have a newly introduced technology that allows for overcoming individual variations in the diagnosis of ALL, so-named machine learning, depending on a complex preprogrammed convolutional network matrix. Objectives: Challenging machine-aided systems, which utilize microscopic blood film images to recognize and diagnose ALL based on a preprogrammed deep convolutional neural network (CNN), i.e., machine learning algorithms. Materials and Methods: We collected a dataset of images composed of PB & BM smear images of two classes; ALL and normal control blood. We analyzed 192 samples of digital images: 96 images of patients with ALL and 96 images of healthy normal controls. For each smear sample, we collected the results of clinical data (clinical history and examination) and laboratory data (morphological, cytochemical, and immunophenotyping assessment). We challenged seven types of CNN models: Alexnet, VGG16, VGG19, GoogLeNet, ResNet50, ResNet101, and Inception-v3 to diagnose ALL. Results: Comparing the ability of 7 models to diagnose ALL revealed that the Alexnet had the lowest accuracy of 95.51%, followed by VGG16 at 92.13% and VGG19 at 93.83%, whilst the Inception-v3 had a promising highest accuracy of 99.98%.and was able to detect almost all ALL cases. Conclusion: The statistical measures of the Inception-v3 performance revealed promising results. The sensitivity, specificity, and accuracy of Inception-v3 reached 99.98% for detection of ALL.

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi- or full-automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user-friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.