ABSTRACT
INTRODUCTION: Almost 281 million people were living in a foreign country in 2022, and more than 100 million were displaced because of war conflicts and human right violations. Vaccination coverage of infectious diseases in migrants from some disadvantaged settings could be lower than reception countries populations, consequently seroprevalence studies and better access to vaccination could contribute to reducing these differences. METHODS: A descriptive retrospective cross-sectional study was conducted including migrants, living ≤5 years in the reception country and ≥16 years old, who requested a medical exam between January 1st, 2020 and January 31st, 2021. Seroprevalence assessment was performed, and vaccination was offered to those individuals without immunity to hepatitis B, hepatitis A, varicella, measles, mumps, and rubella. RESULTS: A total of 315 migrants were attended during the study period. Immunity protection at arrival was 252/296 (85.1%) for measles, 274/295 (92.9%) for rubella, 257/296 (86.8%) for mumps, 264/295 (89.5%) for varicella, 267/313 (85.3%) for hepatitis A, and 104/300 (34.6%) for hepatitis B. The final immunity protection after full vaccination schedules was 278/296 (93.9%) for measles, 287/295 (97.3%) for rubella, 274/296 (92.6%) for mumps, 276/295 (93.6%) for varicella, 280/313 (89.5%) for hepatitis A, and 139/300 (46.3%) for hepatitis B. CONCLUSIONS: The vaccination intervention has increased immunity rates for the studied diseases in the attended migrants in our center, however, such interventions should be maintained to reach local population immunization levels. Moreover, the collaboration between shelter and reference specialized health centers is fundamental to implement such vaccination programs.
ABSTRACT
In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.
