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One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat. The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose. The MobileNetV2 architecture was used based on the good performance shown when compared with the Inception architecture for this task; and its low computational cost, which facilitates implementing the final model on a Raspberry Pi 4B. The resulting image dataset consisted of 102 empty seats, 71 children (0-13 years), and 96 adults (14-75 years). From the data augmentation, there were 2,496 images for adults and 2,310 for children. The classification of faces without sliding window gave a result of 98% accuracy and 100% precision. Finally, using the proposed methodology, it was possible to detect children in the front passenger seat in real time, with a delay of 1 s per decision and sliding window criterion, reaching an accuracy of 100%. Although our 100% accuracy in an experimental environment is somewhat idealized in that the sensor was not blocked by direct sunlight, nor was it partially or completely covered by dirt or other debris common in vehicles transporting children. The present study showed that is possible the implementation of a robust noninvasive classification system made on Raspberry Pi 4 Model B in any automobile for the detection of a child in the front seat through deep learning methods such as Deep CNN. [ABSTRACT FROM AUTHOR]

Introduction. Candida auris is a relatively novel pathogen first described in 2009 in Japan. It has increased its presence worldwide, becoming a public health concern due to its innate resistance to antifungals and outbreak potential. Methods. We performed a query using the word "Candida auris" from the Scopus database, further performing a bibliometric analysis with the open-source R package Bibliometrix. Results. 907 original articles were retrieved, allowing us to map the principal authors, papers, journals, and countries involved in this yeast research, as well as analyze current and future trends and the number of published articles. Conclusion. C. auris will continue to be a pivotal point in fungal resistance research, either for a better understanding of its resistance and pathogenic mechanisms or for developing novel drugs. [ABSTRACT FROM AUTHOR]

Research on the microbiome has drawn an increasing amount of attention over the past decade. Even more so for its association with disease. Neurodegenerative diseases, such as Alzheimer's disease (AD) have been a subject of study for a long time with slow success in improving diagnostic accuracy or identifying a possibility for treatment. In this work, we analyze past and current research on microbiome and its positive impact on AD treatment and diagnosis. We present a bibliometric analysis from 2012 to 2021 with data retrieved on September 2, 2021, from the Scopus database. The query includes "Gut AND (Microbiota OR Microbiome) AND Alzheimer" within the article title, abstract, and keywords for all kinds of documents in the database. Compared with 2016, the number of publications (NPs) on the subject doubled by 2017. Moreover, we observe an exponential growth through 2020, and with the data presented, it is almost certain that it will continue this trend and grow even further in the upcoming years. We identify key journals interested in the subject and discuss the articles with most citations, analyzing trends and topics for future research, such as the ability to diagnose the disease and complement the cognitive test with other clinical biomarkers. According to the test, mild cognitive impairment (MCI) is normally considered an initial stage for AD. This test, combined with the role of the gut microbiome in early stages of the disease, may improve the diagnostic accuracy. Based on our findings, there is emerging evidence that microbiota, perhaps more specifically gut microbiota, plays a key role in the pathogenesis of diseases, such as AD. [ABSTRACT FROM AUTHOR]

Early detection of Alzheimer's disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD. [ABSTRACT FROM AUTHOR]