Alejandro I Trejo Castro

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.

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.

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.

Introduction: Hematohidrosis and hemolacria are 2 conditions surrounded in religiousness, mysticism, and supernatural superstitions. While the mechanism is still unclear, these cases have amazed physicians for centuries. Methods: We performed a systematic review in PubMed from 2000 to mid-2021 accounting for 75 studies from which we included 60 cases in 53 articles which were described. Results: The median age of apparition was 24 years with the youngest case being 12 and the oldest 81. Some of the diseases were secondary to other causes such as hemangiomas and other neoplasias or epistaxis episodes. Most of the cases have been reported in India and the USA; most of them correspond to hemolacria alone (51.6%). Discussion: We have stated the basics of the substances involved in the coagulation process that have been described as genetically altered in some patients such as mucins, metalloproteinases, and fibrinogen, as well as propose a mechanism that can explain the signs of this particular entity and approach to its treatment as well as provide the first trichoscopy image of a patient with hemolacria.

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.

El propósito de este trabajo es analizar las ventajas y desventajas de la telemedicina durante la pandemia. Se realizó una revisión sistemática en Web of Science con las palabras clave "COVID-19 and Dermatology and Telemedicine" y "Telemedicine and Dermatology and Pandemic", arrojando 11 artículos, de los cuales nueve fueron analizados para esta revisión. Se encontró que algunos estudios reportan que más de 70% de las consultas físicas pudieron haber sido sustituidas por la telemedicina; además de que aún existe una resistencia a este tipo de consultas por los pacientes (Datos Wellmedic). La telemedicina es una herramienta que posee ventajas importantes durante tiempos de crisis, disminuyendo el riesgo de exposición de pacientes y médicos, así como una mejor distribución del uso de equipo personal de protección. Sin embargo, posee limitaciones en países en vías de desarrollo o poblaciones rurales por la falta de equipo y la resistencia de pacientes y médicos a utilizarla, además la carencia de un marco legal es clave en algunos países. Sin embargo, el costo-beneficio se considera mayor. Este es el primer artículo que habla acerca del impacto de la telemedicina durante la pandemia en México.

Alzheimer's disease (AD) is the most common type of dementia and predicting who will convert from Mild Cognitive Impairment (MCI) to AD is crucial to patient benefits as well as medical research. To fulfill this purpose, in recent years it has been reported that the texture of magnetic resonance images can be an effective biomarker. In this study we used images from the Alzheimer's Disease Neuroimaging Initiative database to create T2 maps and identify features related to the texture and signal distribution for the prediction of AD. We extracted 3S features from the left and right hippocampus for 40 patients with MCI who either progressed to AD (18) or remained stable (22) and measured the mean and absolute difference of these contralateral features. We also kept the original volume of each region, yielding a total of 7S features. We used 7 machine learning methods to analyze whether by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify who would develop the disease. We found 11 features significantly different between groups. Furthermore, all but one of the machine learning methods improved their accuracy by adding the signal- and texture-related features, and the volumetric information was non-significant. Our results suggest that these imaging features from hippocampal T2 maps should be further investigated as potential MRI biomarkers for the prediction of AD.

Coronavirus disease (COVID-19) is a new infectious disease that is rapidly spreading across the world. Many clinical manifestations of the virus have been described, and new symptoms are emerging, but only a few cases of skin manifestation have been described since the pandemic was announced by the World Health Organization in March.1, 2 Common clinical features of COVID-19 reported include fever, cough, myalgia, fatigue, headache, shortness of breath and diarrhoea.1 Some reports have suggested that skin manifestations are present in up to 20.4% of patients with COVID-19, and reports suggest a wide range of skin conditions being related to the virus, including widespread urticaria, morbilliform rash, erythematous rash, chickenpox-like vesicles, purpuric rash, dusky acrocyanosis, dry gangrene, petechiae rash coexisting with thrombocytopenia, transient livedo reticularis and red papules on fingers resembling chilblains.2-5 Unfortunately, some of these reports do not have clinical pictures to show the relation between COVID-19 and the skin manifestations. We report a family of five members living in the city of Monterrey, Mexico who tested positive for COVID-19, as a result of one of the members becoming infected during a trip to New York in February 2020. All five patients had anosmia, ageusia, chills and dizziness, and two of them had skin manifestations associated with COVID-19. Patient 1 was a 50-year-old woman, and Patient 2 was a 20-year-old woman, who was the daughter of Patient 1. Both patients had a bilateral disseminated rash on the shoulders, elbows, knees and buttocks, characterized by erythematous annular and irregular weals on the skin that appeared suddenly and disappeared within < 24 h (Fig. 1). The rash appeared after the cough and fever appeared. Neither patient had any history of similar lesions, and no trigger factors other than the viral context were identified. No other clinical or laboratory examinations were positive.

An early diagnosis of Alzheimer's disease (AD) is important for both support and therapeutic planning. Predicting who will progress from mild cognitive impairment (MCI) to AD would yield the same clinical benefits. However, it has been shown that the MCI to AD progression varies depending on certain demographic characteristics. AD is highly associated with the apolipoprotein E type 4 allele expressing the protein isoform APOE4. This study aimed at identifying features associated with the MCI to AD progression whose temporal evolution significantly differs between APOE4 carriers and non-carriers. Longitudinal information from 336 subjects (64.58% carriers) who progressed from MCI to AD was gathered, including laboratory assays, information from MRI and PET analyses, and neuropsychological tests. Longitudinal models identified 11 features with significant differences in their behavior between carriers and non-carriers, demonstrating that the way in which carriers and non-carriers progress from MCI to AD is significantly different.