Waveguides are structures that can be used as sensors. After adding a cladding of a proper material, waveguides can be used for detecting specific chemical species. In particular, high band gap oxide semiconductors can be used as cladding for optical waveguides. In these materials, the synthesis processes are expensive or contaminant generators, for this reason novel approaches are required. This work addresses a simple synthesis method for the preparation of zinc oxide nanoparticles using Citrus reticulata extract. XRD presented only the wurtzite structure for the zinc oxide nanoparticles, while TEM showed size and shape homogeneity. The ZnO band gap was lower than commercial ZnO. Using a computer simulation and frequency dependent electrical characteristics, a waveguide model was tested. The resulting characteristics presented great parameters for using in optoelectronic sensors. Resulting ZnO NPs have negligible amounts of green matter afterwards, making this a non-toxic method. As a result, herein presented synthesis is a promising method for developing new optoelectronic sensors. These devices can be used for automatic detection of chemical species by automated systems.

COVID-19 vaccination programs continue in child populations. Thus, parents’ attitude towards COVID-19 vaccination of their children is crucial for these strategies to succeed. The present study derives from the application of an online COVID-19 Vaccine Acceptance & Hesitancy Questionnaire (COV-AHQ) in which we measure parent’s hesitancy towards children’s vaccination (section 4 of the COV-AHQ) and other significant factors. A logistic regression analysis with backward stepwise method was used to quantify the associations between factors and parent’s hesitancy. According to the correlation analysis, the most representative factors predicting vaccine hesitancy/acceptance were positive attitude towards vaccination, parents believing that the COVID-19 vaccine will enhance the economic situation of the country, parents actively researching information, having the willingness to obtain the COVID-19 vaccine themselves, and the possibility of their children developing adverse effects. Our findings also showed that parents are highly interested in having their children vaccinated. Nonetheless, parents expressed high levels of concern involving their children in developing adverse effects from the vaccine. In addition, obtaining influenza immunization prompted interest in obtaining the COVID-19 vaccine, and younger-aged parents are much more concerned with having their children vaccinated. Therefore, in order to ensure successful vaccination programs, policymakers and health authorities should design strategies to gain confidence and provide security amongst the population, including giving continuous information about the benefits of vaccination and presenting the frequency of side effects to bring parents on board with vaccinating their children.

For the last decade, several studies on mid-IR spectroscopy for blood glucose quantification have not considered the compounds involved in the glucose regulation mechanism, in which insulin plays an important role. This work shows how insulin overlaps in the same mid-IR region in which glucose is quantified. This optical absorption interference is an important factor to be considered for this type of studies, in the scope of whole blood modeling for spectroscopy applications and the possible use of computer based metrics. Keywords: Biomedical Optics, Glucose quantification, Whole blood, Insulin, FTIR Spectroscop

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.

The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD.