The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis. For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models. Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91]. These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China CDC has reported results of a descriptive exploratory analysis of all cases diagnosed until the 11th February 2020, presenting the epidemiologic curves and geo-temporal spread of COVID-19 along with case fatality rate according to some baseline characteristics, such as age, gender and several well-established high prevalence comorbidities. Despite this, we intend to increase even further the predictive value of that manuscript by presenting the odds ratio for mortality due to COVID-19 adjusted for the presence of those comorbidities and baseline characteristics such as age and gender. Besides, we present a way to determine the risk of each particular patient, given his characteristics. We found that age is the variable that presents higher risk of COVID-19 mortality, where 60 or older patients have an OR = 18.8161 (CI95%[7.1997; 41.5517]). Regarding comorbidities, cardiovascular disease appears to be the riskiest (OR= 12.8328 CI95%[10.2736; 15.8643], along with chronic respiratory disease (OR=7.7925 CI95%[5.5446; 10.4319]). Males are more likely to die from COVID-19 (OR=1.8518 (CI95%[1.5996; 2.1270]). Some limitations such as the lack of information about the correct prevalence of gender per age or about comorbidities per age and gender or the assumption of independence between risk factors are expected to have a small impact on results. A final point of paramount importance is that the equation presented here can be used to determine the probability of dying from COVID-19 for a particular patient, given its age interval, gender and comorbidities associated.

COVID-19 is having a great impact on public health, mortality and economy worldwide, in spite of the efforts to prevent its epidemy. The SARS-CoV-2 genome is different from that of MERS-CoV and SARS-CoV, although also expected to spread differently according to meteorological conditions. Our main goal is to investigate the role of some meteorological variables on the expansion of this outbreak. In this study, an exponential model relating the number of accumulated confirmed cases and time was considered. The rate of COVID-19 spread, using as criterion the doubling time of the number of confirmed cases, was used as dependent variable in a linear model that took four independent meteorological variables: temperature, humidity, precipitation and wind speed. Only China cases were considered, to control both cultural aspects and containment policies. Confirmed cases and the 4 meteorological variables were gathered between January 23 and March 1 (39 days) for the 31 provinces of Mainland China. Several periods of time were sampled for each province, obtaining more than one value for the rate of disease progression. Two different periods of time were tested, of 12 and 15 days, along with 3 and 5 different starting points in time, randomly chosen. The median value for each meteorological variable was computed, using the same time period; models with were selected. The rate of progression and doubling time were computed and used to fit a linear regression model. Models were evaluated using α = 0.05. Results indicate that the doubling time correlates positively with temperature and inversely with humidity, suggesting that a decrease in the rate of progression of COVID-19 with the arrival of spring and summer in the north hemisphere. A 20°C increase is expected to delay the doubling time in 1.8 days. Those variables explain 18% of the variation in disease doubling time; the remaining 82% may be related to containment measures, general health policies, population density, transportation or cultural aspects.

Three-dimensional technologies are one of the most recent and relevant advancements in the field of Dentistry. These systems, including intraoral scans, 3D imaging exams (CAT scan, CBCT and MRI), CAD/CAM 3D printing devices and 3D computer software, have enabled clinicians to greatly improve patient care along with reducing treatment planning time. The present descriptive study aims to explore possible applications of 3D technologies during the diagnosis, treatment plan, case monitoring and result assessment in orthodontics. The overall upgrade provided by these technologies can improve the clinicians’ workflow and effectiveness by simplifying conventional techniques considered to be especially arduous.

Endodontic biofilms eradication from the infected root canal system remains as the primary focus in endodontic field. In this study it was assessed the efficacy of antimicrobial Photodynamic Therapy (aPDT) with the Zn(II)chlorin e6 methyl ester (Zn(II)e6Me) activated by red light against monospecies and mixed biofilms of Enterococcus faecalis and Candida albicans. The results were compared with the ones obtained with Rose Bengal (RB), Toluidine Blue-O (TBO), the synthetic tetracationic porphyrin (TMPyP) as well as classical endodontic irrigants (3% NaOCl, 17% EDTA and 2% CHX). The antimicrobial efficacy of aPDT towards monospecies and mixed biofilms was quantified resorting to safranin red method. The changes of biofilm organization and of cellular ultrastructure were evaluated through several microscopy techniques (light, laser confocal and transmission electron microscopy). Zn(II)e6Me once activated with light for 60 or 90 s was able to remove around 60% of the biofilm’s biomass. It was more efficient than TBO and RB and showed similar efficiency to TMPyP and classical irrigants, CHX and EDTA. As desirable in a PS, Zn(II)e6Me in the dark showed smaller activity than TMPyP. Only NaOCl revealed higher efficiency, with 70-90% of the biofilm’s biomass removal. The organization of biofilms and the normal microbial cell ultrastructure were extensively damaged by the presence of Zn(II)e6Me. aPDT with Zn(II)e6Me showed to be an efficient antimicrobial strategy deserving further studies leading to a future clinical usage in endodontic disinfection.