Background: Non-alcoholic fatty liver disease is a chronic disease caused by the accumulation of fat in the liver related to overweight and obesity, insulin resistance, hyperglycemia, and high levels of triglycerides and leads to an increased cardiovascular risk. It is considered a global pandemic, coinciding with the pandemic in 2020 caused by the “coronavirus disease 2019” (COVID-19). Due to COVID-19, the population was placed under lockdown. The aim of our study was to evaluate how these unhealthy lifestyle modifications influenced the appearance of metabolic alterations and the increase in non-alcoholic fatty liver disease. Methods: A prospective study was carried out on 6236 workers in a Spanish population between March 2019 and March 2021. Results: Differences in the mean values of anthropometric and clinical parameters before and after lockdown were revealed. There was a statistically significant worsening in non-alcoholic fatty liver disease (NAFLD) and in the insulin resistance scales, with increased body weight, BMI, cholesterol levels with higher LDL levels, and glucose and a reduction in HDL levels. Conclusions: Lockdown caused a worsening of cardiovascular risk factors due to an increase in liver fat estimation scales and an increased risk of presenting with NAFLD and changes in insulin resistance.

Obesity induces alterations in lipid biochemistry, evolving toward dyslipidaemia atherogenesis, a critical factor in the development of cardiovascular events. Two relevant forms of lipid abnormalities are atherogenic dyslipidaemia (AD) and lipid triad (LT), which involve alterations in triglyceride levels, HDL-c, and LDL-c. The aim of this study was to assess the linkage of atherogenic AD and LT with different scales of overweight and obesity. We carried out a cross-sectional study including 418,343 Spanish adult workers, recruited from workplace health assessments. Atherogenic dyslipidaemia was defined as triglyceride levels150mg/dL, HDL values<40mg/dL in men and 160mg/dL, LT was considered. Subjects affected by AD and LT in the study exhibited significantly higher mean values than those without AD and LT in all overweight, obesity, and body fat related scales studied. VAI (visceral adiposity index) was the strongest predictor of AD (AUC=0.934, 95 CI: 0.933 to 0.936) and LT (AUC=0.926, 95 CI: 0.923 to 0.928). Atherogenic dyslipidaemia and LT positively correlate with different scales of overweight and obesity. Further studies should aim to identify other contributory factors. Our obtained data might be useful in laying the groundwork for future works on AD and LT.

Background/Objectives: In a previous study, we utilized categorical variables and machine learning (ML) algorithms to predict the success of non-surgical root canal treatments (NSRCTs) in apical periodontitis (AP), classifying the outcome as either success (healed) or failure (not healed). Given the importance of radiographic imaging in diagnosis, the present study evaluates the efficacy of deep learning (DL) in predicting NSRCT outcomes using two-dimensional (2D) periapical radiographs, comparing its performance with ML models. Methods: The DL model was trained and validated using leave-one-out cross-validation (LOOCV). Its output was incorporated into the set of categorical variables, and the ML study was reproduced using backward stepwise selection (BSS). The chi-square test was applied to assess the association between this new variable and NSRCT outcomes. Finally, after identifying the best-performing method from the ML study reproduction, statistical comparisons were conducted between this method, clinical professionals, and the image-based model using Fisher’s exact test. Results: The association study yielded a p-value of 0.000000127, highlighting the predictive capability of 2D radiographs. After incorporating the DL-based predictive variable, the ML algorithm that demonstrated the best performance was logistic regression (LR), differing from the previous study, where random forest (RF) was the top performer. When comparing the deep learning–logistic regression (DL-LR) model with the clinician’s prognosis (DP), DL-LR showed superior performance with a statistically significant difference (p-value < 0.05) in sensitivity, NPV, and accuracy. The same trend was observed in the DL vs. DP comparison. However, no statistically significant differences were found in the comparisons of RF vs. DL-LR, RF vs. DL, or DL vs. DL-LR. Conclusions: The findings of this study suggest that image-based artificial intelligence models exhibit superior predictive capability compared with those relying exclusively on categorical data. Moreover, they outperform clinician prognosis.

Background/aim: The aim of this study was to determine the prevalence of cardiovascular risk factors (CVRF) and the level of cardiovascular risk (CVR), determined with different scales (REGICOR, SCORE, ERICE, vascular age...) in people with low and normal weight. Materials and Methods: A total of 192,711 underweight and normal weight Spanish workers participated. CVR parameters included were weight, abdominal circumference, blood pressure, glycemia (high >125 mg/dL or under hypoglycemic treatment) and lipids (cutoff points: total cholesterol 200 mg/dL, LDL 130 mg/dL, triglycerides 150 mg/dL) were obtained by automated enzymatic methods. Body mass index (BMI) was calculated, considering underweight 18.5 and normal weight 18.5-24.9. A descriptive analysis of the categorical variables was carried out. Results: The values of analytical, anthropometric and clinical parameters were more unfavorable in the normal weight group. Also, the prevalence of alterated values of CVR seen with different scales show higher risk in this group and in all cases the values in men are worse. It was seen that the variable with the greatest influence on the appearance of altered values of the cardiovascular risk scales (CVRS), influencing all of them was the age. Conclusion: All the CVRS analyzed showed higher values in the group of people with normal weight compared to those with underweight. Age, followed by sex, were the variables that most influence the appearance of high CVR values.

Introduction: The acquisition of skills and manual dexterity in aesthetic dentistry in undergraduate teaching requires preclinical practices with simulation that should approximate real clinical situations.Objective: We will present a digital procedure for the creation of three‐dimensional (3D) resin models adapted to the practices of the Aesthetic Dentistry course.Material and method: Stereolithography or Standard Tesellation Language (STL) files of a real patient were previously obtained with a 3‐shape intraoral scanner. Using the Exocad computer programme for dental prosthesis design, various modifications were made, such as incisor rotation, surface alterations imitating dysplasias or erosions, the creation of diastemas, or even changes in tooth size. The virtual model was printed in resin for use by students. Once the practices were finished, the students and the teachers evaluated the use of the 3D printed models.Results: The result is the typodont model, in which seven laboratory sessions took place: 1‐Restoration of conoid tooth morphology, 2‐Cervical abrasion/ erosion restoration, 3‐Direct Composite Veneer, 4‐Aesthetic correction in a tooth with rotation, 5‐Diastem closure, 6‐Occlusal abrasions/ erosion, and 7‐Maryland bridge. 90.48% of the students evaluated the designed 3D model as the best method for laboratory practice compared to other methods, obtaining a general assessment of 8.3 out of 10.Conclusion: The method used has provided a reproducible standard analog model for direct aesthetic dental restoration practice, with a good assessment by students and teachers.