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We aimed to comprehensively and systematically review studies associating key foot-loading factors (i.e., plantar pressure, weight-bearing activity, adherence or a combination thereof) with ulcer development and ulcer healing in people with diabetes. A systematic literature search was performed in PubMed and EMBASE. We included studies if barefoot or in-shoe plantar pressure, weight-bearing activity or footwear or device adherence was measured and associated with either ulcer development or ulcer healing in people with diabetes. Out of 1954 records, 36 studies were included and qualitatively analyzed. We found low to moderate quality evidence that lower barefoot plantar pressure and higher footwear and device adherence associate with lower risk of ulcer development and shorter healing times. For the other foot-loading factors, we found low quality evidence with limited or contradictory results. For combined measures of foot-loading factors, we found low quality evidence suggesting that lower cumulative plantar tissue stress is associated with lower risk of ulcer development and higher ulcer healing incidence. We conclude that evidence for barefoot plantar pressure and adherence in association with ulcer outcome is present, but is limited for the other foot-loading factors. More comprehensive investigation in particularly the combination of foot-loading factors may improve the evidence and targeting preventative treatment.Predicting risk for major adverse cardiovascular events (MACE) is an evidence-based practice that incorporates lifestyle, history, and other risk factors. Statins reduce risk for MACE by decreasing lipids, but it is difficult to stratify risk following initiation of a statin. Genetic risk determinants for on-statin MACE are low-effect size and impossible to generalize. Our objective was to determine high-level epistatic risk factors for on-statin MACE with GWAS-scale data. Controlled-access data for 5890 subjects taking a statin collected from Vanderbilt University Medical Center’s BioVU were obtained from dbGaP. We used Random Forest Iterative Feature Reduction and Selection (RF-IFRS) to select highly informative genetic and environmental features from a GWAS-scale dataset of patients taking statin medications. Variant-pairs were distilled into overlapping networks and assembled into individual decision trees to provide an interpretable set of variants and associated risk. 1718 cases who suffered MACE and 4172 controls were obtained from dbGaP. Pathway analysis showed that variants in genes related to vasculogenesis (FDR = 0.024), angiogenesis (FDR = 0.019), and carotid artery disease (FDR = 0.034) were related to risk for on-statin MACE. We identified six gene-variant networks that predicted odds of on-statin MACE. The most elevated risk was found in a small subset of patients carrying variants in COL4A2, TMEM178B, SZT2, and TBXAS1 (OR = 4.53, p less then 0.001). The RF-IFRS method is a viable method for interpreting complex “black-box” findings from machine-learning. In this study, it identified epistatic networks that could be applied to risk estimation for on-statin MACE. Further study will seek to replicate these findings in other populations.Objective. Our objective was to analyze the evolution of the information in Spanish online about the prevention of the coronavirus disease 2019 (COVID-19). Methods. On 1 March and 13 July 2020, two searches were conducted on Google with the terms “Prevencion COVID-19” and “Prevencion Coronavirus”. In each stage, a univariate analysis was performed to study the association of the authorship and country of origin with the basic recommendations to avoid COVID-19 provided by the World Health Organization (WHO). Results. A total of 120 weblinks were evaluated. The recommendation found most frequently in both stages was “wash your hands frequently” (93.3% in March vs. 90.0% in July). There was a significant increase in the detection of the following recommendations “avoid touching your face” (56.7% vs. 80.0%) and “stay at home if you feel unwell” (28.3% vs. 63.3%). Weblinks of official public health organizations more frequently provided the advice to “seek medical advice if you develop a fever/cough or have difficulty breathing”. Furthermore, in July, such weblinks provided recommendations to “avoid touching your face” and “maintain a distance of one meter” more frequently than the mass media (OR = 11.5 and 10.5, respectively). In March, the recommendation to “maintain a distance of at least 1 m” was associated with the weblinks from countries with local transmission/imported cases (OR = 8.1). Eganelisib Different/ambiguous information regarding the WHO recommendations was detected in four weblinks. Conclusion. The availability of information in Spanish online on basic prevention measures has improved over time, although there is still room for improvement. It is necessary to promote the use of the websites of official public health organizations among Spanish-speaking users.According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.