Objective: This study aimed to evaluate the psychological impacts of COVID-19 prevention measures, such as social isolation, on a Mexican sample. Methods: We conducted an online sociodemographic and Impact of Event Scale-Revised (IES-R) survey during the second phase of the COVID-19 outbreak in Mexico to evaluate the presence of psychological distress, signs of post-traumatic stress, and to identify the groups at highest risk in the sample. Results: Prevalence of psychological distress at moderate or severe levels in the sample were as follows: 943 (22%) intrusive thoughts, 933 (22.3%) avoidance, and 515 (12.2%) hyperarousal. Furthermore, we found the symptoms of clinically significant post-traumatic stress in 1160 (27.7%) of the participants. The variables positively correlated with higher psychological distress were as follows: age (younger), sex (female), employment (employed), relationship status (single), in social isolation, number of days in isolation, the number of people in the household (3-5), and a perception of a high risk of contracting COVID-19, change in routine, engaging in less activity, and loss of income. Conclusion: During phase 2 of the COVID-19 outbreak in Mexico, we observed the presence of psychological distress and post-traumatic stress symptoms in over a quarter of the population. This investigation may guide mental health interventions and policies towards the groups that are most vulnerable to the impacts of the social and lifestyle changes taking place in Mexico due to COVID-19.

Telomere shortening increases with the duration of diabetes. The time of exhibition suggests in parallel that the progressive increase of inflammation and/or oxidative stress plays a direct role in telomere shortening.

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

No abstract

No abstract