Lung changes after COVID-19: What are the functional differences across the spectrum of disease severity?

The pulmonary limitations after COVID-19 are still not completely known. Lung function test (LFT) and 6-minute walk test (6MWT) are accessible and safe tests to access them. Aim(s): To evaluate the differences between non-severe and severe COVID-19 patients regarding LFT and 6MWT. Method(s): This study included patients with previous COVID-19 assessed in Pulmonology Department at 2 hospitals during 7 months who performed LFT and 6MWT. Baseline and immediately pos-6MWT heart rate (HR), SpO2, respiratory rate (RR) and perceived symptoms using a modified BORG scale were collected. We compared nonsevere and severe patients. Result(s): We included 151 patients, 69 (45.7%) with severe disease. LFT was performed 116.8+/-68.3days and the 6MWT 129.1+/-72.3days after COVID-19, without statistical difference between groups. We documented lower %FVC (94.4+/-14.7vs101.1+/-12.6%, p=0.003), %TLC (95.4+/-15.3vs107.1+/-12.3%, p=0.000) and %DLCO (68.8+/-16.5vs78.9+/-15.9%, p=0.000) in the severe group, without statistical differences in FEV1, FEV1/FVC and KCO. The 6MWT distance (m: 426.5+/-110.9vs498.2+/-93.5m, p=0.000;%:77.3+/-16.8%vs86.1+/-13.4%, p=0.001), estimated metabolic equivalents (3.03+/-0.5vs3.4+/-0.4, p=0.000) and minimal SpO2 (92.0+/-3.3vs93.8+/-3.1%, p=0.000) were lower in the severe group. The time spent below 90%SpO2 (5.6+/-19.4vs2.6+/-13.6%, p=0.039), %age-predicted maximal HR (68.5+/-10.5vs64.9+/-8.8%, p=0.023) and initial RR (19.1+/-5.1vs18.7+/-9.3 cpm, p=0.014) were higher. We did not document differences regarding the differential (maximal-initial) HR, final RR, differential (final-initial) RR and symptoms. Conclusion(s): Severe group showed higher functional limitation, mainly in lung volumes and in submaximal exercise evaluation.

Mobile Application with AR as a Strategy to Improve the Marketing Process in a Dental Center

The post-pandemic period brought new challenges for businesses and private health centers, many of which were affected by the loss of customers. In the case of dental centers, many were affected by the distrust of customers, since activities performed in the oral cavity exposed them to the contagion of Covid-19. This research work proposes the implementation of a mobile application with Augmented Reality (AR) as a strategy for digital marketing immersion, to achieve a dynamic approach to the services provided in the dental center to customers, this is through the use of this technology in conjunction with social networks, contributing to the improvement of the business and building trust with customers. The application was developed under the Mobile-D methodology with a layered system development architecture, having as indicators the time of elaboration of the advertisement, the cost of information material, the time to inform the services, and the level of customer satisfaction. Finally, the results revealed that the time of elaboration of the advertisement decreased from 25 hours to 14 hours, the cost of informative material was considered "low" since the implementation of the application turns out to be economic, and the time to inform the services in its marketing process went from 30 min to 19 min with the use of the application, finally, the customer satisfaction increased being considered in 87% between "Good" and "Excellent". © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

Impacts of COVID-19 Pandemic Crisis in the Transportation Sector: A Classification Analysis in Regard with Preferred Modes of Transportation Using Random Forest Algorithm

The study observes the Pandemic Crisis (Covid 19) that resulted in impacts on the Transportation category in the area National Capital Region. Public transportation is an important aspect of human's ability to travel to different places whether its personal or business purpose, it's a part of life that people take for granted and can't be taken away easily. But due to the pandemic era, people have been careful in their choices, which resulted in the change standard when it comes to public transportation choices. With that said, to understand and observe these impacts, a scenario must be made such as before and after the pandemic designed as an environment for the study to take root. The study has used machine learning called Random Forest Algorithm with the used several parameters to create a prediction model. As for the method in gathering data, a survey of Google Form is utilized to gather 200 participants of the National Capital Region with varying parameters for their choice of public transportation. The machine algorithm has shown satisfactory accuracy of 89.88% and 88.88%. As an important note, it is observed that travel expense has more impact on public transportation choices than other parameters. The Random Forest Algorithm has been utilized in creating classification types of models and can help future researchers improve the machine learning approach. © 2021 IEEE.