Recommendation system for emotional self-control of older adults post-COVID-19 in Panama

The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. This research will contribute to the elderly population in Panama having a mobile application which is beneficial as a non-pharmaceutical alternative to cope with psychological conditions caused by the Covid-19 disease. Regarding the most relevant limitations we have are the acquisition of the data set for training. As future works, we hope to have a more robust architecture to implement it in other activities related to the heath self-control of Panamanian patients. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

The Impact of Steroids on Outcomes of Children Admitted to the Hospital with Non Misc Covid19: An Analysis from the Sccm Discovery Network Virus Registry

BACKGROUND AND AIM: Evidence for therapies for pediatric COVID-19 is limited. Primary aim was to study the effect of steroid administration within 2 days of admission for pediatric non-MIS-C-COVID-19 on hospital and ICU length of stay (LOS). The secondary aim was to study its effect on inflammation and fever defervescence. METHOD(S): A retrospective study of 1163 children hospitalized with non-MISC-COVID-19, from 03/20 to 09/21, from 58 hospitals (7 countries, 92% US), in the Viral Infection and Respiratory Illness Universal Study (VIRUS) registry. Effect of steroid administration <= 2 days of admission on hospital and ICU LOS was studied using intention to treat analysis, adjusted for confounders by multivariable mixed linear regression. RESULT(S): Median age was 7(IQR 0.9,14.3) years. 184(15.8%) children who received steroids within <= 2 days were compared to 979 (84.1%) children who did not. 56.5% (n=658) required respiratory support. Patients in the steroid group were older, with higher severity of illness. A greater proportion required respiratory and vasoactive support. On multivariable linear regression with random intercept for site (Table), there was no significant difference in hospital LOS (exponentiated [exp] co-efficient 0.92, 95%CI = 0.77, 1.10, p=0.374) or ICU LOS (exp co-efficient 1.02, 95%CI = 0.78, 1.34, p=0.864) between the groups. There was no significant difference in time to fever defervescence and normalization of inflammatory mediators by Day 3. CONCLUSION(S): In pediatric non-MIS-C COVID-19, steroid treatment <= 2 days of hospital admission did not show a statistically significant effect on hospital or ICU LOS. (Table Presented).

Sentiment Analysis of Public Social Media as a Tool for Health -Related Topics

For decades, researchers have experimented with the possibility that machines can equal human linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest. Author

Deep learning and machine learning approach applied to the automatic classification of opinions on Twitter in the Covid-19 pandemic in Panama

Twitter is an important social network and information channel where opinions (tweets) can be obtained and processed in real time that can be explored, analyzed and organized to make better decisions. Opinion mining is a natural language processing task that identifies user opinions as positive, negative, or neutral. COVID-19 is an infectious disease caused by the coronavirus that appeared in December 2019 in China and immediately provoked a large number of opinions. To allow Panamanian health organizations to detect opportunities to improve the quality of medical care, we propose to classify the tweets the analysis of two approaches: deep learning and machine learning for to appreciate which is more precise. We obtained encouraging results with a precision of 95.6%. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Manufacturing Processes of SER-109, a Purified Investigational Microbiome Therapeutic, Reduce Risk of Transmission of Emerging and Undetected Infections in Donor Stool

Background. Fecal microbiota transplantation (FMT) is vulnerable to emerging pathogens due to reliance on donor screening for risk mitigation. These concerns were highlighted by dual FDA safety alerts regarding FMT transmission of bacterial pathogens, which were recognized in hindsight only after hospitalizations and deaths. The FDA also warned of potential risk of SARS-CoV-2 transmission, leading to quarantine of FMT in March 2020, two months after COVID-19 was reported on US soil. Conversely, our development program for SER-109, an oral investigational microbiome therapeutic, was prospectively designed to inactivate organisms of concern, while purifying the hardy Firmicutes spores. We evaluated whether the manufacturing processes for SER-109 inactivate model organisms, including a coronavirus with gastrointestinal tropism, and a representative Gram-negative bacterium. Methods. Model organisms were selected based on biologic suitability, detectability, and laboratory safety. Porcine Epidemic Diarrhea Virus (PEDV, a coronavirus) was selected to model SARS-CoV-2. Quantitation used a Vero cell tissue culture infectious dose (TCID50) assay. For E. coli, a rifampicin-tolerant Salmonella enterica was selected and quantified with MacConkey lactose agar plus rifampicin. Spiking experiments into representative fecal suspensions were completed to measure inactivation of model organisms. Log-reduction factors (LRF) were calculated based on the drop in organism titer during inactivation. Hold controls in non-ethanolic test matrices were used to confirm specificity of the ethanol inactivation. Results. In 70% v/v ethanol, PEDV was inactivated by more than 4.2 log10 (to limit of detection, LOD) within 4 minutes (Fig1). In 50% v/v ethanol, S. enterica was inactivated by more than 6.5 log10 (to LOD) within 30 seconds (Fig2). Average of three experiments with error bars represent 95% CI. Also shown is the maximum achievable inactivation based on the limit of detection (LOD). Conclusion. These experiments demonstrate substantial inactivation of the model organisms and support the potential benefit of SER-109 manufacturing process to mitigate risks of undetected or emerging pathogens for which reliable screening is limited. Ethanol exposure leads to a purified investigational product of beneficial Firmicutes spores while affording a safety net beyond donor screening alone.

Geodesign Brazil: Trees for the Metropolitan Area of Sao Paulo

Trees are central in the Nature-based Solutions for promoting simultaneously quality of life and biodiversity while providing mitigation and adaptive ecosystem services in the cities. Based on the Geodesign framework using the GIS-Colab Platform, the impact of decision-making scenarios on tree-cover changes, as well as the consequences it will have for carbon sequestration, was evaluated for 2020, 2035 and 2050 in the Metropolitan Area of Sao Paulo (MASP). This metropolitan area is one of the largest urban conglomerates in the world with more than 22 million people. It lies on the Atlantic Rainforest Biome, a tropical moist broadleaf forest regarded as a world hotspot of biodiversity. First, a diagnostic of the current conditions was elaborated using available layers of geospatial data from the MASP. Then the future tree cover was discussed according to three scenarios: i) the non-adopters that represent the business as usual;ii) the late-adopters that develop innovative actions from 2035;and iii) early-adopters that undertake innovative interventions of urban greening from 2020. The vegetation cover was estimated to be reduced by 4% considering the current non-adopter scenario by 2050. On the other hand, vegetation cover has the potential to increase 30% in 2050, once there is an early adoption of innovative interventions, promoting various ecosystem services and co-benefits that support the quality of life and the biodiversity in the MASP, while fostering the carbon credit in the city through vegetation carbon sink. This article points to possible pathways required to attain desired afforestation goals in the MASP following the Geodesign framework. This framework proved to be effective even though it was based only on remote meetings, imposed by the social distancing during the pandemic of COVID-19.