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FOSSIL FUEL ENVIRONMENTAL CONTAMINATION: A STRATEGY USING RADIOCARBON, N-ALKANES, AND ALGAE
- Túlio César Aguiar Silva, Carla Carvalho, Bruno Libardoni, Kita Macario, Felippe Braga de Lima, Mariana Cruz Pimenta, Maria Isabela Nascimento de Oliveira, Marcelo Corrêa Bernardes, Gabriela da Silva Marques, Fernanda Pinto, Rosa de Souza, Diana Negrão Cavalcanti
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- Journal:
- Radiocarbon / Volume 63 / Issue 4 / August 2021
- Published online by Cambridge University Press:
- 09 June 2021, pp. 1165-1173
- Print publication:
- August 2021
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Fossil fuels are of utmost importance to the world we live in today. However, their use can cause major impacts on the environment, especially on water resources. In this regard, algae have been intensively used as a strategy for remediation and monitoring of environmental pollution due to its efficient absorption of contaminants. In this work, samples of seaweed collected in Niterói/RJ—contaminated with kerosene and diesel—were analyzed by radiocarbon (14C) accelerator mass spectrometry (AMS) and by n-alkane quantification with gas chromatography to evaluate bioaccumulation in function of the dosage of contaminants. The biogenic content measured by radiocarbon analysis resulted in 95.6% for algae contaminated with 10 mL of kerosene and 67.6% for algae contaminated with 10 mL of diesel. The maximum intensity of n-C17 n-alkane in algae with 5 mL, 10 mL, and 15 mL of diesel was 768.2, 1878.1, and 5699.2 ng.g-1, respectively. While the maximum concentration of n-C27 in algae with 5 mL, 10 mL and 15 mL of kerosene was 3.3, 35.9, and 150.3 ng.g-1. We concluded that, for both contaminants, their incorporation into algae increases as the contamination dosage increases, making this methodology an effective technique for monitoring and remediation of urban aquatic ecosystems.
Prevalence and risk factors of psychiatric symptoms and diagnoses before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 mental health cohort
- André Russowsky Brunoni, Paulo Jeng Chian Suen, Pedro Starzynski Bacchi, Lais Boralli Razza, Izio Klein, Leonardo Afonso dos Santos, Itamar de Souza Santos, Leandro da Costa Lane Valiengo, José Gallucci-Neto, Marina Lopes Moreno, Bianca Silva Pinto, Larissa de Cássia Silva Félix, Juliana Pereira de Sousa, Maria Carmen Viana, Pamela Marques Forte, Marcia Cristina de Altisent Oliveira Cardoso, Marcio Sommer Bittencourt, Rebeca Pelosof, Luciana Lima de Siqueira, Daniel Fatori, Helena Bellini, Priscila Vilela Silveira Bueno, Ives Cavalcante Passos, Maria Angelica Nunes, Giovanni Abrahão Salum, Sarah Bauermeister, Jordan W. Smoller, Paulo Andrade Lotufo, Isabela Martins Benseñor
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- Journal:
- Psychological Medicine / Volume 53 / Issue 2 / January 2023
- Published online by Cambridge University Press:
- 21 April 2021, pp. 446-457
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Background
There is mixed evidence on increasing rates of psychiatric disorders and symptoms during the coronavirus disease 2019 (COVID-19) pandemic in 2020. We evaluated pandemic-related psychopathology and psychiatry diagnoses and their determinants in the Brazilian Longitudinal Study of Health (ELSA-Brasil) São Paulo Research Center.
MethodsBetween pre-pandemic ELSA-Brasil assessments in 2008–2010 (wave-1), 2012–2014 (wave-2), 2016–2018 (wave-3) and three pandemic assessments in 2020 (COVID-19 waves in May–July, July–September, and October–December), rates of common psychiatric symptoms, and depressive, anxiety, and common mental disorders (CMDs) were compared using the Clinical Interview Scheduled-Revised (CIS-R) and the Depression Anxiety Stress Scale-21 (DASS-21). Multivariable generalized linear models, adjusted by age, gender, educational level, and ethnicity identified variables associated with an elevated risk for mental disorders.
ResultsIn 2117 participants (mean age 62.3 years, 58.2% females), rates of CMDs and depressive disorders did not significantly change over time, oscillating from 23.5% to 21.1%, and 3.3% to 2.8%, respectively; whereas rate of anxiety disorders significantly decreased (2008–2010: 13.8%; 2016–2018: 9.8%; 2020: 8%). There was a decrease along three wave-COVID assessments for depression [β = −0.37, 99.5% confidence interval (CI) −0.50 to −0.23], anxiety (β = −0.37, 99.5% CI −0.48 to −0.26), and stress (β = −0.48, 99.5% CI −0.64 to −0.33) symptoms (all ps < 0.001). Younger age, female sex, lower educational level, non-white ethnicity, and previous psychiatric disorders were associated with increased odds for psychiatric disorders, whereas self-evaluated good health and good quality of relationships with decreased risk.
ConclusionNo consistent evidence of pandemic-related worsening psychopathology in our cohort was found. Indeed, psychiatric symptoms slightly decreased along 2020. Risk factors representing socioeconomic disadvantages were associated with increased odds of psychiatric disorders.
Pattern Recognition Algorithms for Predicting Surgical Site Infection in Abdominal Hysterectomy
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Amanda Martins Fagundes, Beatriz Viana Ferreira Escalda, Isabela Marques de Souza, Laura Ferraz de Vasconcelos, Maria Eduarda Rodrigues Medeiros, Thais Azevedo de Almeida
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s344-s345
- Print publication:
- October 2020
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Background: This research represents an experiment based in surgical site infection (SSI) to patients undergoing abdominal hysterectomy surgery procedures in hospitals in Belo Horizonte, (population, 3 million). We statistically evaluated such incidences and studied the SSI prediction power of pattern recognition algorithms, the artificial neural networks based in multilayer perceptron (MLP). Methods: Between July 2016 and June 2018, data on SSI were collected by the hospital infection control committees (CCIH) of the 3 hospitals involved in the research. They collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH (ie, automated hospital infection control system software) to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed for SSI prediction: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation). MLPs were tested with 3, 5, 7, and 10 hidden-layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring area under the curve (AUC; range, 0–1) presented for each of the configurations. Results: From 1,166 records collected, only 665 records were enabled for analysis. Regarding statistical data: the average duration of surgery was 100 minutes (range, 31–180); patients were aged 41–49 years; the SSI rate was low (only 10 cases); the average length of stay was 2 days; and there were no deaths among the cases. Moreover, 29% of the operative sites were contaminated and 57% were potentially contaminated, revealing a high rate of potential contamination in the operative sites. The prediction process achieved 0.995. Conclusions: Despite the noise in the database, it was possible to obtain a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some settings achieved AUC results of 0.5, others achieved and AUC of 0.995, indicating the promise of the automated SSI monitoring framework for abdominal hysterectomy surgery (available in www.sacihweb.com). To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.
Funding: None
Disclosures: None