Publications by authors named "Alex Álisson Bandeira Santos"

5 Publications

  • Page 1 of 1

Potential application of novel technology developed for instant decontamination of personal protective equipment before the doffing step.

PLoS One 2021 4;16(6):e0250854. Epub 2021 Jun 4.

SENAI CIMATEC, SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), University Center SENAI/CIMATEC, Salvador, Bahia, Brazil.

The use of personal protective equipment (PPE) has been considered the most effective way to avoid the contamination of healthcare workers by different microorganisms, including SARS-CoV-2. A spray disinfection technology (chamber) was developed, and its efficacy in instant decontamination of previously contaminated surfaces was evaluated in two exposure times. Seven test microorganisms were prepared and inoculated on the surface of seven types of PPE (respirator mask, face shield, shoe, glove, cap, safety glasses and lab coat). The tests were performed on previously contaminated PPE using a manikin with a motion device for exposure to the chamber with biocidal agent (sodium hypochlorite) for 10 and 30s. In 96.93% of the experimental conditions analyzed, the percentage reduction was >99% (the number of viable cells found on the surface ranged from 4.3x106 to <10 CFU/mL). The samples of E. faecalis collected from the glove showed the lowest percentages reduction, with 86.000 and 86.500% for exposure times of 10 and 30 s, respectively. The log10 reduction values varied between 0.85 log10 (E. faecalis at 30 s in glove surface) and 9.69 log10 (E. coli at 10 and 30 s in lab coat surface). In general, E. coli, S. aureus, C. freundii, P. mirabilis, C. albicans and C. parapsilosis showed susceptibility to the biocidal agent under the tested conditions, with >99% reduction after 10 and 30s, while E. faecalis and P. aeruginosa showed a lower susceptibility. The 30s exposure time was more effective for the inactivation of the tested microorganisms. The results show that the spray disinfection technology has the potential for instant decontamination of PPE, which can contribute to an additional barrier for infection control of healthcare workers in the hospital environment.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250854PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177472PMC
June 2021

Numerical and experimental analyses for the improvement of surface instant decontamination technology through biocidal agent dispersion: Potential of application during pandemic.

PLoS One 2021 19;16(5):e0251817. Epub 2021 May 19.

SENAI CIMATEC, National Service of Industrial Learning-SENAI, Computational Modeling and Industrial Technology, University Center SENAI/CIMATEC, Salvador, Bahia, Brazil.

The transmission of SARS-CoV-2 through contact with contaminated surfaces or objects is an important form of transmissibility. Thus, in this study, we evaluated the performance of a disinfection chamber designed for instantaneous dispersion of the biocidal agent solution, in order to characterize a new device that can be used to protect individuals by reducing the transmissibility of the disease through contaminated surfaces. We proposed the necessary adjustments in the configuration to improve the dispersion on surfaces and the effectiveness of the developed equipment. Computational Fluid Dynamics (CFD) simulations of the present technology with a chamber having six nebulizer nozzles were performed and validated through qualitative and quantitative comparisons, and experimental tests were conducted using the method Water-Sensitive Paper (WSP), with an exposure to the biocidal agent for 10 and 30 s. After evaluation, a new passage procedure for the chamber with six nozzles and a new configuration of the disinfection chamber were proposed. In the chamber with six nozzles, a deficiency was identified in its central region, where the suspended droplet concentration was close to zero. However, with the new passage procedure, there was a significant increase in wettability of the surface. With the proposition of the chamber with 12 nozzles, the suspended droplet concentration in different regions increased, with an average increase of 266%. The experimental results of the new configuration proved that there was an increase in wettability at all times of exposure, and it was more significant for an exposure of 30 s. Additionally, even in different passage procedures, there were no significant differences in the results for an exposure of 10 s, thereby showing the effectiveness of the new configuration or improved spraying and wettability by the biocidal agent, as well as in minimizing the impact caused by human factor in the performance of the disinfection technology.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251817PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8133442PMC
June 2021

Supercritical Extraction of Red Propolis: Operational Conditions and Chemical Characterization.

Molecules 2020 Oct 20;25(20). Epub 2020 Oct 20.

School of Pharmacy, Federal University of Bahia (UFBA), Barão de Jeremoabo Street, 147, Salvador 40110-100, Bahia, Brazil.

The objective of this study was to determine the best operational conditions for obtaining red propolis extract with high antioxidant potential through supercritical fluid extraction (SFE) technology, using carbon dioxide (CO) as the supercritical fluid and ethanol as the cosolvent. The following parameters were studied: overall extraction curve, S/F (mass of CO/mass of sample), cosolvent percentage (0, 1, 2 and 4%) and global yield isotherms as a function of different pressures (250, 350 and 450 bar) and temperatures (31.7, 40 and 50 °C). Within the investigated parameters, the best conditions found were an S/F of 131 and the use of ethanol at the highest concentration (4% /), which resulted in higher extract yields and higher content of antioxidant compounds. Formononetin, the main biomarker of red propolis, was the compound found at the highest amounts in the extracts. As expected, the temperature and pressure conditions also influenced the process yield, with 350 bar and 40 °C being the best conditions for obtaining bioactive compounds from a sample of red propolis. The novel results for red propolis found in this study show that it is possible to obtain extracts with high antioxidant potential using a clean technology under the defined conditions.
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http://dx.doi.org/10.3390/molecules25204816DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587948PMC
October 2020

Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes.

PLoS One 2020 14;15(7):e0234959. Epub 2020 Jul 14.

University Center SENAI/CIMATEC, SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), Salvador, Bahia, Brazil.

The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and -classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234959PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360088PMC
September 2020

Application of convolutional neural networks for classification of adult mosquitoes in the field.

PLoS One 2019 14;14(1):e0210829. Epub 2019 Jan 14.

University Center SENAI CIMATEC, National Service of Industrial Learning-SENAI, Salvador, Bahia, Brazil.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210829PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331110PMC
October 2019
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