Publications by authors named "Tamara Teixeira"

5 Publications

  • Page 1 of 1

Ruthenium (II)/allopurinol complex inhibits breast cancer progression via multiple targets.

J Biol Inorg Chem 2021 Apr 10. Epub 2021 Apr 10.

Laboratório de Genética Molecular E Citogenética Humana, sala 213, Departamento de Genética, Instituto de Ciências Biológicas I, Campus Samambaia, Universidade Federal de Goiás, Avenida Esperança, s/n, Cx Postal: 131, Goiânia, Goiás, CEP 74690-900, Brazil.

Metal complexes based on ruthenium have established excellent activity with less toxicity and great selectivity for tumor cells. This study aims to assess the anticancer potential of ruthenium(II)/allopurinol complexes called [RuCl(allo)(PPh)] (1) and [RuCl(allo)(dppb)] (2), where allo means allopurinol, PPh is triphenylphosphine and dppb, 1,4-bis(diphenylphosphino)butane. The complexes were synthesized and characterized by elemental analysis, IR, UV-Vis and NMR spectroscopies, cyclic voltammetry, molar conductance measurements, as well as the X-ray crystallographic analysis of complex 2. The antitumor effects of compounds were determined by cytotoxic activity and cellular and molecular responses to cell death mechanisms. Complex 2 showed good antitumor profile prospects because in addition to its cytotoxicity, it causes cell cycle arrest, induction of DNA damage, morphological and biochemical alterations in the cells. Moreover, complex 2 induces cell death by p53-mediated apoptosis, caspase activation, increased Beclin-1 levels and decreased ROS levels. Therefore, complex 2 can be considered a suitable compound in antitumor treatment due to its cytotoxic mechanism.
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http://dx.doi.org/10.1007/s00775-021-01862-yDOI Listing
April 2021

Oncology nursing workforce: challenges, solutions, and future strategies.

Lancet Oncol 2020 12 16;21(12):e564-e574. Epub 2020 Nov 16.

Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, Netherlands.

The global oncology nursing workforce is essential to achieving Sustainable Development Goals 3.4 (reduce non-communicable disease morbidity by a third by 2030) and 3.8 (universal health coverage). Unfortunately, challenges to a robust oncology nursing workforce include nursing shortages, recruitment barriers (eg, perceptions of a demanding specialty with complex care and hazardous work environments), and burnout. Innovative recruitment strategies, onboarding and continuing education programmes, occupational safety measures, and burnout prevention interventions are documented solutions. The long-term effect of COVID-19 on oncology care worldwide is unknown, but immediate therapy interruptions, workforce consequences, and threats to standard oncology nursing practice are addressed here. Retention of experienced oncology nurses is crucial for future cancer control in all countries and must be addressed, particularly in resource-constrained countries with few oncology nursing staff and continuing out-migration of nurses to resource-rich countries. As the cancer burden worldwide increases, the future of the oncology nursing workforce is reflected in the call from the International Council of Nurses, Nursing Now, and WHO for nurses to move to higher levels of leadership, advocacy, and policy making (ie, national cancer control planning) and assume responsibility for their key role in achieving global goals for cancer control.
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http://dx.doi.org/10.1016/S1470-2045(20)30605-7DOI Listing
December 2020

Determination of eccentric deposition thickness on offshore horizontal pipes by gamma-ray densitometry and artificial intelligence technique.

Appl Radiat Isot 2020 Nov 26;165:109221. Epub 2020 Jun 26.

Laboratório de Instrumentação Nuclear (COPPE/UFRJ), P.O Box 68509, Rio de Janeiro, RJ, Brazil.

The extraction of oil is accompanied by water and sediments that, mixed with the oil, cause the formation of scale depositions in the pipelines walls promoting the reduction of the inner diameter of the pipes, making it difficult for the fluids to pass through interest. In this sense, there is a need to control the formation of these depositions to evaluate preventive and corrective measures regarding the waste management of these materials, as well as the optimization of oil extraction and transport processes. Noninvasive techniques such as gamma transmission and scattering can support the determination of the thickness of these deposits in pipes. This paper presents a novel methodology for prediction of scale with eccentric deposition in pipes used in the offshore oil industry and its approach is based on the principles of gamma densitometry and deep artificial neural networks (DNNs). To determine deposition thicknesses, a detection system has been developed that utilizes a 1 mm narrow beam geometry of collimation aperture comprising a source of Cs and three properly positioned 2″×2″ NaI(Tl) detectors around the system, pipe-scale-fluid. Crude oil was considered in the study, as well as eccentric deposits formed by barium sulfate, BaSO. The theoretical models adopted a static flow regime and were developed using the MCNPX mathematical code and, secondly, used for the training and testing of the developed DNN model, a 7-layers deep rectifier neural network (DRNN). In addition, the hyperparameters of the DRNN were defined using a Baysian optimization method and its performance was validated via 10 experiments based on the K-Fold cross-validation technique. Following the proposed methodology, the DRNN was able to achieve, for the test sets (untrained samples), an average mean absolute error of 0.01734, mean absolute relative error of 0.29803% and R2 Score of 0.9998813 for the scale thickness prediction and an average accuracy of 100% for the scale position prediction. Therefore, the results show that the 7-layers DRNN presents good generalization capacity and is able to predict scale thickness with great precision, regardless of its position inside the tube.
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http://dx.doi.org/10.1016/j.apradiso.2020.109221DOI Listing
November 2020

A new application of radioactive particle tracking using MCNPX code and artificial neural network.

Appl Radiat Isot 2019 Jul 10;149:38-47. Epub 2019 Apr 10.

Instituto de Engenharia Nuclear (IEN / CNEN - RJ), Rua Hélio de Almeida 75, 21941-906, Cidade Universitária, RJ, Brazil. Electronic address:

Stirrers and mixers are highly used in chemical, food, pharmaceutical, cosmetic, concrete industries and others. During the fabrication process, the equipment may fail to appropriately stir or mix the solution. Besides that, it is also important to determine when the right homogeneity of the mixture is attained. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and maintain market competitiveness. Nuclear techniques, such as gamma densitometry, are widely used in industry to overcome a sort of difficulties, as they are minimally non-invasive techniques. This paper presents a method based on the principles of the radioactive particle tracking technique to predict the instantaneous position of a radioactive particle to monitor a concrete mixture inside an industrial unit by means of Monte Carlo method and artificial neural network. Counts obtained by an array of detectors properly positioned around the mixing canister will be correlated to each other, by means of an appropriate mathematical search location algorithm, in order to predict the instantaneous positions occupied by an inserted radioactive particle. The simulation consists of a detection geometry of eight NaI(Tl) scintillator detectors, a 662 keV Cs point source with isotropic emission of gamma-rays and a polyvinyl chloride tank. At first, the tank is air filled and, afterwards, filled with concrete made with Portland cement. The modeling of the detection system is performed using the MCNPX code. For both medium, the correlation coefficient was 0.99 for all coordinates, which indicates that this methodology could be a good tool to evaluate industrial mixers.
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http://dx.doi.org/10.1016/j.apradiso.2019.04.011DOI Listing
July 2019

Inorganic scale thickness prediction in oil pipelines by gamma-ray attenuation and artificial neural network.

Appl Radiat Isot 2018 Nov 10;141:44-50. Epub 2018 Aug 10.

Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970 Rio de Janeiro, Brazil. Electronic address:

Scale can be defined as chemical compounds that are inorganic, initially insoluble, and precipitate accumulating on the internal walls of pipes, surface equipment, and/or parts of components involved in the production and transport of oil. These compounds, when precipitating, cause problems in the oil industry and consequently result in losses in the optimization of the extraction process. Despite the importance and impact of the precipitation of these compounds in the technological and economic scope, there remains difficulty in determining the methods that enable the identification and quantification of the scale at an initial stage. The use of gamma transmission technique may provide support for a better understanding of the deposition of these compounds, making it a suitable tool for the noninvasive determination of their deposition in oil transport pipelines. The geometry used for the scale detection includes a 280-mm diameter steel tube containing barium sulphate (BaSO) scale ranging from 0.5 to 6 cm, a gamma radiation source with divergent beam, and a NaI(Tl) 2 × 2″ scintillation detector. The opening size of the collimated beam was also evaluated (2-7 mm) to quantify the associated error in calculating the scale. The study was done with computer simulation, using the MCNP-X code, and the results were validated using analytical equations. Data obtained by the simulation were used to train an artificial neural network (ANN), thereby making the study system more complex and closer to the real one. The input data provided for the training, testing, and validation of the network consisted of pipes with 4 different internal diameters (D1, D2, D3, and D4) and 14 different scale thicknesses (0.5 to 7 cm, with steps of 0.5 cm). The network presented generalization capacity and good convergence, with 70% of cases with less than 10% relative error and a linear correlation coefficient of 0.994, which indicates the possibility of using this study for this purpose.
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http://dx.doi.org/10.1016/j.apradiso.2018.08.008DOI Listing
November 2018