Technological Applications of Porphyrins and Related Compounds: Spintronics and Micro-/Nanomotors

The vital role played by porphyrins in cells and their use in therapeutic processes are well known. More recently, the technological applications of porphyrins have attracted the attention of researchers. Porphyrins have the property of half-metallic material, i.e., molecules that can host transition metals making feasible the production of spin-polarized electronic states at different channels. Therefore, porphyrins and hemeproteins are among the materials that have spin-filtering property to be applied in spintronics. Molecular spintronics is an emerging and highly relevant field due to their applications to the development of high-capacity information-storage devices and quantum computers. The catalytic properties of porphyrins and related compounds such as the hemeproteins are also applicable in the fabrication of micro-/nanomotors (MNMs). In this chapter, we describe the advances and future perspectives in the technological applications of porphyrins and related compounds in spintronic devices and micro-/nanomotors

Cardiotoxicity in haematological diseases : are the tyrosine kinase inhibitors imatinib and nilotinib safe?

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017.Introduction: Chemotherapy-induced cardiotoxicity is a growing concern. The true cardiotoxic impact of new drugs such as tyrosine kinase inhibitors is unknown, especially the ones used for chronic myeloid leukaemia. We aim to evaluate nilotinib and imatinib induced cardiotoxicity. Methods: Single-center prospective study of consecutive patients with chronic myeloid leukaemia treated with tyrosine kinase inhibitors during 2015. Patients underwent an initial clinical, laboratorial and echocardiographic evaluation, repeated one year after therapy initiation. Results: Eleven patients were included [60.0 (11) years, 63.6% of males; 7 patients treated with imatinib and 4 with nilotinib]. After one year of follow-up, all patients remained in functional NYHA class I, with a similar Minnesota quality of life score [21 (20) vs. 21 (19), p = NS]. Also there was no difference in the biomarkers evaluated: cystatin-C [0.9 (0.2) vs. 0.8 (0.2) mg/L, p = NS; NT-proBNP 46.0 (45.0) vs. 42.0 (34.0) pg/mL, p = NS]. Previous to the TKI treatment, all patients had normal left ventricular ejection fraction (LVEF) [(median 67% (63–69)], without structural abnormalities. During the follow-up, there weren't differences regarding the LVEF, left atrium volume, E/A ratio, deceleration time, septal e', lateral e', E/e' ratio and tricuspid annular plane systolic excursion. With regard to myocardial deformation, all patients presented normal values of longitudinal, circumferential and radial strain in the baseline study, without changes during follow-up [DML -21.3 (6, 1) vs. -21.7 (6.0)%, p = NS; DMC -20.0 (9.3) vs. -22.3 (5.3)%, p = NS; DMR 36.9 (21.3) vs. 39.2 (19.2)%, p = NS]. In addition, there were no differences between the two tyrosine kinase inhibitors used, considering all the aforementioned variables. Conclusion: No clinical, laboratory or echocardiographic evidence of nilotinib and imatinib induced cardiotoxicity was observed, even when myocardial deformation analysis was performed. However, these results should be confirmed in larger studies, ideally multicentre, given the low incidence of chronic myeloid leukaemia.info:eu-repo/semantics/publishedVersio

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

The increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.Rakesh David, Rhys‑Joshua D. Menezes, Jan De Klerk, Ian R. Castleden, Cornelia M. Hooper, Gustavo Carneiro and Matthew Gilliha

Upregulation of the cell-cycle regulator RGC-32 in Epstein-Barr virus-immortalized cells

Epstein-Barr virus (EBV) is implicated in the pathogenesis of multiple human tumours of lymphoid and epithelial origin. The virus infects and immortalizes B cells establishing a persistent latent infection characterized by varying patterns of EBV latent gene expression (latency 0, I, II and III). The CDK1 activator, Response Gene to Complement-32 (RGC-32, C13ORF15), is overexpressed in colon, breast and ovarian cancer tissues and we have detected selective high-level RGC-32 protein expression in EBV-immortalized latency III cells. Significantly, we show that overexpression of RGC-32 in B cells is sufficient to disrupt G2 cell-cycle arrest consistent with activation of CDK1, implicating RGC-32 in the EBV transformation process. Surprisingly, RGC-32 mRNA is expressed at high levels in latency I Burkitt's lymphoma (BL) cells and in some EBV-negative BL cell-lines, although RGC-32 protein expression is not detectable. We show that RGC-32 mRNA expression is elevated in latency I cells due to transcriptional activation by high levels of the differentially expressed RUNX1c transcription factor. We found that proteosomal degradation or blocked cytoplasmic export of the RGC-32 message were not responsible for the lack of RGC-32 protein expression in latency I cells. Significantly, analysis of the ribosomal association of the RGC-32 mRNA in latency I and latency III cells revealed that RGC-32 transcripts were associated with multiple ribosomes in both cell-types implicating post-initiation translational repression mechanisms in the block to RGC-32 protein production in latency I cells. In summary, our results are the first to demonstrate RGC-32 protein upregulation in cells transformed by a human tumour virus and to identify post-initiation translational mechanisms as an expression control point for this key cell-cycle regulator

Interference with Hemozoin Formation Represents an Important Mechanism of Schistosomicidal Action of Antimalarial Quinoline Methanols

Heme is an essential molecule to most living organisms, but once in a free state it exerts toxic effects. Blood-feeding organisms evolved efficient ways to detoxify free heme derived from hemoglobin digestion. A key mechanism present in some hematophagous organisms consists of the crystallization of heme into a pigment named hemozoin. Schistosoma mansoni is one of the etiologic agents of human schistosomiasis, a parasitic disease that affects over 200 million people in tropical and subtropical areas. Hemozoin formation represents the main heme detoxification pathway in S. mansoni. Here, we report that the antimalarial quinoline methanols quinine and quinidine exert schistosomicidal effects notably due to their capacity to interfere with hemozoin formation. When quinine or quinidine were administered intraperitoneally during seven days to S. mansoni-infected mice (75 mg/kg/day), both worm and eggs burden were significantly reduced. Interestingly, hemozoin content in female worms was drastically affected after treatment with either compound. We also found that quinine caused important changes in the cellular organization of worm gastrodermis and increased expression of genes related to musculature, protein synthesis and repair mechanisms. Together, our results indicate that interference with hemozoin formation is a valid chemotherapeutic target for development of new schistosomicidal agents

Medidas de distanciamento social podem ter reduzido as mortes estimadas relacionadas à COVID-19 no Brasil

Social distancing was planned as a preventive measure to control the extensive spread of COVID-19. COVID-19-related deaths in Brazil were analyzed during the period of social distancing measures. Mortality data for COVID-19 was obtained from the Worldometer website. Deaths were estimated up to the 31st day after the occurrence of the 5th COVID-19-related death in Brazil. Social distance was measured using Google's community mobility reports. The Brazilian epidemic curves were interconnected, and mathematical models were evaluated to fit the mortality estimation curves. The optimistic model was defined in the opening period of social distancing and, therefore, in the lower mobility (40-60%). The realistic model was calculated according to relaxed social distance measures (<40%) and the pessimistic model was calculated based on the transmission rate between 2-3. Thus, the equations of the mathematical models provided the outcomes for the date of June 9, 2020, as follows: realistic model with 40,623 deaths, pessimistic model with 64,310 deaths and the optimistic model with a projection of 31,384 deaths. As a result of these analyzes, on May 24, 2020, there were a total of 22,965 deaths related to COVID-19, and those deaths included within the proposed mathematical models were 17,452 for the optimistic model, 22,623 for the realistic model and 32,825 for the pessimistic model. Thus, it is concluded that social distancing measures promoted by the Brazilian public managers contributes to the reduction in approximately ten thousand deaths related to COVID-19 in the current pandemic scenario.O distanciamento social foi planejado como uma medida preventiva para controlar a disseminação extensiva da COVID-19. Foi analisado as mortes relacionadas à COVID-19 no Brasil durante o período de medidas de distanciamento social. Os dados de mortalidade do COVID-19 foram obtidos no site da Worldometer. As mortes foram estimadas até o 31º dia após a ocorrência da 5ª morte relacionada à COVID-19 no Brasil. O distanciamento social foi medido por meio dos relatórios de mobilidade comunitária do Google. As curvas epidêmicas brasileiras foram interligadas e os modelos matemáticos foram avaliados para se ajustarem às curvas de estimativa de mortalidade. O modelo otimista foi definido no período de abertura do distanciamento social e, portanto, na menor mobilidade (40-60%). O modelo realista foi calculado de acordo com medidas de distanciamento social relaxado (<40%) e o modelo pessimista foi calculado com base na taxa de transmissão entre 2-3. Assim, as equações dos modelos matemáticos proveram os desfechos para a data de nove de junho de 2020, conforme seguem: modelo realista com 40.623 mortes, modelo pessimista com 64.310 mortes e o modelo otimista com projeção de 31.384. Como resultados dessas análises, na data de 24 de maio de 2020 obteve um total de 22.965 mortes relacionadas com a COVID-19, sendo que essas mortes inseridas dentro dos modelos matemáticos propostos foram de 17.452 para o modelo otimista, 22.623 para o modelo realista e 32.825 para o modelo pessimista. Assim, conclui-se que a medida de distanciamento social promovida pelos gestores públicos brasileiros contribui para a redução em aproximadamente dez mil mortes relacionadas com a COVID-19 no atual cenário pandêmico

Social distancing measures could have reduced estimated deaths related to COVID-19 in Brazil

Judging  to  make  a  decision  amidst  several  possibilities  that   rise to risks, losses, gains and uncertainties is not a simple matter. In this process, the subject needs to verify the  situation  considering  the  alternatives  that  are  made  up of several elements, among them, the analysis of how much this decision will cost and what benefit it will have in response, and what the consequences will be in the long, medium and short term. In this sense, decision-making is associated with the deliberative and affective process that relates  to  multiple  variables  that  are  interconnected  with  the flow of information assimilated by the agent who is responsible for the judgment and the decision. This process is interdependent on the subject’s structure with external stimuli.  Thus,  the  individual’s  forces  and  social  pressure  are  important  elements  to  be  considered  for  decision  making.  For  the  adolescent,  this  issue  becomes  even  more  important,  as  it  is  a  phase  of  human  development  in which the person exhibits behaviors of risk and during this  stage  of  life  there  is  a  process  of  maturation  of  the  central  nervous  system,  which  are  related  to  decision-making  and  motivational  processes.  Therefore,  decision  making  among  adolescents  is  a  complex  issue  that  in  addition to biological factors are directly related to social and  psychological  elements,  depends  on  a  maturity  in  development,  but  can  be  impaired  if  stressful  situations  are constant stimuli in the lives of adolescents. = O distanciamento social foi planejado como uma medida  preventiva  para  controlar  a  disseminação  extensiva  da   COVID-19.   Nós   analisamos   as   mortes   relacionadas   à  COVID-19  no  Brasil  durante  o  período  de  medidas  de  distanciamento social. Os dados de mortalidade do COVID-19 foram obtidos no site da Worldometer (www.worldometer.info). As  mortes  foram  estimadas  até  o  31º  dia  após  a  5ª  morte.  O  distanciamento  social  foi  medido  por  meio  dos  relatórios  de    mobilidade    comunitária    COVID-19;    Google    (https://www.google.com/covid19/mobility/).   As   curvas   epidêmicas   brasileiras   foram   interligadas   e   os   modelos   matemáticos   foram  avaliados  para  se  ajustarem  às  curvas  de  estimativa  de mortalidade. O modelo otimista foi fundado no período de abertura da distância social e, portanto, na menor mobilidade (40-60%).  O  modelo  realista  foi  calculado  de  acordo  com  medidas de distanciamento social relaxado (<40%) e o modelo pessimista  foi  calculado  com  base  em  R0  entre  2-3.  Sob  essa  situação,  o  modelo  matemático  realista  estimou  40.623  mortes em 9 de junho de 2020, enquanto o modelo pessimista antecipou 64.310 mortes e o modelo otimista projetou 31.384. Até  hoje  (24  de  maio  de  2020),  um  total  de  22.965  foram  relatadas,  enquanto  nosso  modelo  projetou  17.452  para  o  modelo otimista, 22.623 para o modelo realista e 32.825 para o modelo pessimista. Observamos movimento reduzido ao longo deste período. Em resumo, o modelo matemático sugere que a mobilidade reduzida da comunidade diminuiu o total estimado de  mortes  relacionadas  à  COVID-19  no  Brasil.  Enfatizamos  que  mais  procedimentos  metodológicos  serão  necessários  para confirmar esta teoria