A Comprehensive In Silico Analysis of the Functional and Structural Impact of SNPs in the IGF1R Gene

Insulin-like growth factor 1 receptor (IGF1R) acts as a critical mediator of cell proliferation and survival. Many single nucleotide polymorphisms (SNPs) found in the IGF1R gene have been associated with various diseases, including both breast and prostate cancer. The genetics of these diseases could be better understood by knowing the functions of these SNPs. In this study, we performed a comprehensive analysis of the functional and structural impact of all known SNPs in this gene using publicly available computational prediction tools. Out of a total of 2412 SNPs in IGF1R retrieved from dbSNP, we found 32 nsSNPs, 58 sSNPs, 83 mRNA 3′ UTR SNPs, and 2225 intronic SNPs. Among the nsSNPs, a total of six missense nsSNPs were found to be damaging by both a sequence homology-based tool (SIFT) and a structural homology-based method (PolyPhen), and one nonsense nsSNP was found. Further, we modeled mutant proteins and compared the total energy values with the native IGF1R protein, and showed that a mutation from arginine to cysteine at position 1216 (rs61740868) on the surface of the protein caused the greatest impact on stability. Also, the FASTSNP tool suggested that 31 sSNPs and 3 intronic SNPs might affect splicing regulation. Based on our investigation, we report potential candidate SNPs for future studies on IGF1R mutations

CYTOTOXICITY, ANTI-POLIOVIRUS ACTIVITY AND IN SILICO BIOLOGICAL EVALUATION OF CONSTITUENTS FROM MAYTENUS GONOCLADA (CELASTRACEAE)

Objective: The in silico free access web tools PASS online and ChemMapper were used to predict potential biological activities of compounds 1 to 8 isolated from Maytenus gonoclada (Celastraceae). The constituents 4'-O-methylepigalocatequin (6), tingenone (7) and proanthocyanidin A (8), and ethanolic extracts were subjected to in vitro cytotoxicity using VERO cells and anti-Poliovirus assays. Methods: QSAR and molecular superposition, correlating the average number of pharmacophores were used in the prediction studies. Cellular line VERO ATCC CCL-81 was used to determine anti-Poliovirus effect, observed by colorimetric (MTT) method. The annexing V/propidium iodide assay was used to determine the occurrence of apoptosis in the cytotoxicity assays. Results: The experimental results found for constituents 6-8 were in accordance with observed data obtained through PASS online and ChemMapper simulation. Conclusion: Compound 7 showed higher cytotoxic and apoptosis induction properties, and 6 and 8 presented anti-Poliovirus activity

Screening Methodology for the Efficient Pairing of Ionic Liquids and Carbonaceous Electrodes Applied to Electric Energy Storage

A model is presented that correlates the measured electric capacitance with the energy that comprises the desolvation, dissociation and adsorption energy of an ionic liquid into carbonaceous electrode (represented by single-wall carbon nanotubes). An original methodology is presented that allows for the calculation of the adsorption energy of ions in a host system that does not necessarily compensate the total charge of the adsorbed ions, leaving an overall net charge. To obtain overall negative (favorable) energies, adsorption energies need to overcome the energy cost for desolvation of the ion pair and its dissociation into individual ions. Smaller ions, such as BF4 −, generally show larger dissociation energies than anions such as PF6 − or TFSI−. Adsorption energies gradually increase with decreasing pore size of the CNT and show a maximum when the pore size is slightly greater than the dimensions of the adsorbed ion and the attractive van der Waals forces dominate the interaction. At smaller pore diameters, the adsorption energy sharply declines and becomes repulsive as a result of geometry deformations of the ion. Only for those diameters where the adsorption reaches maximum values is the adsorption energy sufficiently negative to balance the positive dissociation and desolvation energies. We present for each ion (and ionic liquid) what the most adequate electrode pore size should be for maximum capacitance

Análise da influência dos processos de plasticidade e fratura no comportamento mecânico de microestruturas de Compósitos de Matriz Metálica

RESUMO O presente trabalho trata da simulação numérica do comportamento mecânico de microestruturas de Compósitos com Matriz Metálica (CMMs) utilizando uma proposta de modelo de homogeneização computacional baseada numa abordagem multi-escala. Na microestrutura do compósito, as inclusões são consideradas elásticas e o comportamento da matriz é governado pelo modelo de von Mises com endurecimento isotrópico. Um modelo de fratura coesiva é desenvolvido para simular a fase de descolamento da interface matriz/inclusão. Todo o estudo é baseado no conceito de Elemento de Volume Representativo (EVR), no qual podem ser empregados modelos constitutivos que levam em conta os fenômenos dissipativos de fissuração e plasticidade. Uma série de EVRs com diferentes composições de inclusões elásticas e submetidos a diferentes condições de restrição cinemática foram analisados. Também observou-se a sensibilidade paramétrica do modelo de fratura coesiva e a importância de se considerar a fase de descolamento matriz/inclusão no processo de ruptura da microestrutura. De modo geral, os resultados encontrados contribuem para a discussão acerca do emprego de modelos simples, em termos de formulação e identificação paramétrica, na modelagem da microestrutura de materiais heterogêneos, refletindo assim na acurácia de resultados qualitativos quanto ao seu comportamento macroscópico

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.Publisher PDFPeer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio