Aline Thaís Bruni

Bio: Aline Thaís Bruni is an academic researcher from Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto. The author has contributed to research in topics: Humanities & Philosophy. The author has an hindex of 10, co-authored 32 publications receiving 274 citations. Previous affiliations of Aline Thaís Bruni include Sao Paulo State University & State University of Campinas.

The effects of solubility of silver nanoparticles, accumulation, and toxicity to the aquatic plant Lemna minor

Abstract: The use of silver nanoparticles (AgNPs) in commercial products has increased due to their antibacterial properties and their impacts on the environment must be investigated. This scenario has motivated the conduction of this study, which relates different factors that affect the toxicity of AgNPs to the aquatic plant Lemna minor such as size, accumulation, concentration, and dissolution of AgNPs. To this end, synthesized AgNPs measuring 30, 85, and 110 nm were added into the culture medium to observe toxicity for 30 days. The mapping by SEM showed that the smallest AgNPs can translocate from roots to leaves due to its mobility and internalization. As predicted by the Ostwald equation, the solubility for 30-nm AgNPs increased almost 3 times at the end of 30 days, while for 85 and 110 nm size nanoparticles, after 7 days, the solubility decreased due to "Ostwald ripening" process. Plant mortality was assessed and, after 1 month, the size of 30 nm was the most toxic with negative growth in all studied concentrations, with 60% mortality in the worst case. The concentration of 50 μg mL-1 was toxic in all sizes with negative growth in the period. Therefore, the investigation of AgNPs' toxicity needs to consider a different factor to better understand their effects on aquatic plants and the environment.

Inibidores da HIV-integrase: potencial abordagem farmacológica para tratamento da AIDS

Abstract: AIDS has the HIV as its etiological agent. Researches has been done to find new pharmacological agents to be used in therapy, because of problems of resistance and side effects. The HIV-integrase inhibitors are some of those new agents that are being studied. This updating focusses on the fundamental information about HIV and HIV-integrase and the main methods being used to develop these new drugs, with examples for each case.

Omeprazole and analogue compounds: a QSAR study of activity against Helicobacter pylori using theoretical descriptors

Abstract: Omeprazole and analogues were studied with respect to their activity as inhibitors of urease Helicobacter pylori. Conformational analysis was performed according to the method proposed by Bruni et al. Theoretical descriptors were calculated by an ab initio method (6-31G** basis set). Since several minimum energy structures were obtained for each compound, and the calculated descriptors proved to be sensitive to the structural conformation, different criteria were proposed for conformation selection. Three data sets were generated wherein conformations were grouped according to minimum heat of formation, minimum electronic energy and structural similarity. For these three sets, experimental per cent of control was used to develop quantitative structure-activity models by PLS. Their cross-validation and correlation coefficients were very good (Q 2 = 0.97 and R2 = 0.99 on average) and the standard error of validation was much smaller in comparison with results from the literature.

Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis

Abstract: The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Silibinin prevents amyloid β peptide-induced memory impairment and oxidative stress in mice

Abstract: Background and purpose: Accumulated evidence suggests that oxidative stress is involved in amyloid β (Aβ)-induced cognitive dysfunction. Silibinin (silybin), a flavonoid derived from the herb milk thistle (Silybum marianum), has been shown to have antioxidative properties; however, it remains unclear whether silibinin improves Aβ-induced neurotoxicity. In the present study, we examined the effect of silibinin on the memory impairment and accumulation of oxidative stress induced by Aβ25–35 in mice.

Machine learning techniques and drug design.

Abstract: The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

A review of biogas production from palm oil mill effluents using different configurations of bioreactors

Abstract: Palm oil mill effluent (POME) is generated from the sterilization, condensation and hydrocycloning of palm oil in mills. If the effluent is discharged into the aquatic and terrestrial ecosystem without treatment, it could lead to high biological oxygen demand (BOD), chemical oxygen demand (COD) and acidic pH of the receiving waters. Biogas consisting mostly of methane, carbon dioxide, and to a lesser hydrogen has been produced through anaerobic treatment of this toxic effluent. The process of biogas production involves microbial synthesis involving hydrolysis, acidogenesis, acetogenesis and methanogenesis. Biogas is formed during anaerobic degradation of POME by indigenous microbial communities. This review updates the current state of art of biogas production through anaerobic digestion of POME using different configurations of reactors such as fluidized bed reactor, anaerobic filtration, up-flow anaerobic sludge blanket (UASB) reactor, anaerobic contact digestion, up-flow anaerobic sludge fixed-film (UASFF) reactor, modified anaerobic baffled bioreactor (MABB), anaerobic baffled bioreactor (ABR), continuous stirred tank reactor (CSTR), expanded granular sludge bed (EGSB) reactor, Ultrasonicated membrane anaerobic system (UMAS), Ultrasonic-assisted Membrane Anaerobic System (UAMAS), membrane anaerobic system (MAS)and upflow anaerobic sludge blanket reactor (UASBR). The factors that influences biogas yield during treatment include pH, temperature (environmental factors), organic loading rate (OLR), hydraulic retention time (HRT), mixing rate, pressure, equilibrium, nutrient and microbial activities (Internal factors). Based on this study, UAMAS is the best configuration for methane production from POME during anaerobic treatment. Biogas from POME could contribute to energy sources of oil palm producing nations, while preventing the attendant environmental impacts associated with its disposal.

Zebrafish Models in NeuroPsychopharmacology and CNS Drug Discovery

Abstract: Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets.