Tânia F.G.G. Cova

Lysine-based surfactants as chemical permeation enhancers for dermal delivery of local anesthetics

The aim of this study is to investigate the efficacy of new, biocompatible, lysine-based surfactants as chemical permeation enhancers for two different local anesthetics, tetracaine and ropivacaine hydrochloride, topically administered. Results show that this class of surfactants strongly influences permeation, especially in the case of the hydrophilic and ionized drug, ropivacaine hydrochloride, that is not easily administered through the stratum corneum. It is also seen that the selected permeation enhancers do not have significant deleterious effects on the skin structure. A cytotoxicity profile for each compound was established from cytotoxicity studies. Molecular dynamics simulation results provided a rationale for the experimental observations, introducing a mechanistic view of the action of the surfactants molecules upon lipid membranes.

A new perspective on correlated polyelectrolyte adsorption: Positioning, conformation, and patterns

This work focuses on multiple chain deposition, using a coarse-grained model. The phenomenon is assessed from a novel perspective which emphasizes the conformation and relative arrangement of the deposited chains. Variations in chain number and length are considered, and the surface charge in the different systems ranges from partially neutralized to reversed by backbone deposition. New tools are proposed for the analysis of these systems, in which focus is given to configuration-wise approaches that allow the interpretation of correlated multi-chain behavior. It is seen that adsorption occurs, with a minimal effect upon the bulk conformation, even when overcharging occurs. Also, chain ends create a lower electrostatic potential, which makes them both the least adsorbed region of the backbone, and the prevalent site of closer proximity with other chains. Additionally, adsorption into the most favorable region of the surface overrides, to a large degree, interchain repulsion.

Novel serine-based gemini surfactants as chemical permeation enhancers of local anesthetics: A comprehensive study on structure-activity relationships, molecular dynamics and dermal delivery.

This work aims at studying the efficacy of a series of novel biocompatible, serine-based surfactants as chemical permeation enhancers for two different local anesthetics, tetracaine and ropivacaine, combining an experimental and computational approach. The surfactants consist of gemini molecules structurally related, but with variations in headgroup charge (nonionic vs. cationic) and in the hydrocarbon chain lengths (main and spacer chains). In vitro permeation and molecular dynamics studies combined with cytotoxicity profiles were performed to investigate the permeation of both drugs, probe skin integrity, and rationalize the interactions at molecular level. Results show that these enhancers do not have significant deleterious effects on the skin structure and do not cause relevant changes on cell viability. Permeation across the skin is clearly improved using some of the selected serine-based gemini surfactants, namely the cationic ones with long alkyl chains and shorter spacer. This is noteworthy in the case of ropivacaine hydrochloride, which is not easily administered through the stratum corneum. Molecular dynamics results provide a mechanistic view of the surfactant action on lipid membranes that essentially corroborate the experimental observations. Overall, this study suggests the viability of these serine-based surfactants as suitable and promising delivery agents in pharmaceutical formulations.

Is standard multivariate analysis sufficient in clinical and epidemiological studies

Clinical tests and epidemiological studies often produce large amounts of data, being multivariate in nature. The respective analysis is, in most cases, of importance comparable to the clinical and sampling tasks. Simple, easily interpretable techniques from chemometrics provide most of the ingredients to carry out this analysis. We have selected available data from different sources pertaining to cancer diagnosis and incidence: (1) cytological diagnosis of breast cancer, (2) classification of breast tissues through parameters obtained from impedance spectra and (3) distribution of new cancer cases in the United States. Hierarchical cluster analysis (HCA) is needed especially in cases where there is no a priori identification of classes, suggesting a structure of the data based on clusters. These clusters or the classes, are then further detailed and rationalized by principal component analysis (PCA). Partial least squares (PLS) and linear discriminant analysis (LDA) provide further insight into the systems. An additional step for understanding the data set is the removal of less characteristic data (NR) using a density-based approach, so as to make it more clearly defined. Results clearly reveal that breast cytology diagnosis relies on variables conveying mostly the same type of information, being thus interchangeable in nature. In the study on tissue characterization by electrical measurements, the distribution of the different types of tissues can be easily constructed. Finally, the distribution of new cancer cases possesses clear, easily unravelled, geographical patterns.

Iberian universities: a characterisation from ESI rankings

The access to bibliographic and citation databases allows to evaluate scientific performance, and provides useful means of general characterisation. In this paper we investigate the clustering of Iberian universities, resulting from the similarity in the number and specific nature of the scientific disciplines given by the Essential Science Indicators database. A further refining of the analysis, as provided by PCA, clearly reveals the relationship between the universities and the scientific disciplines in the main groups. Similarity between universities is not dictated only by the number of areas in the ranking, but also stems from the nature of the ranked scientific areas and the specific combination in each university.

Unsupervised characterization of research institutions with task-force estimation

This work aims at establishing the task-force involved in scientific production at the institutional or national level, globally or per area or sub-area of knowledge. In the proposed system, the estimated task-force is further divided into core (permanent members of the institution(s)) and collaborators (more mobile members), and allows normalization of scientific production.

Computational modeling in glioblastoma: from the prediction of blood–brain barrier permeability to the simulation of tumor behavior

The integrated in silico-in vitro-in vivo approaches have fostered the development of new treatment strategies for glioblastoma patients and improved diagnosis, establishing the bridge between biochemical research and clinical practice. These approaches have provided new insights on the identification of bioactive compounds and on the complex mechanisms underlying the interactions among glioblastoma cells, and the tumor microenvironment. This review focuses on the key advances pertaining to computational modeling in glioblastoma, including predictive data on drug permeability across the blood-brain barrier, tumor growth and treatment responses. Structure- and ligand-based methods have been widely adopted, enabling the study of dynamic and evolutionary aspects of glioblastoma. Their potential applications as predictive tools and the advantages over other well-known methodologies are outlined. Challenges regarding in silico approaches for predicting tumor properties are also discussed.

Improving discrimination in the grading of rat mammary tumors using two-dimensional mapping of histopathological observations

This work aims at characterizing rat mammary tumors induced by 7,12-dimethylbenz(a)anthracene (DMBA) and the respective malignancy potential, commonly graded with histopathology features grouped by intensity levels. Tumors were described over fourteen multiple ranged microscopic parameters and a comprehensive characterization of the histological patterns and their relation with tumor grade was carried out by principal component analysis (PCA). The number of histological patterns present on a tumor tends to correlate with malignant features. High grade tumors are characterized by the presence of several structural patterns, with cribriform prevalence and necrosis. The cribriform pattern correlates with grading, i.e., tumors having a higher predominance of the cribriform pattern are likely to be more malignant. The findings may represent a benchmark for similar characterization studies in other models.

Modeling of ultra-small lipid nanoparticle surface charge for targeting glioblastoma

&NA; Surface modification of ultra‐small nanostructured lipid carriers (usNLC) via introduction of a positive charge is hypothesized to prompt site‐specific drug delivery for glioblastoma multiforme (GBM) treatment. A more effective interaction with negatively charged lipid bilayers, including the blood‐brain barrier (BBB), will facilitate the nanoparticle access to the brain. For this purpose, usNLC with a particle size of 43.82 ± 0.03 nm and a polydispersity index of 0.224 were developed following a Quality by Design approach. Monomeric and gemini surfactants, either with conventional headgroups or serine‐based ones, were tested for the surface modification, and the respective safety and efficacy to target GBM evaluated. A comprehensive in silico‐in vitro approach is also provided based on molecular dynamics simulations and cytotoxicity studies. Overall, monomeric serine‐derived surfactants displayed the best performance, considering altogether particle size, zeta potential, cytotoxic profile and cell uptake. Although conventional surfactants were able to produce usNLC with suitable physicochemical properties and cell uptake, their use is discouraged due to their high cytotoxicity. This study suggests that monomeric serine‐derived surfactants are promising agents for developing nanosystems aiming at brain drug delivery. Graphical abstract Figure. No caption available.

Reconstructing the historical synthesis of mauveine from Perkin and Caro: procedure and details

Mauveine, an iconic dye, first synthesised in 1856 still has secrets to unveil. If nowadays one wanted to prepare the original Perkin’s mauveine, what would be the procedure? It will be described in this work and lies on the use of a 1:2:1 (mole) ratio of aniline, p-toluidine and o-toluidine. This was found from a comparison of a series of products synthesized from different proportions of these starting materials, with a set of historical samples of mauveine and further analysed with two unsupervised chemometrics methods.