University of Madeira

About: University of Madeira is a education organization based out in Funchal, Portugal. It is known for research contribution in the topics: Population & Dendrimer. The organization has 1014 authors who have published 2759 publications receiving 59457 citations.

Insight into the role of N,N-dimethylaminoethyl methacrylate (DMAEMA) conjugation onto poly(ethylenimine): cell viability and gene transfection studies

Abstract: In the present study, the effect of N,N-dimethylaminoethyl methacrylate (DMAEMA) conjugation onto branched poly(ethylenimine) (PEI) with different grafting degree was examined for gene delivery applications. The DMAEMA-grafted-PEI conjugates were characterized and complexed with plasmid DNA (pDNA) at various concentrations, and the physicochemical properties, cell viability, and in vitro transfection efficiency of the complexes were evaluated in HEK 293T cells. Computational techniques were used to analyze the interaction energies and possible binding modes between DNA and conjugates at different grafting degrees. The cytotoxicity analysis and in vitro transfection efficiency of the conjugate/pDNA complexes exhibited a beneficial effect of DMAEMA conjugation when compared to PEI alone. The computational results revealed that the DNA/vector interaction energy decreases with increasing grafting degree, which can be associated to an enhanced release of the pDNA from the carrier once inside cells. The results indicate the significance of DMAEMA conjugation onto PEI as a promising approach for gene delivery applications.

Optimization of sleep apnea detection using SpO2 and ANN

Abstract: Repetitive respiratory disturbance during sleep is called Sleep Apnea Hypopnea Syndrome and causes various diseases. Different features and classifiers have been used by different researchers to detect sleep apnea. This study is undertaken to identify the better performing blood oxygen saturation features subset using an Artificial Neural Network classifier for sleep Apnea detection. A database of 8 subjects with one-minute annotation is used to test the proposed system. The optimized system has seven features chosen from a total set of sixty-one features presenting a high accuracy rate using a genetic algorithm. Artificial Neural Network was able to achieve 97.7 percentage of accuracy with only seven features chosen by the Genetic algorithm.

Investigation of urinary volatomic alterations in head and neck cancer: a non-invasive approach towards diagnosis and prognosis

Abstract: Head and neck cancer (HNC), like many other forms of cancer, is usually detected in advanced stages, causing poor survival outcomes. Lack of specific and sensitive screening markers for early detection of HNC has worsened the scenario for the patients as well as the clinicians. Therefore, identification of efficient, noninvasive and affordable screening marker/methodology with high specificity and sensitivity is imminent need of situation. This study aims to identify and characterize urinary volatomic alterations specific to HNC. Volatomic analysis of urine samples collected from HNC patients (n = 29) and healthy controls (n = 31) was performed using headspace solid phase microextraction coupled to gas chromatography mass spectrometry (GC–MS). Both univariate and multivariate statistical approaches were used to investigate HNC specific volatomic alterations. Statistical analysis revealed a total of 28 metabolites with highest contribution towards discrimination of HNC patients from healthy controls (VIP >1, p < 0.05, Log2 FC ≥0.58/≤−0.57). The discrimination efficiency and accuracy of urinary VOCs was ascertained by ROC curve analysis that allowed the identification of four metabolites viz. 2,6-dimethyl-7-octen-2-ol, 1-butanol, p-xylene and 4-methyl-2-heptanone with highest sensitivity and specificity to discriminate HNC patients from healthy controls. Further, the metabolic pathway analysis identified several dysregulated pathways in HNC patients and their detailed investigations could unravel novel mechanistic insights into the disease pathophysiology. Overall, this study provides valuable fingerprint of the volatile profile of HNC patients, which in turn, might help in improving the current understanding of this form of cancer and lead to the development of non-invasive approaches for HNC diagnosis.