Silica nanoparticles as dexamethasone delivery systems able to induce the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells

Bioactive glasses, especially silica-based materials, are reported to pres- ent osteoconductive and osteoinductive properties, fundamental char- acteristics in bone regeneration [1,2]. Additionally, dexamethasone (Dex) is one of the bioactive agents able to induce the osteogenic differ- entiation of mesenchymal stem cells by increasing the alkaline phos- phatase activity, and the expression levels of Osteocalcin and Bone Sialoprotein [3]. Herein, we synthesised silica (SiO2) nanoparticles (that present inherent bioactivity and ability to act as a sustained drug delivery system), and coated their surface using poly-L-lysine (PLL) and hyaluronic acid (HA) using the layer-by-layer processing technique. Further on, we studied the influence of these new SiO2-polyelectrolyte coated nanoparticles as Dex sustained delivery systems. The SiO2 nanoparticles were loaded with Dex (SiO2-Dex) and coated with PLL and HA (SiO2-Dex-PLL-HA). Their Dex release profile was evaluated and a more sustained release was obtained with the SiO2-Dex-PLL-HA. All the particles were cultured with human bone marrow-derived mes- enchymal stem cells (hBMSCs) under osteogenic differentiation culture conditions. hBMSCs adhered, proliferated and differentiated towards the osteogenic lineage in the presence of SiO2 (DLS 174nm), SiO2-Dex (DLS 175nm) and SiO2-Dex-PLL-HA (DLS 679nm). The presence of these materials induced the overexpression of osteogenic transcripts, namely of Osteocalcin, Bone Sialoprotein and Runx2. Scanning Elec- tron Microscopy/Electron Dispersive Spectroscopy analysis demon- strated that hBMSCs synthesised calcium phosphates when cultured with SiO2-Dex and SiO2-Dex-PLL-HA nanoparticles. These results indi- cate the potential use of these SiO2-polyelectrolytes coated nanoparti- cles as dexamethasone delivery systems capable of promoting osteogenic differentiation of hBMSCs.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Clinical characteristics of women diagnosed with carcinoma who tested positive for cervical and anal high-risk human papillomavirus DNA and E6 RNA

High-risk human papillomavirus (hrHPV) is an essential cause of cervical carcinoma and is also strongly related to anal cancer development. The hrHPV E6 oncoprotein plays a major role in carcinogenesis. We aimed to evaluate the frequency of hrHPV DNA and E6 oncoprotein in the anuses of women with cervical carcinoma. We analyzed 117 women with cervical cancer and 103 controls for hrHPV and the E6 oncogene. Positive test results for a cervical carcinoma included 66.7 % with hrHPV-16 and 7.7 % with hrHPV-18. One case tested positive for both HPV variants (0.9 %). The samples from the anal canal were positive for HPV-16 in 59.8 % of the cases. Simultaneous presence of HPV in the cervix and anal canal was found in 53.8 % of the cases. Regarding expression of E6 RNA, positivity for HPV-16 in the anal canal was found in 21.2 % of the cases, positivity for HPV-16 in the cervix was found in 75.0 %, and positivity for HPV-18 in the cervix was found in 1.9 %. E6 expression in both the cervix and anal canal was found in 19.2 % of the cases. In the controls, 1 % tested positive for HPV-16 and 0 % for HPV-18. Anal samples from the controls showed a hrHPV frequency of 4.9 % (only HPV16). The presence of hrHPV in the anal canal of women with cervical cancer was detected at a high frequency. We also detected E6 RNA expression in the anal canal of women with cervical cancer, suggesting that these women are at risk for anal hrHPV infection.

Regulation of human mesenchymal stem cell osteogenesis by specific surface density of fibronectin: a gradient study

 The success of synthetic bone implants requires good interface between the material and the host tissue. To study the biological relevance of fi bronectin (FN) density on the osteogenic commitment of human bone marrow mesenchymal stem cells (hBMMSCs), human FN was adsorbed in a linear density gradient on the surface of PCL. The evolution of the osteogenic markers alkaline phosphatase and collagen 1 alpha 1 was monitored by immunohistochemistry, and the cytoskeletal organization and the cell-derived FN were assessed. The functional analysis of the gradient revealed that the lower FN-density elicited stronger osteogenic expression and higher cytoskeleton spreading, hallmarks of the stem cell commitment to the osteoblastic lineage. The identifi cation of the optimal FN density regime for the osteogenic commitment of hBM-MSCs presents a simple and versatile strategy to signifi cantly enhance the surface properties of polycaprolactone as a paradigm for other synthetic polymers intended for bone-related applications.

Osteogenic differentiation of human mesenchymal stem cells in the absence of osteogenic supplements: a surface-roughness gradient study

The use of biomaterials to direct osteogenic differentiation of human mesenchymal stem cells (hMSCs) in the absence of osteogenic supplements is thought to be part of the next generation of orthopedic implants. We previously engineered surface-roughness gradients of average roughness (Ra) varying from the sub-micron to the micrometer range ( 0.5–4.7 lm), and mean distance between peaks (RSm) gradually varying from 214 lm to 33 lm. Here we have screened the ability of such surface-gradients of polycaprolactone to influence the expression of alkaline phosphatase (ALP), collagen type 1 (COL1) and mineralization by hMSCs cultured in dexamethasone (Dex)-deprived osteogenic induction medium (OIM) and in basal growth medium (BGM). Ra 1.53 lm/RSm 79 lm in Dex-deprived OI medium, and Ra 0.93 lm/RSm 135 lm in BGM consistently showed higher effectiveness at supporting the expression of the osteogenic markers ALP, COL1 and mineralization, compared to the tissue culture polystyrene (TCP) control in complete OIM. The superior effectiveness of specific surface-roughness revealed that this strategy may be used as a compelling alternative to soluble osteogenic inducers in orthopedic applications featuring the clinically relevant biodegradable polymer polycaprolactone.

MMP-9/RECK imbalance: a mechanism associated with high-grade cervical lesions and genital infection by Human Papillomavirus (HPV) and Chlamydia trachomatis

"Manuscript"

Synthesis, antiangiogenesis evaluation and molecular docking studies of 1-Aryl-3-[(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas: Discovery of a new substitution pattern for type II VEGFR-2 Tyr kinase inhibitors

The synthesis and biological evaluation of novel 1-aryl-3-[2-, 3- or 4-(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas 3, 4 and 5 as VEGFR-2 tyrosine kinase inhibitors, are reported. The 1-aryl-3-[3-(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas 4a-4h, with the arylurea in the meta position to the thioether, showed the lowest IC50 values in enzymatic assays (10-206 nM), the most potent compounds 4d-4h (IC50 10-28 nM) bearing hydrophobic groups (Me, F, CF3 and Cl) in the terminal phenyl ring. A convincing rationalization was achieved for the highest potent compounds 4 as type II VEGFR-2 inhibitors, based on the simultaneous presence of: (1) the thioether linker and (2) the arylurea moiety in the meta position. For compounds 4, significant inhibition of Human Umbilical Vein Endothelial Cells (HUVECs) proliferation (BrdU assay), migration (wound-healing assay) and tube formation were observed at low concentrations. These compounds have also shown to increase apoptosis using the TUNEL assay. Immunostaining for total and phosphorylated (active) VEGFR-2 was performed by Western blotting. The phosphorylation of the receptor was significantly inhibited at 1.0 and 2.5 microM for the most promising compounds. Altogether, these findings point to an antiangiogenic effect in HUVECs.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores