Modulation of host cell signaling pathways as a therapeutic approach in periodontal disease

Recently, new treatment approaches have been developed to target the host component of periodontal disease. This review aims at providing updated information on host-modulating therapies, focusing on treatment strategies for inhibiting signal transduction pathways involved in inflammation. Pharmacological inhibitors of MAPK, NFκB and JAK/STAT pathways are being developed to manage rheumatoid arthritis, periodontal disease and other inflammatory diseases. Through these agents, inflammatory mediators can be inhibited at cell signaling level, interfering on transcription factors activation and inflammatory gene expression. Although these drugs offer great potential to modulate host response, their main limitations are lack of specificity and developments of side effects. After overcoming these limitations, adjunctive host modulating drugs will provide new therapeutic strategies for periodontal treatment.

Matrix-vector multiplication and triangular linear solver using GPGPU for symmetric positive definite matrices derived from elliptic equations

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.

Femtosecond laser processing of glassy and polymeric matrices containing metals and semiconductor nanostructures

Tailoring properties of materials by femtosecond laser processing has been proposed in the last decade as a powerful approach for technological applications, ranging from optics to biology. Although most of the research output in this field is related to femtosecond laser processing of single either organic or inorganic materials, more recently a similar approach has been proposed to develop advanced hybrid nanomaterials. Here, we report results on the use of femtosecond lasers to process hybrid nanomaterials, composed of polymeric and glassy matrices containing metal or semiconductor nanostructures. We present results on the use of femtosecond pulses to induce Cu and Ag nanoparticles in the bulk of borate and borosilicate glasses, which can be applied for a new generation of waveguides. We also report on 3D polymeric structures, fabricated by two-photon polymerization, containing Au and ZnO nanostructures, with intense two-photon fluorescent properties. The approach based on femtosecond laser processing to fabricate hybrid materials containing metal or semiconductor nanostructures is promising to be exploited for optical sensors and photonics devices.