Fiber average size and distribution dependence on the electrospinning parameters of poly(vinylidene fluoride trifluoroethylene)) membranes for biomedical applications

Poly(vinylidene fluoride-trifluoethylene) electrospun membranes were obtained from a blend of dimethylformamide (DMF) and methylethylketone (MEK) solvents. The inclusion of the MEK to the solvent system promotes a faster solvent evaporation allowing complete polymer crystallization during the jet travelling between the tip and the grounded collector. Several processing parameters were systematically changed to study their influence on fiber dimensions. Applied voltage and inner needle diameter do not have large influence on the electrospun fiber average diameter but in the fiber diameter distribution. On the other hand, the increase of the distance between the needle tip to collector results in fibers with larger average diameter. Independently on the processing conditions, all mats are produced in the electroactive phase of the polymer. Further, MC-3T3-E1cell adhesion was not inhibited by the fiber mats preparation, indicating their potential use for biomedical applications.

Influence of electrospinning parameters on Poly(hydroxybutyrate) electrospun membranes fiber size and distribution

Poly(hydroxybutyrate) (PHB) obtained from sugar cane was dissolved in a blend of chloroform and dimethylformamide (DMF) and electrospun at 40 ºC. By adding DMF to the solution, the electrospinning process for the PHB polymer becomes more stable, allowing complete polymer crystallization during the jet travelling between the tip and the grounded collector. The influence of processing parameters on fiber size and distribution was systematically studied. It was observed that an increase of tip inner diameter promotes a decrease of the fiber average size and a broader distribution. On the other hand, an increase of the electric field and flow rate produces an increase of fiber diameter until a maximum of ~2.0 m, but for electric fields higher than 1.5 kV.cm-1, a decrease of the fiber diameter was observed. Polymer crystalline phase seems to be independent of the processing conditions and a crystallinity degree of 53 % was found. Moreover, thermal degradation of the as-spun membrane occurs in single step degradation with activation energy of 91 kJ/mol. Furthermore, MC-3T3-E1 cell adhesion was not inhibited by the fiber mats preparation, indicating their potential use for biomedical applications.

Pectus Excavatum postsurgical outcome based on preoperative soft body dynamics simulation

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar under the sternum and over the ribs. Although this technique has been shown to be safe and reliable, not all patients have achieved adequate cosmetic outcome. This often leads to psychological problems and social stress, before and after the surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses the patient pre-surgical thoracic CT scan and anatomical-surgical references to perform a 3D segmentation of the left ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the ribs and the sternum, trough b-spline interpolation; b) a volumetric mass spring model that connects two layers - inner skin layer based on the outer pleura contour and the outer surface skin; and c) displacement of the sternum according to the prosthetic bar position. A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical skin surface acquired with Polhemus FastSCAN system

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention