3D Printed Silicone Meniscus Implants: Influence of the 3D Printing Process on Properties of Silicone Implants

Osteoarthritis of the knee with meniscal pathologies is a severe meniscal pathology suffered by the aging population worldwide. However, conventional meniscal substitutes are not 3D-printable and lack the customizability of 3D printed implants and are not mechanically robust enough for human implantation. Similarly, 3D printed hydrogel scaffolds suffer from drawbacks of being mechanically weak and as a result patients are unable to execute immediate post-surgical weight-bearing ambulation and rehabilitation. To solve this problem, we have developed a 3D silicone meniscus implant which is (1) cytocompatible, (2) resistant to cyclic loading and mechanically similar to native meniscus, and (3) directly 3D printable. The main focus of this study is to determine whether the purity, composition, structure, dimensions and mechanical properties of silicone implants are affected by the use of a custom-made in-house 3D-printer. We have used the phosphate buffer saline (PBS) absorption test, Fourier transform infrared (FTIR) spectroscopy, surface profilometry, thermo-gravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) to effectively assess and compare material properties between molded and 3D printed silicone samples

3D Printed Silicone Meniscus Implants: Influence of the 3D Printing Process on Properties of Silicone Implants

Osteoarthritis of the knee with meniscal pathologies is a severe meniscal pathology suffered by the aging population worldwide. However, conventional meniscal substitutes are not 3D-printable and lack the customizability of 3D printed implants and are not mechanically robust enough for human implantation. Similarly, 3D printed hydrogel scaffolds suffer from drawbacks of being mechanically weak and as a result patients are unable to execute immediate post-surgical weight-bearing ambulation and rehabilitation. To solve this problem, we have developed a 3D silicone meniscus implant which is (1) cytocompatible, (2) resistant to cyclic loading and mechanically similar to native meniscus, and (3) directly 3D printable. The main focus of this study is to determine whether the purity, composition, structure, dimensions and mechanical properties of silicone implants are affected by the use of a custom-made in-house 3D-printer. We have used the phosphate buffer saline (PBS) absorption test, Fourier transform infrared (FTIR) spectroscopy, surface profilometry, thermo-gravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) to effectively assess and compare material properties between molded and 3D printed silicone samples

Influence of the initial chemical conditions on the rational design of silica particles

The influence of the water content in the initial composition on the size of silica particles produced using the Stöber process is well known. We have shown that there are three morphological regimes defined by compositional boundaries. At low water levels (below stoichiometric ratio of water:tetraethoxysilane), very high surface area and aggregated structures are formed; at high water content (>40 wt%) similar structures are also seen. Between these two boundary conditions, discrete particles are formed whose size are dictated by the water content. Within the compositional regime that enables the classical Stöber silica, the structural evolution shows a more rapid attainment of final particle size than the rate of formation of silica supporting the monomer addition hypothesis. The clearer understanding of the role of the initial composition on the output of this synthesis method will be of considerable use for the establishment of reliable reproducible silica production for future industrial adoption

Facile Fabrication of Ultrafine Hollow Silica and Magnetic Hollow Silica Nanoparticles by a Dual-Templating Approach

The development of synthetic process for hollow silica materials is an issue of considerable topical interest. While a number of chemical routes are available and are extensively used, the diameter of hollow silica often large than 50 nm. Here, we report on a facial route to synthesis ultrafine hollow silica nanoparticles (the diameter of ca. 24 nm) with high surface area by using cetyltrimethylammmonium bromide (CTAB) and sodium bis(2-ethylhexyl) sulfosuccinate (AOT) as co-templates and subsequent annealing treatment. When the hollow magnetite nanoparticles were introduced into the reaction, the ultrafine magnetic hollow silica nanoparticles with the diameter of ca. 32 nm were obtained correspondingly. Transmission electron microscopy studies confirm that the nanoparticles are composed of amorphous silica and that the majority of them are hollow

Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

Type I Interferon Induction Is Detrimental during Infection with the Whipple's Disease Bacterium, Tropheryma whipplei

Macrophages are the first line of defense against pathogens. Upon infection macrophages usually produce high levels of proinflammatory mediators. However, macrophages can undergo an alternate polarization leading to a permissive state. In assessing global macrophage responses to the bacterial agent of Whipple's disease, Tropheryma whipplei, we found that T. whipplei induced M2 macrophage polarization which was compatible with bacterial replication. Surprisingly, this M2 polarization of infected macrophages was associated with apoptosis induction and a functional type I interferon (IFN) response, through IRF3 activation and STAT1 phosphorylation. Using macrophages from mice deficient for the type I IFN receptor, we found that this type I IFN response was required for T. whipplei-induced macrophage apoptosis in a JNK-dependent manner and was associated with the intracellular replication of T. whipplei independently of JNK. This study underscores the role of macrophage polarization in host responses and highlights the detrimental role of type I IFN during T. whipplei infection

Dramatic Co-Activation of WWOX/WOX1 with CREB and NF-κB in Delayed Loss of Small Dorsal Root Ganglion Neurons upon Sciatic Nerve Transection in Rats

BACKGROUND:Tumor suppressor WOX1 (also named WWOX or FOR) is known to participate in neuronal apoptosis in vivo. Here, we investigated the functional role of WOX1 and transcription factors in the delayed loss of axotomized neurons in dorsal root ganglia (DRG) in rats. METHODOLOGY/PRINCIPAL FINDINGS:Sciatic nerve transection in rats rapidly induced JNK1 activation and upregulation of mRNA and protein expression of WOX1 in the injured DRG neurons in 30 min. Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury. At the second month, dramatic nuclear accumulation of WOX1 with CREB (>65% neurons) and NF-kappaB (40-65%) occurred essentially in small DRG neurons, followed by apoptosis at later months. WOX1 physically interacted with CREB most strongly in the nuclei as determined by FRET analysis. Immunoelectron microscopy revealed the complex formation of p-WOX1 with p-CREB and p-c-Jun in vivo. WOX1 blocked the prosurvival CREB-, CRE-, and AP-1-mediated promoter activation in vitro. In contrast, WOX1 enhanced promoter activation governed by c-Jun, Elk-1 and NF-kappaB. WOX1 directly activated NF-kappaB-regulated promoter via its WW domains. Smad4 and p53 were not involved in the delayed loss of small DRG neurons. CONCLUSIONS/SIGNIFICANCE:Rapid activation of JNK1 and WOX1 during the acute phase of injury is critical in determining neuronal survival or death, as both proteins functionally antagonize. In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptosis. Likely in vivo interactions are: 1) WOX1 inhibits the neuroprotective CREB, which leads to eventual neuronal death, and 2) WOX1 enhances NF-kappaB promoter activation (which turns to be proapoptotic). Evidently, WOX1 is the potential target for drug intervention in mitigating symptoms associated with neuronal injury

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes