Response projected clustering for direct association with physiological and clinical response data

<p>Abstract</p> <p>Background</p> <p>Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes.</p> <p>Results</p> <p>We introduced a novel clustering analysis approach, <it>response projected clustering </it>(RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the <it>in vitro </it>growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis.</p> <p>Conclusion</p> <p>We have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.</p

Enhancement of Mechanical Properties of PCL/PLA/DMSO2 Composites for Bone Tissue Engineering

Bone tissue engineering shows potential for regenerating or replacing damaged bone tissues by utilizing biomaterials renowned for their biocompatibility and structural support capabilities. Among these biomaterials, polycaprolactone (PCL) and polylactic acid (PLA) have gained attention due to their biodegradability and versatile applications. However, challenges such as low degradation rates and poor mechanical properties limit their effectiveness. Dimethyl sulfone (DMSO2) has emerged as a potential additive to address these limitations, offering benefits such as reduced viscosity, increased degradation time, and enhanced surface tension. In this study, we investigate tailored composites comprising PLA, PCL, and DMSO2 to enhance mechanical properties and hydrophilicity. Through material characterization and mechanical testing, we found that the addition of DMSO2 led to improvements in the yield strength, modulus, and hydrophilicity of the composites. PCL and DMSO2 10, 20, and 30 wt% were premixed, and 20 wt% PCL + 10, 20, and 30 wt% DMSO2 were mixed with PLA. Specifically, PLA/PCL/DMSO2 composites exhibited higher yield strengths and moduli compared to pure PLA, pure PCL, and PLA/PCL composites. Moreover, the hydrophilicity of the composites increased with DMSO2 concentration, facilitating cell attachment. Fourier-transform infrared spectroscopy (FTIR) confirmed the presence of –COOH and –COH bands in PLA/PCL/DMSO2 composites, indicating chemical interactions between DMSO2 and the polymer matrix. Fractography analysis revealed enhanced interface adhesion in PLA/PCL/DMSO2 composites due to the hydrogen bonding. Overall, this study demonstrates the potential of PLA/PCL/DMSO2 composites in bone tissue engineering applications, offering improved mechanical properties and enhanced cell compatibility. The findings contribute to the advancement of biomaterials for additive manufacturing in tissue engineering and regenerative medicine

Role of Binder on Yield Strength of polycaprolactone/dimethylsulfone composites for bio-applications

Polycaprolactone (PCL) and dimethylsulfone (DMSO2) composites can tailor the properties of scaffold materials, allowing their use in bone tissue engineering. With an increase in DMSO2 content, the modulus of the material increases but not the yield strength. In order to increase yield strength, a binder was added. However, the optimization of the content and the mixing process of the binder were not optimized in the previous studies. In this study, gamma-methacryloxypropyltrimethoxysilane (A-174) was used as a binder to increase the strength of a composite. Four different mixing processes were employed based on the binder mixing sequence. The binders with content of 0, 0.4, 0.5, 0.7, and 1.5 phr were employed. The yield strengths of composites were investigated in terms of the binder mixing sequence and binder content. When the binder and DMSO2 particle fillers were premixed in the PCL matrix consisting of a DMSO2 filler and an A-174 binder system, the filler surface was coated smoothly and uniformly, and less agglomeration occurred. The yield strength of the composites with the appropriate mixing sequence was 36.71 % higher than that of the specimen without a binder, which was attributed to the improved adhesion between the matrix and fillers. Upon increasing the binder content, elongation and tearing of the matrix surface were observed in the cross-sections after yield tests; however, the weakening of mechanical anchoring was caused by excessive binder content, and filler debonding was observed on the surface. Because of the use of the A-174 silane binder at a concentration of 0.5 phr and the premixing of the binder and filler, the highest performance in terms of strength improvement of a PCL-20 wt % DMSO2 composite was achieved

Development of Prediction Method for Dimensional Stability of 3D-Printed Objects

Fused deposition modeling (FDM), as one of the additive manufacturing processes, is known for strong layer adhesion suitable for prototypes and end-use items. This study used a multiple regression model and statistical analysis to explore the dimensional accuracy of FDM objects. Factors such as inclination angle, layer thickness, support space, and raster angle were examined. Machine learning models (Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN)) predicted dimensions using 81 datapoints. The mean squared dimensional error (MSDE) between the measured and designed surface profiles was selected as an output for the dimensional accuracy. Support spacing, layer thickness, and raster angle were determined to be statistically significant, and all factors were confirmed as significant predictors. The coefficients of determination for multiple linear regression, GPR, SVM, and ANN models were 76%, 98%, 93%, and 99%, respectively. The mean absolute errors (MAEs)—errors between the measured and the predicted MSDEs—were 0.020 mm and 0.034 mm, respectively, for GPR and SVM models. The MAEs for ANN models were 0.0055 mm for supporting cases and 2.1468 x 10 -5 mm for non-supporting cases

PCL and DMSO2 Composites for Bio-Scaffold Materials

Polycaprolactone (PCL) has been one of the most popular biomaterials in tissue engineering due to its relatively low melting temperature, excellent thermal stability, and cost-effectiveness. However, its low cell attraction, low elastic modulus, and long-term degradation time have limited its application in a wide range of scaffold studies. Dimethyl sulfone (DMSO2) is a stable and non-hazardous organosulfur compound with low viscosity and high surface tension. PCL and DMSO2 composites may overcome the limitations of PCL as a biomaterial and tailor the properties of biocomposites. In this study, PCL and DMSO2 composites were investigated as a new bio-scaffold material to increase hydrophilicity and mechanical properties and tailor degradation properties in vitro. PCL and DMSO2 were physically mixed with 10, 20, and 30 wt% of DMSO2 to evaluate thermal, hydrophilicity, mechanical, and degradation properties of the composites. The water contact angle of the composites for hydrophilicity decreased by 15.5% compared to pure PCL. The experimental results showed that the mechanical and degradation properties of PCL and DMSO2 were better than those of pure PCL, and the properties can be tuned by regulating DMSO2 concentration in the PCL matrix. The elastic modulus of the composite with 30 wt% of DMSO2 showed 532 MPa, and its degradation time was 18 times faster than that of PCL

Rheological Properties and 3D Printing Behavior of PCL and DMSO2 Composites for Bio-Scaffold

The significance of rheology in the context of bio three-dimensional (3D) printing lies in its impact on the printing behavior, which shapes material flow and the layer-by-layer stacking process. The objective of this study is to evaluate the rheological and printing behaviors of polycaprolactone (PCL) and dimethyl sulfone (DMSO2) composites. The rheological properties were examined using a rotational rheometer, employing a frequency sweep test. Simultaneously, the printing behavior was investigated using a material extrusion 3D printer, encompassing varying printing temperatures and pressures. Across the temperature range of 120–140 °C, both PCL and PCL/DMSO2 composites demonstrated liquid-like behavior, with a higher loss modulus than storage modulus. This behavior exhibited shear-thinning characteristics. The addition of DMSO2 10, 20, and 30 wt% into the PCL matrix reduced a zero-shear viscosity of 33, 46, and 74% compared to PCL, respectively. The materials exhibited extrusion velocities spanning from 0.0850 to 6.58 mm/s, with velocity being governed by the reciprocal of viscosity. A significant alteration in viscosity by temperature change directly led to a pronounced fluctuation in extrusion velocity. Extrusion velocities below 0.21 mm/s led to the production of unstable printed lines. The presence of distinct viscosities altered extrusion velocity, flow rate, and strut diameter. This phenomenon allowed the categorization of pore shape into three zones: irregular, normal, and no-pore zones. It underscored the importance of comprehending the rheological aspects of biomaterials in enhancing the overall quality of bio-scaffolds during the 3D printing process

Development of a thermal sensor to probe cell viability and concentration in cell suspensions

This paper presents a novel biothermal sensor to probe cell viability and concentration of a cell suspension. The sensing technique exploits the thermophysical properties of the suspension, so no labeling of suspended cells is required. When the sensor is periodically heated, the amplitude and phase of the thermal signal are dependent on the thermal properties of the cell suspension, particularly the thermal conductivity k. We measured k of HeLa, hepatocyte, and NIH-3T3 J2 cell suspensions with various concentrations and viabilities. The results demonstrate that the k of a cell suspension has a strong correlation with its concentration and viability. Accordingly, k can be employed as an index of cell concentration and viability. Furthermore, without data processing to obtain k, the electric signal that reflects the thermal response of the sensor can be used as a tool to probe viability of a cell suspension in real time. The proposed thermal sensing technique offers label-free, non-invasive, long-term, and real-time means to probe the viability and concentration of cells in a suspension. (C) 2014 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.X1134sciescopu