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Stem-cell (SC) chirality or left-right (LR) asymmetry is an essential attribute, observed during tissue regeneration. The ability to control the LR orientation of cells by biophysical manipulation is a promising approach for recapitulating their inherent function. Despite remarkable progress in tissue engineering, the development of LR chirality in SCs has been largely unexplored. Here, we demonstrate the role of substrate stiffness on the LR asymmetry of cultured mesenchymal stem cells (MSCs). We found that MSCs acquired higher asymmetricity when cultured on stiffer PCL/collagen matrices. To confirm cellular asymmetry, different parameters such as the aspect ratio, orientation angle and intensity of polarized proteins (Par) were investigated. The results showed a significant (p < 0.01) difference in the average orientation angle, the cellular aspect ratio, and the expression of actin and Par proteins in MSCs cultured on matrices with different stiffnesses. Furthermore, a Gaussian support-vector machine was applied to classify cells cultured on both (2% and 10% PCL/Collagen) matrices, with a resulting accuracy of 96.2%. To the best of our knowledge, this study is the first that interrelates and quantifies MSC asymmetricity with matrix properties using a simple 2D model.

No abstract

Autofluorescence imaging is an emerging method for detection of cancer associated risks, depending on the variation in autofluorescence property of normal and cancer cells. In the present study, autofluorescence images of cervical cell were assessed for early cervical cancer risk prediction. For classification of cervical epithelial cells collected from normal and clinically diagnosed cancer patients, a set of spectral texture features (SPTF) were extracted from 2-dimensional (2-D) Fourier transform of autofluorescence images. To discriminate the normal and the abnormal cells, SPTFs were evaluated by two 1-D functions, i.e., radial function and angular function, accumulated from the frequency spectrum. The classification was assessed by four different types of support vector machines (SVMs), namely, Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The best accuracy was achieved by Gaussian SVM (0.95) with a sensitivity of 0.90 and specificity of 0.88. The overall accuracy of all the classifiers was more than 0.82. This experiment was also evaluated by receiver operating characteristics (ROC) curve and area under ROC curve (AUC) of each classifier, which reveals the comparative results for this database.

In this study, we show how input power, terminal voltage, and efficiency changes as a function of load current. In this situation, we've chosen three scenarios they are- Coil connection in Parallel Coil connection in Series Two Winding Transformer Here, we have connected the coils of a single-phase transformer in series and parallel, then observed the transformer's loading as an autotransformer and as a two-winding transformer. It is essential to comprehend the instant polarities of the secondary terminals in relation to the primary in order to properly connect the winding.