Search

Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease. [ABSTRACT FROM AUTHOR]

The potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy. [ABSTRACT FROM AUTHOR]

The linear properties of magnetosonic waves are studied in nearly degenerate and nearly non‐degenerate quantum plasmas composed of electrons, positrons and ions in the presence of spin‐12 effect. Using the fluid equations, a generalized dispersion relation for perpendicular and oblique propagation is derived. It is found that degree of temperature and spin degeneracy modify the dispersive properties of the given modes. The results of analysis are beneficial for understanding the collective phenomena in dense quantum astrophysical plasmas. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome or PCOS is an endocrine disorder in women of reproductive age. It is a diversified multi factorial disorder and diagnosis is very complicated because of its overlapping symptoms some of which are irregular menstrual cycle, acne in face, excess level of androgen (AE), insulin resistance, obesity, cardiovascular disease, mood disorder and type 2 diabetes (T2DM). PCOS may be caused by hormonal imbalance, genetic and epigenetic vulnerability, hypothalamic and ovarian troubles. PCOS is essentially hyperandrogenimia with oligo‐anovulation. This review explains the abnormal regulation of autophagy related genes and proteins in different cells at various stages which leads to the genesis of PCOS. During nutrient starvation cells face stress condition, which it tries to overcome by activating its macroautophagy mechanism and by degrading the cytoplasmic material. This provides energy to the cell facilitating its survival. Downregulation of autophagy related genes in endometria has been observed in PCOS women. PCOS can be managed by maintaining proper lifestyle and medical treatment. Healthy meals and regular exercise can prevent the excessive weight and also reduce the PCOS complications. Medicines such as metformin, clomiphene, and the oral contraceptive pill can also balance the hormonal level. The imbalance in regulation of autophagy genes has been discussed with correlation to PCOS. The different management strategies for PCOS have also been summarized. [ABSTRACT FROM AUTHOR]

Plant‐derived natural products are of great interest due to their diversity in modern drug discovery. Sarcococca saligna has been used for the treatment of different diseases. The present study was aimed at isolating phytochemical constituents including Alkaloid‐C (a), Dictyophlebine (b), Sarcovagine‐D (c) and Saracodine (d) Holaphylline (e) from Sarcococca saligna to investigate the anticancer effect of these compounds. These compounds were evaluated for inhibition of aromatase enzyme of breast cancer in assistance by molecular docking simulations to understand molecular interaction between the enzyme and ligands. The IC50 values of compound 1 and 5 were found 138.27 ± 0.01 µl and 12.91 ± 0.01 µl, respectively, and both were found active due to their bulky structures in comparison to the active site of aromatase enzyme. The standard drug exemestane showed potent activity in comparison with the test compounds, having IC50 values of 0.052 ± 0.01 µl. Both compounds showed favorable electrostatic interactions with the active site of aromatase enzyme but the shape and steric bulk of the compounds was the limiting factor in their inhibitory effects. New lead compounds could be generated after extensive modifications guided by computational and experimental tools as a possible anticancer agents by targeting aromatase enzyme. [ABSTRACT FROM AUTHOR]

In a quest to use Raman spectroscopy as an optical diagnostic tool, we recorded Raman spectra of 32 dengue virus (DENV)-infected and 28 healthy sera samples in the near-infrared spectral range (540 to 1700 cm−1) using laser at 785 nm as the excitation source. We observed clear differences in the Raman spectra of DENV-infected sera as compared with those of healthy individuals. Here, as a result of our study, we report 12 unique Raman bands associated with DENV-infected sera that are not reported earlier. After applying analysis of variance and t-test ( p < 0.05) on these 12 bands, six Raman bands at 630 ( N-acetylglucosamine), 883 (in-plane bending (ring) of deoxyribose), 1218 (amide III- β conformation from C6H5-C stretching vibrations of tryptophan and phenylalanine), 1273 (amide-III), 1623 (tryptophan) and 1672 cm−1 (ceramide) were found only in the DENV-infected sera. The remaining six Raman bands at 716 (lipids), 780 (Uracil-based ring breathing mode), 828 (ring breathing tyrosine), 840 ( α-anomers), 1101 (ν(C-N) of lipids and DNA) and 1150 cm−1(glycogen/carotenoids) were only found in healthy sera. Two types of classification models, principal component analysis and linear discriminant analysis, were employed to develop principal component analysis-linear discriminant analysis model that has provided diagnostic accuracy 96.50%, sensitivity 93.44%, and specificity 100%. This indicates that these 12 Raman bands have the potential to be used as biomarkers for optical diagnosis of DENV infection. This study provides a new insight for future research in the field of optical diagnosis using Raman spectroscopy. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]