Electrospinning of PHBV/ZnO membranes: structure and properties - Naphade, Rounak; Jog, Jyoti Prakash (Fibers and Polymers, 2012)
Polyhydroxy butyrate-co-valerate (PHBV) - Zinc oxide (ZnO) nano composite fibers were prepared using electrospinning. The structural and optical properties were studied using Fourier transform infrared spectroscopy (FT-ER), X-ray diffraction (XRD) and photoluminescence study (PL). The morphology observed with scanning electron microscope (SEM) revealed no significant changes in the nano composite fibers as compared to bare polymer. The low concentration of ZnO nanoparticles resulted in an increase in overall crystallinity of the polymer matrix which was confirmed from FT-IR and XRD results. The photoluminescence (PL) study indicated the quenching of visible emission in the composite fibers. The ratio of UV to visible emission (I-uv/I-vis) intensity was found to be 12.8 times enhanced in the composite fibers compared to bare ZnO nanoparticles. The nanofibrous mats are self supported and hence offer potential applications in optoelectronic devices and the biomedical imaging.
Keywords: Biomarkers; Biopolymers; electrospinning; ZnO nanoparticles

Review on computational analysis of big data in breast cancer for predicting potential biomarkers - Shaikh, Nilofer; Bapat, Sanket; Karthikeyan, Muthukumarasamy; Vyas, Renu (Current Topics in Medicinal Chemistry, 2022)
Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.
Keywords: Big data; Biomarkers; Breast cancer; Driver genes; Network analysis; text mining