Browsing by Author "N, S."

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    Classification and grade prediction of kidney cancer histological images using deep learning
    (Springer, 2024) Chanchal, A.K.; N, S.; Lal, S.; Kumar, S.; Saxena, P.U.P.
    Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist’s experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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    Coexistence of Spin-Polarized Dirac and Weyl Nodes in 2D Electride Ferromagnet Yttrium Iodide
    (American Chemical Society, 2025) N, S.; S, A.K.; Ballav, N.; Tarafder, K.
    The unique electronic structure of yttrium iodide (YI) has been predicted for the first time, revealing nontrivial topological properties that combine spin-polarized Dirac and Weyl nodes within the same phase. The study identified nodal points present in both spin channels. The existence of Weyl nodes is further confirmed by the source and sink features in the Berry curvature plot and the presence of Fermi arcs in the surface band structure. The electron and hole packets are found to be connected at the Weyl point, classifying YI as a type-II magnetic Weyl semimetal. Under spin–orbit coupling (SOC), eight pairs of Weyl points exhibit robust behavior, while a small band gap forms at the spin-polarized Dirac cone. The system also demonstrates a high Fermi velocity, estimated at about 3.4 × 105 m/s, for Dirac Fermions and a giant anisotropic intrinsic anomalous Hall conductivity of up to 933 S/cm. © 2025 American Chemical Society
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    Development of electrospun scaffolds for bone regeneration from strontium-doped hydroxyapatite nanorods and thermoplastic polyurethane elastomer
    (Elsevier Ltd, 2025) Murugesan, S.; Patil, H.G.; Deshmukh, B.K.; N, S.; Asokan, A.; Mohapatra, A.; Lenka, N.; Anandhan, S.
    Strontium based biomaterials have gained importance in bone tissue regeneration due to their incredible osteoinductivity and differentiation ability. In this study, strontium-doped hydroxyapatite nanorods [SrHAp, Ca9Sr(PO4)6(OH)2] were synthesized by the coprecipitation method. Subsequently, electrospun fibrous scaffolds were fabricated from thermoplastic polyurethane elastomer (TPU) dispersed with SrHAp nanorods. The loading of SrHAp nanorods in TPU was varied from 1 wt% to 7 wt% in steps of 2. Morphology of electrospun fibrous scaffolds and the dispersion of nanorods in the TPU matrix were characterised by field emission scanning electron microscopy, and elemental mapping by energy-dispersive x-ray spectroscopy, respectively. The scaffolds exhibited 3D interconnected network structure with well-distributed pores. The SrHAp nanorods were observed to be smoothly dispersed in the polymer matrix in the scaffolds using elemental mapping and transmission electron microscopy. The newly developed scaffolds exhibited adequate mechanical strength combined with good biocompatibility and excellent biomineralization characteristics. Further, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay of the electrospun scaffolds against gingiva-derived mesenchymal stem cells (gMSCs) revealed excellent survival and growth rate of the cells. In addition, the osteoinductivity study using gMSCs confirms the better osteodifferentiation in the scaffold containing 5 wt% SrHAp compared with its counterparts by showing the expressions of alkaline phosphatase (ALP), osteocalcin (OCN) and RUNX2. Among all the compositions, the one with 3 wt% SrHAp loading demonstrated promising results in terms of fiber uniformity, improved mechanical properties, and enhanced cell viability. Thus, the SrHAp/TPU scaffolds developed in this study have the potential for use in bone tissue regeneration. © 2025 Elsevier Ltd