Faculty Publications

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Poly(N,N-diethyl acrylamide)/functionalized graphene quantum dots hydrogels loaded with doxorubicin as a nano-drug carrier for metastatic lung cancer in mice
(Elsevier Ltd, 2019) Havanur, S.; Batish, I.; Cheruku, S.P.; Gourishetti, K.; JagadeeshBabu, J.; Kumar, N.
Cancer has emanated as a daunting menace to human-kind even though medicine, science, and technology has reached its zenith. Subsequent scarcity in the revelation of new drugs, the exigency of salvaging formerly discovered toxic drugs such as doxorubicin has emerged. The invention of drug carrier has made drug delivery imminent which is ascribable to its characteristic traits of specific targeting, effective response to stimuli and biocompatibility. In this paper, the nanoscale polymeric drug carrier poly(N,N-diethyl acrylamide) nanohydrogel has been synthesized by inverse emulsion polymerization. Lower critical solution temperature of the polymeric carrier has been modified using graphene quantum. The particle size of pure nanohydrogel was in the range of 47 to 59.5 nm, and graphene quantum dots incorporated nanohydrogels was in the range of 68.1 to 87.5 nm. Doxorubicin (hydroxyl derivative of anthracycline) release behavior as a function of time and temperature was analyzed, and the Lower critical solution temperature of the synthesized nanohydrogels has been found to be in the range of 28–42 °C. Doxorubicin release characteristics have improved significantly as the surrounding temperature of the release media was increased near to physiological temperature. Further, the cumulative release profile was fitted in the different kinetic model and found to follow a Fickian diffusion release mechanism. The hydrogel was assessed for its cytotoxicity in B16F10 cells by MTT assay. In-vivo studies were done to study the lung metastasis by melanoma cancer and the results showed a rational favorable prognosis which was confirmed by evaluating hematological parameters and the non-immunogenic nature of nanohydrogel by cytokine assay. Comprehensively, the results suggested that poly(N,N-diethyl acrylamide) nanohydrogels have potential application as an intelligent drug carrier for melanoma cancer. © 2019 Elsevier B.V.
Hydroxyapatite—a promising sunscreen filter
(Springer, 2020) Pal, A.; Hadagalli, K.; Bhat, P.; Goel, V.; Mandal, S.
Exposure to ultraviolet (UV) radiation has been known to cause skin cancer, erythema, and sunburn. Continuous efforts have been made to make sunscreens more efficient and non-toxic. Inorganic sunscreens like TiO2 and ZnO are continued to be used for a few decades, and they are efficient in giving protection against harmful UV radiation, but they are photochemically active as well. They generate free radicals upon irradiation, which leads to reactive oxygen species (ROS) generation which is harmful to the human skin. Hydroxyapatite (HA) is a biocompatible material as it has a composition the same as the mineral content of the human bone; therefore, it is suitable for the dermatological application. Though HA itself does not provide protection against UV, studies on doped HA with various ions showed excellent performance. Pure HA absorbs only between 200 and 340 nm, with an intense band below 247 nm. HA doped with bivalent Zn2+, Fe2+, and trivalent Fe3+ and Cr3+, showed absorbance in the entire UV region. TiO2 provides absorbance in the entire UV range, while ZnO does so only in UVA. Compared to HA (refractive index, n = 1.6), TiO2 (n = 2.6) and ZnO (n = 1.9) have higher refractive index, which gives unwanted whitening effect. Additional properties can be brought in HA composites by adding material while retaining their individual properties. As HA is not photocatalytic, it does not lead to a generation of free radicals. This paper throws light on several aspects of HA-based sunscreen filters as an emerging future cosmetic material, and brief analysis and conclusions. © 2019, Australian Ceramic Society.
Multistage Image Reconstruction and Attention-Based Semi-Supervised Learning for Medical Image Segmentation
(SAGE Publications Ltd, 2025) Gawas, P.; Kamath S, S.; Singh, A.; Gurupur, V.
Automated segmentation of medical images is critical in detecting and diagnosing various conditions. In recent years, supervised deep learning (DL) techniques have been widely researched. However, their application is often limited by the availability of annotated data in the medical domain. To address this, recent studies have explored semi-supervised techniques, though very few of these works focus on skin-lesion segmentation. In addition, they struggle to effectively capture contextual features to delineate the region of interest from the surrounding tissues in the image, which is crucial for accurate segmentation. In this article, a semi-supervised approach for medical image segmentation called MIRA (Medical Image Reconstruction and Analysis) is proposed, which uses adaptive-attention U-Net (AA-U-Net) trained on pseudo-labels generated with a lightweight feature-consistent encoder-decoder network (FCED-Net) to address these challenges. A case study focusing on the precise segmentation of malignant skin lesions is considered for our experiments, as the scarcity of extensive annotated dermatology data limits the effectiveness of traditional DL models. The proposed pipeline is validated and tested using two standard datasets, ISIC2016 and PH2. With only 50% annotated samples, the proposed approach demonstrated promising performance with DSC, IoU, and accuracy of 0.96, 0.92, and 0.85 on ISIC2016 and 0.93, 0.88, and 0.93 on cross-data testing with PH 2 dataset. When benchmarked against leading edge models trained on 100% labeled data, MIRA achieved promising results and even outperformed in some cases. These findings show that it can significantly reduce manual annotation requirements while achieving segmentation performance comparable to models trained on fully annotated skin lesion data. © The Author(s) 2025

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