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Boddu Adilakshmi,1 Obireddy Sreekanth Reddy,2– 4 Duddekunta Hemalatha,1 Kummari SV Krishna Rao,1 Wing-Fu Lai2,3 1Polymer Biomaterial Design and Synthesis Laboratory, Department of Chemistry, Yogi Vemana University, Kadapa, Andhra Pradesh, 516005, India; 2Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Zhejiang, 310012, People’s Republic of China; 3Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong Special Administrative Region, People’s Republic of China; 4Department of Chemistry, Sri Krishnadevaraya University, Anantapuramu, Andhra Pradesh, 515003, IndiaCorrespondence: Wing-Fu Lai; Kummari SV Krishna Rao, Email rori0610@graduate.hku.hk; ksvkr@yogivemanauniversity.ac.inPurpose: Site-specific drug delivery systems can contribute to the development and execution of effective cancer treatment. Due to its favorable features (including high biocompatibility, high hydrophilicity and ease of functionalization), poly(ethylene glycol) (PEG) has been widely adopted to design drug carriers. Generating carriers for delivery of hydrophobic anticancer agents, however, is still a challenge in carrier design.Methods: In the first step, PEG is functionalized with dialdehyde to generate PEG-(CHO)2 using EDC/NHS chemistry. In the second step, Fe3O4 nanoparticles are functionalized with amino groups to generate Fe3O4-NH2. In the third step, PEG-(CHO)2, Fe3O4-NH2 and doxorubicin (DOX) react in an acidic environment to yield a drug conjugate (PEGDA-MN-DOX), which is subsequently characterized by FT-IR, 1H-NMR, SEM, TEM, DLS, TGA, and DSC.Results: The chemical functionalities of the drug conjugate are confirmed by FTIR, H-NMRand XRD analysis.The release pattern of PEGDA-MN-DOX is investigated at 25 and 37 °C at different pH values. The results indicate that the developed drug conjugate cannot only behave as a sustained-release carrier, but can also generate a significant level of reactive oxygen species (ROS), leading to a high level of toxicity against MCF-7 cells while still showing excellent biocompatibility in 3T3 cells.Conclusion: The reported conjugate shows anticancer potential, cancer-targeting ability, and ROS-generating capacity for effective drug encapsulation and sustained release in chemotherapy.Graphical Abstarct: Keywords: PEG, doxorubicin, magnetic nanoparticles, anti-cancer, biocompatibility, drug delivery

Bilateral adrenal metastases is a rare manifestation which can be seen in malignancies such as breast, lung carcinoma and melanomas. Adrenal metastases are usually detected on one side or more rarely on both sides. Molecular imaging with 18fluorodeoxyglucose positron emission tomography-computed tomography often shows unilateral or bilateral adrenal metastases in various malignancies. Here, we present a case of renal cell carcinoma that presented with widespread skeletal and bilateral adrenal metastases. [ABSTRACT FROM AUTHOR]

Burkitt's lymphoma (BL) is a highly aggressive type of B-cell non-Hodgkin lymphoma. BL commonly occurs in children of age 4-7 years and is uncommon in adults, with a worse prognosis. Patients often present with a rapidly growing mass typically involving the abdomen (liver and spleen) and head and neck (nodes, jaw, and facial bones). Pancreas involvement is very rare and very few case reports have been documented so far. Fluorine-18 positron emission tomography/ computed tomography (F-18 PET/CT) is a whole-body survey commonly used for initial staging evaluation. Here, we present an interesting case of BL in an adult female of 43 years, who presented with swelling in the left submandibular region after tooth extraction with multiorgan involvement found on F-18 fluorodeoxyglucose PET/CT. [ABSTRACT FROM AUTHOR]

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. [ABSTRACT FROM AUTHOR]