암 예방을 위한 유산균의 효과

Reddy Bandaru S. 한국유가공협회 1993 牛乳 Vol.54 No.-

한국야쿠르트유업은 지난 8월27일 롯데호텔에서 ‘유산균과 건강’이란 주제로 국제학술심포지엄을 개최했다. 이날 심포지엄에서는 암예방을 위한 유산균의 효과(Bandaru S. Reddy 미국 보건연구재단 영양 · 암연구부장), 비피더스균을 이용한 유산균제품의 개발(Denis Roy 캐나다 농업연구소 식품연구개발센터 낙농부장), 유산균의 항암작용에 대한 실험연구(Wilhelm H · Holzapfel 독일 국립 영양연구센터 위생 및 독성연구소장) 등의 주제발표가 있었다. 본지는 이중 ‘암예방을 위한 유산균의 효과’를 전재한다.

A Review on Advanced Methodologies to Identify the Breast Cancer Classification using the Deep Learning Techniques

Bandaru, Satish Babu,Babu, G. Rama Mohan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

Identification of High Affinity Non-Peptidic Small Molecule Inhibitors of MDM2-p53 Interactions through Structure-Based Virtual Screening Strategies

Bandaru, Srinivas,Ponnala, Deepika,Lakkaraju, Chandana,Bhukya, Chaitanya Kumar,Shaheen, Uzma,Nayarisseri, Anuraj Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.9

Background: Approaches in disruption of MDM2-p53 interactions have now emerged as an important therapeutic strategy in resurrecting wild type p53 functional status. The present study highlights virtual screening strategies in identification of high affinity small molecule non-peptidic inhibitors. Nutlin3A and RG7112 belonging to compound class of Cis-imidazoline, MI219 of Spiro-oxindole class and Benzodiazepine derived TDP 665759 served as query small molecules for similarity search with a threshold of 95%. The query molecules and the similar molecules corresponding to each query were docked at the transactivation binding cleft of MDM2 protein. Aided by MolDock algorithm, high affinity compound against MDM2 was retrieved. Patch Dock supervised Protein-Protein interactions were established between MDM2 and ligand (query and similar) bound and free states of p53. Compounds with PubCid 68870345, 77819398, 71132874, and 11952782 respectively structurally similar to Nutlin3A, RG7112, Mi219 and TDP 665759 demonstrated higher affinity to MDM2 in comparison to their parent compounds. Evident from the protein-protein interaction studies, all the similar compounds except for 77819398 (similar to RG 7112) showed appreciable inhibitory potential. Of particular relevance, compound 68870345 akin to Nutlin 3A had highest inhibitory potential that respectively showed 1.3, 1.2, 1.16 and 1.26 folds higher inhibitory potential than Nutilin 3A, MI 219, RG 7112 and TDP 1665759. Compound 68870345 was further mapped for structure based pharamacophoric features. In the study, we report Cis-imidazoline derivative compound; Pubcid: 68870345 to have highest inhibitory potential in blocking MDM2-p53 interactions hitherto discovered.

FALLING FUZZY FILTERS IN BE-ALGEBRAS

Bandaru, Ravi Kumar,Rafi, N.,Davvaz, B. Chungcheong Mathematical Society 2017 충청수학회지 Vol.30 No.2

The notion of falling fuzzy filters of a BE-algebra is introduced based on the theory of falling shadows and fuzzy sets. Relation between fuzzy filters and falling fuzzy filters are presumed and characterized them interms of subsets of a sample space.

A Hybrid Optimized Deep Learning Techniques for Analyzing Mammograms

Bandaru, Satish Babu,Deivarajan, Natarajasivan,Gatram, Rama Mohan Babu International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

Photoluminescence and photocatalytic studies of rice water and papaya fruit extract-encapsulated cadmium sulfide nanoparticles

Bandaru Srinivasa Goud,Yathapu Suresh,Sathiraju Annapurna,Ganghishetti Bhikshamaiah,Mangalarapu Tarun Babu,Singh A. K. 한국세라믹학회 2023 한국세라믹학회지 Vol.60 No.1

Natural extracts can act as potential sources for the synthesis of nanoparticles in non-hazardous ways. The stabilization of nanoparticles can be done by any sufficiently large, quasi-polar, organic molecule. In the present study, cadmium sulfide nanoparticles (CdSNPs) encapsulated by natural extract have been synthesized via green chemical reduction route that uses natural stabilizers such as rice water, papaya fruit extracts and precursors such as cadmium chloride, cadmium nitrate, and cadmium sulfate. Different experimental techniques such as X-ray Diffraction (XRD), UV Visible Absorption Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy-Dispersive Spectroscopy (EDS), Small Angle X-ray Spectroscopy (SAXS), and Transmission Electron Microscopy (TEM) are used to confirm that the as-prepared samples contain cubic crystalline CdSNPs with average size less than 20 nm and a thin natural molecule layer developed on their surface. The luminescence properties of as-prepared CdSNPs are studied through photoluminescence measurements. The photoluminescence spectra of the CdSNPs have exhibited one broad peak along with shoulders on either side of it. Cadmium sulfide nanomaterials that belong to the II–VI group are known for their photocatalytic applications due to their efficient physical properties along with wide bandgap energy. Further, the as-prepared CdSNPs have exhibited their potentiality in degrading Methylene Blue (MB), and Rhodamine B (RhB) which can be attributed to their mixed phase.

Towards Versatile Continuous-Flow Chemistry and Process Technology Via New Conceptual Microreactor Systems

Ramanjaneyulu, Bandaru T.,Vishwakarma, Niraj K.,Vidyacharan, Shinde,Adiyala, Praveen Reddy,Kim, Dong-Pyo Korean Chemical Society 2018 Bulletin of the Korean Chemical Society Vol.39 No.6

In the past decade, microreaction technology has been attracted much attention to the scientific community as one of the subareas in chemical synthesis. The microreactor improves the yield with higher selectivity, and also facilitates the reactions by simple, safe, fast, and green approaches. This review gives an overview on our contributions to develop versatile continuous-flow syntheses and process technology by exampling gas-liquid binary phase in modified PDMS microreactors, and process intensification for safe operation of toxic/hazardous chemistry by generating hazardous chemicals to end utilization via various separation techniques in newly devised systems as well as ordinary capillary reactors. Furthermore, it covers process technology for ultrafast organic synthesis such as submillisecond control of short-lived intermediates in a polyimide chip reactor. These works provide outlooks for integrated and automated flow chemistry via one-flow/feed to end concept, i.e., useful in pharmaceutical industry, toward enabling new and innovative chemistry beyond limits of a batch reactor.

FALLING FUZZY FILTERS IN BE-ALGEBRAS

Ravi Kumar Bandaru,N. Rafi,Bijan Davvaz 충청수학회 2017 충청수학회지 Vol.30 No.2

The notion of falling fuzzy filters of a BE-algebra is introduced based on the theory of falling shadows and fuzzy sets. Relation between fuzzy filters and falling fuzzy filters are presumed and characterized them interms of subsets of a sample space.

An Optimized Deep Learning Techniques for Analyzing Mammograms

Satish Babu Bandaru,Natarajasivan. D,Rama Mohan Babu. G International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

Pharmacological Analysis of Vorinostat Analogues as Potential Anti-tumor Agents Targeting Human Histone Deacetylases: an Epigenetic Treatment Stratagem for Cancers

Praseetha, Sugathan,Bandaru, Srinivas,Nayarisseri, Anuraj,Sureshkumar, Sivanpillai Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.3

Alteration of the acetylation status of chromatin and other non-histone proteins by HDAC inhibitors has evolved as an excellent epigenetic strategy in treatment of cancers. The present study was sought to identify compounds with positive pharmacological profiles targeting HDAC1. Analogues of Vorinostat synthesized by Cai et al, 2015 formed the test compounds for the present pharmacological evaluation. Hydroxamte analogue 6H showed superior pharmacological profile in comparison to all the compounds in the analogue dataset owing to its better electrostatic interactions and hydrogen bonding patterns. In order to identify compounds with even better high affinity and pharmacological profile than 6H and Vorinostat, virtual screening was performed. A total of 83 compounds similar to Vorinostat and 154 compounds akin to analogue 6H were retrieved. SCHEMBL15675695 (PubCid: 15739209) and AKOS019005527 (PubCid: 80442147) similar to Vorinostat and 6H, were the best docked compounds among the virtually screened compounds. However, in spite of having good affinity, none of the virtually screened compounds had better affinity than that of 6H. In addition SCHEMBL15675695 was predicted to be a carcinogen while AKOS019005527 is Ames toxic. From, our extensive analysis involving binding affinity analysis, ADMET properties predictions and pharmacophoric mappings, we report Vorinostat hydroxamate analogue 6H to be a potential candidate for HDAC inhibition in treatment of cancers through an epigenetic strategy.