9 research outputs found

    A study on awareness of Breast Carcinoma amongst the women aged 15 years and above in Urban slums of Turbhe, Navi Mumbai

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    Background: Breast cancer is second most important cancer among Indian women. In India, the number of new breast cancer cases is about 115,000 per year. Although risk factors are not much prevalent as in western countries, incidence rate is increasing in India. Breast cancer awareness is an effort to raise awareness and reduce the stigma of breast cancer through education on symptoms and treatment. Objectives: Present study has the objective to assess the level of awareness about Carcinoma Breast and to study the knowledge and practice regarding screening methods of breast carcinoma Material & Methods: A cross sectional study was conducted among 160 women aged 15 years & above by simple random sampling. Pretested preformed questionnaire was used. Verbal consent was taken prior to interview. Data analysis was done by SPSS version 20 & MS Office Excel 2007. Results: 58.7% women had no knowledge regarding breast carcinoma. 93.12% women had no knowledge regarding risk factors of it. Only 8 women had knowledge about Breast Self Examination and amongst them only 2 practice it. Only 2 women had knowledge about mammography and MRI as screening method. Conclusion: Majority of women had no knowledge regarding Risk factors & Screening methods of Breast Ca. Education has positive impact on knowledge of symptoms of breast carcinoma

    Product Purchase Recommendation Of User By Data Analysis Using Data Mining

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    With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users’ rating behaviors. In order to predict user-service ratings, we focus on users’ rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, what the rating is, 2) what the item is, 3) what the user interest that we could dig from his/her rating records is, and 4) how the user’s rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users’ daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors, user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users’ rating behavior habits), and interpersonal rating behavior diffusion (related to users’ behavior diffusions), into a unified matrix-factorized framework. We conduct a series of experiments in Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach

    Analysis of Complement Receptor Type 1 (CR1) Polymorphisms and Its Association With Malaria in Rural Population of Maharashtra

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    The interaction between human host and the Plasmodium parasite is complex. The factors affecting the causality of infection and its severity are yet not completely understood. Single Nucleotide Polymorphisms (SNP) associated with CR1 may be associated with patho-physiology of malaria and its susceptibility to the disease. Methods: The objective of the present study was to calculate the incidence of various antigens of Knops blood group system and CR1 Exon22 polymorphisms in rural population from Chiplun Taluka of Ratnagiri district. The study included 112 malaria positive cases and 909 healthy controls, which were screened for CR1 Exon22 polymorphism. Knops (Kna/b), McCoy (McCa/b), Swain-Langley (Sl1/2) polymorphisms were screened in 93 cases and 321 healthy controls. The frequencies were determined using a PCR-RFLP technique. Results: Only wild types of the allele form were observed in Knops blood group system in malaria cases and healthy control. CR1 exon22 polymorphism was seen in the study population with all the 3 allele type distributed in the cases and control samples. No significant allelic or genotypic differences were found between the controls and the disease groups. Conclusion: The results of the present study demonstrate that common CR1 Exon22 and Knops blood group system are not associated with malaria in the endemic area

    PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

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    Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery

    PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

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    Abstract Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery

    Abstracts of AICTE Sponsored International Conference on Post-COVID Symptoms and Complications in Health

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    This book presents the selected abstracts of the International Conference on Post-COVID Symptoms and Complications in Health, hosted from the 28th to 29th of April 2022 in virtual mode by the LR Institute of Pharmacy, Solan (H.P.)-173223 in Collaboration with AICTE, New Delhi. This conference focuses on the implications of long-term symptoms on public health, ways to mitigate these complications, improve understanding of the disease process in COVID-19 patients, use of computational methods and artificial intelligence in predicting complications, and the role of various drug delivery systems in combating the complications. Conference Title:  International Conference on Post-COVID Symptoms and Complications in HealthConference Sponsor: AICTE, New Delhi.Conference Date: 28-29 April 2022Conference Location: OnlineConference Organizer: LR Institute of Pharmacy, Solan (H.P.)-173223

    Abstracts of AICTE Sponsored International Conference on Post-COVID Symptoms and Complications in Health

    No full text
    This book presents the selected abstracts of the International Conference on Post-COVID Symptoms and Complications in Health, hosted from the 28th to 29th of April 2022 in virtual mode by the LR Institute of Pharmacy, Solan (H.P.)-173223 in Collaboration with AICTE, New Delhi. This conference focuses on the implications of long-term symptoms on public health, ways to mitigate these complications, improve understanding of the disease process in COVID-19 patients, use of computational methods and artificial intelligence in predicting complications, and the role of various drug delivery systems in combating the complications. Conference Title:  International Conference on Post-COVID Symptoms and Complications in HealthConference Sponsor: AICTE, New Delhi.Conference Date: 28-29 April 2022Conference Location: OnlineConference Organizer: LR Institute of Pharmacy, Solan (H.P.)-173223
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