Monica Saifi-Biological Sciences- Young Scientist Award-5715

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Congratulations to Monica Saifi-Biological Sciences- Young Scientist  – Award Winner 2023
Monica Saifi-Biological Sciences

Heartiest Congratulations on Your Young Scientist Award, Dr. Monica Saifi, It is with immense joy and pride that we extend our warmest congratulations to you on receiving the Young Scientist Award. Your exceptional contributions to the field of plant biotechnology and your dedication to advancing research have rightfully earned you this prestigious honor. Your unwavering commitment to excellence, coupled with your profound expertise in Plant Metabolic Engineering, Genome Editing, and Medicinal Plants, has set a remarkable standard in the scientific community. This Young Scientist Award is a testament to your hard work, passion, and the impact you've made in your field

professional profile:

Early Academic Pursuits

Dr. Monica Saifi embarked on her academic journey with a Bachelor's degree in Science, majoring in Biochemistry, Biostatistics, Botany, Zoology, and Chemistry. She continued her educational pursuits with a Master's degree in Biotechnology from Jamia Hamdard, where she delved into subjects such as Biochemistry, Cell Biology, Molecular Biology, Plant Physiology, Genomics, Proteomics, Immunology, Bioinformatics, and Plant Biotechnology. Her academic journey reached its pinnacle with a Ph.D. in Biotechnology from the same institution, focusing on "In silico analysis, modulation and expression of miRNAs in Stevia rebaudiana to evaluate their impact on accumulation of steviol glycosides."

Professional Endeavors

Dr. Monica Saifi has accumulated more than twelve years of research experience in the field of plant biological sciences, specializing in Plant Metabolic Engineering of Medicinal plants, Plant Tissue Culture, and Genome Editing. Her significant roles include being a Research Scientist and Project Coordinator at M/s Hydrogreen Leaf Pvt. Ltd., where she contributed to genome editing in Cannabis spp. to develop THC-free plants for improved therapeutic efficacy. She also served as a Guest Faculty at Jamia Hamdard, teaching various aspects of biotechnology to M.Sc. and B.Sc. Biotechnology students.

Contributions and Research Focus

Dr. Saifi's research interests span across Plant Metabolic Engineering, Plant Biotechnology, Plant Molecular Biology, Genetic Engineering, Genome Editing, Plant Tissue Culture, Nutraceutical Research, Medicinal Plants, and Plant Biodiversity and Evolution. Her notable contributions include work on CRISPR/Cpf1 genome editing in rice and in silico analysis of miRNAs in Stevia rebaudiana to evaluate their impact on the accumulation of steviol glycosides.

Accolades and Recognition

Dr. Monica Saifi has received several accolades, including the World Intellectual Property Accreditation Scholar Medal for her outstanding performance in "Plant Molecular Biology." She was awarded first prize for the best paper presentation in the National Seminar on Biotechnology in Health Care at Jamia Hamdard in 2017. Additionally, she qualified GATE with 90 percentiles in Life Sciences in 2011 and received UGC-Maulana Azad National Fellowship in 2012.

Impact and Influence

Dr. Saifi's impact is evident in her extensive list of research papers, patent publications, and her role as an editor for a special issue on "Functional Genomics of Secondary Metabolite Biosynthesis in Medicinal Plants" in Frontiers in Genetics. Her teaching experience, along with her efforts in organizing conferences and workshops, showcases her commitment to knowledge dissemination and academic enrichment.

Legacy and Future Contributions

Dr. Monica Saifi's legacy is marked by her dedication to advancing knowledge in plant biotechnology and her role in fostering collaborations. Her future contributions are anticipated to further enrich the field of life sciences, with a focus on sustainable development, genetic engineering, and the exploration of plant biodiversity.

Enhancement of diterpenoid steviol glycosides by co-overexpressing SrKO and SrUGT76G1 genes in Stevia rebaudiana Bertoni  2023

Integrated miRNA, target mRNA, and metabolome profiling of Tinospora cordifolia with reference to berberine biosynthesis  2022 (3)

Natural inhibitors: A sustainable way to combat aflatoxins  2022 (9)

Set of miRNAs Involved in Sulfur Uptake and the Assimilation Pathway of Indian Mustard (B. juncea) in Response to Sulfur Treatments  2022

Identification and the potential involvement of miRNAs in the regulation of artemisinin biosynthesis in A. annua  2020 (3)

 

Sashikanta Prusty – Cancer Classification and Prediction- Best Researcher  – Award Winner 2023

Published on by Academic Achievements
Congratulations to Sashikanta Prusty – Cancer Classification and Prediction- Best Researcher  – Award Winner 2023

Sashikanta Prusty

Sashikanta Prusty is dedicated to the development and implementation of new technology tools and applications in healthcare, with a focus on employing adaptive deep learning/machine learning strategies for effective disease diagnosis to prevent unnecessary deaths.

professional profile:

Professional Endeavors

  • Industry Experience: 03 years
  • Teaching Experience: 04 years
  • Research Experience: 3 years

Contributions and Research Focus

Sashikanta Prusty has an extensive research focus in the field of healthcare technology, particularly in the development and implementation of new technology tools and applications. His primary research areas include:

  • Deep Learning/Machine Learning Strategies: Applied for disease diagnosis to prevent unnecessary deaths.
  • Meta-Learning Algorithm: Used in the development of SEMeL-LR for breast cancer classification.
  • Integrated Machine Learning-Fuzzy and Dimension Reduction Techniques: Applied for breast cancer prediction.
  • Transfer Learning Techniques: Developed a novel technique for detecting breast cancer mammograms using VGG16 bottleneck features.

Accolades and Recognition

  • Published Papers in Journals:
    1. SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer.
    2. Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques.
    3. SKCV: Stratified K-fold Cross-Validation on ML Classifiers for Predicting Cervical Cancer.
    4. Comparative analysis and prediction of coronary heart disease.
    5. A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms Using VGG16 Bottleneck Feature.
    6. Smart City E-Governance Through Intelligent ICT Framework.
    7. Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers.
    8. Internet of Things brings Revolution in eHealth: Achievements and Challenges.
    9. Designing a Pipeline for Predicting Power Grid Stability with Artificial Neural Network (ANN).
    10. Impact of COVID-19 on Breast Cancer Screening Program (BCSP) in India.
    11. Application of Artificial Intelligence in Fuzzy Logic for Crop Management in Agriculture.
  • Conference Contributions:
    • Presented papers in various international conferences, such as APSIT, DASA, ICIDeA, MLCSS, and ODICON.

Impact and Influence

Sashikanta Prusty has contributed significantly to the field of healthcare technology, specifically in the development of machine learning models for disease diagnosis. His work has been published in reputable journals and presented at international conferences, indicating a positive impact on the academic and research community.

Legacy and Future Contributions

While the provided information does not explicitly mention the legacy, Sashikanta Prusty’s continuous involvement in research and academia suggests a commitment to making further contributions in the field of healthcare technology, machine learning, and data analysis.

Notable Publication

Smart City E-Governance Through Intelligent ICT Framework. 31 January 2022

Time Series Analysis of SAR-Cov-2 Virus in India Using Facebook’s Prophet. 

SEMeL-LR: An improvised modeling approach using a meta-learning algorithm to classify breast cancer.  March 2024, 107630

Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques. 02 July 2023 1633-1652, 2023