Faezeh Darbaniyan’s Post

In my internship I've seen osteolysis due to cancer or cancer treatment.  Osteolysis means the destruction of bone and could have serious effects that may result in the inability to walk or use certain limbs.  The article below talks about this phenomenon and new ways to block this destruction.  This article talks about breast cancer specifically and how it can spread to the bones.  As the cancer spreads to the bones, it literally eats your bones and can cause them to collapse.  Scientists at the Huntsman Cancer Institute found that Ron cells “secrete acids, destroying the bone.” and without those Ron cells, bones would be protected from destruction.  It is impossible to remove genes from people so as an alternative Huntsman worked with a biotech company to block activity of the Ron gene.  This new Ron inhibitor showed great promise in decreasing osteolysis and was well tolerated with patients.  It is unknown if this drug would work well with other drugs or would help breast cancer patients specifically, but this new technology is certainly something to keep in mind.  Nebo School District CTE Internships Christine Thomas #nebointerns #nebo #cteinternships #cteintern #internships #MMHS #mmhsintern #medicine #healthcare #oncology

Our poster( Which I definitely forgot to upload) titled ' The clinical significance of exosome in patients with cancer' has won the Best Poster Award in Computational Biology and Health Science category at the International Conference on 'Advances and Challenges through Translational Research in Biological Science' held on 27th April. You can find the poster at this link: https://lnkd.in/ghi7NvDJ

Our latest research paper, "Advancing Breast Cancer Subtype Prediction and Mutation Analysis: Integrating Deep Learning and Machine Learning Techniques in Genomic Research," has been published in the Proceedings of the International Conference on Intelligent Systems and New Applications. 🔗 DOI: 10.58190/icisna.2024.83 In this collaborative effort between myself and my talented student, Samhita Gadamsetty, at ICER, VIT Bangalore, we explore how deep learning and machine learning can significantly improve the accuracy of breast cancer subtype predictions and mutation analysis. This study could lead to more personalized and effective treatment strategies. 🔍 Key Insights: Integration of ML techniques with genomic data. Enhanced predictive accuracy for breast cancer subtypes. Advanced mutation analysis for targeted therapies. We are grateful to all the contributors and supporters of our research. Your dedication is helping advance the fight against breast cancer. Feel free to dive into our full article and share your thoughts or questions. Let’s continue to innovate and make meaningful advancements in cancer research! #BreastCancerResearch #MachineLearning #DeepLearning #Genomics #ICISNA2024 #VITBangalore

Completed my Last project "Breast cancer detection prediction" using ML in my internship at InternPe Early detection of breast cancer significantly increases the chances of successful treatment and survival. This project focuses on leveraging machine learning techniques to predict the likelihood of breast cancer based on medical data. In this project, I developed a machine learning model that predicts whether a patient has breast cancer based on diagnostic features. The dataset used includes various attributes obtained from clinical measurements and imaging tests, such as tumor size, texture, and shape. By training the model on this data, it can accurately classify whether a tumor is benign or malignant. The final model demonstrated high accuracy in predicting breast cancer, with a significant improvement over traditional methods. The project highlights the potential of machine learning in enhancing diagnostic processes and supporting healthcare professionals in making informed decisions.

Treating cancer even more gently and precisely – a team of scientists has been working towards this ambitious goal since the beginning of March 2024. And we from Fraunhofer ENAS are also involved.   The four-year project “𝗡𝗲𝘅𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗼𝘀𝗲 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝘁𝗼𝗻 𝗧𝗵𝗲𝗿𝗮𝗽𝘆” (𝗡𝗢𝗩𝗢) aims to achieve groundbreaking progress towards even more targeted cancer treatment using so-called proton therapy. In this procedure, high-energy radiation is delivered directly into the diseased tissue, which destroys the tumor in a targeted manner without damaging the surrounding healthy tissue. For this form of proton-based cancer therapy, the researchers are developing a new AI-based imaging system that will work in real time and is intended to verify that the dose of radiation reaches exactly where it is needed by exploiting measurements of secondary radiations produced by the protons. As part of the project, our colleagues will also develop new and ultra-fast algorithms based on artificial intelligence in order to process the generated images and support personnel in changing the treatment in case a deviation from the planned irradiation is detected. With their help, the destruction of the tumorous tissue can be specifically controlled and monitored and the radiation dose can be tailored individually to the patient.   As part of the project, we as Fraunhofer ENAS are contributing our many years of expertise as research and development partner for solutions in the fields of medicine, biology and life sciences. The focus of our developments is on the application in combination with technical and technological aspects, in particular on the use of micro and nanotechnologies and methods of artificial intelligence, which make new applications possible for use in the health sector and medical technology.   We look forward to contributing to advances in patient-centered cancer treatment with the project and our work! If you would like to support us on this journey, do not hesitate to send us your 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – 𝘄𝗲 𝗮𝗿𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗺𝗮𝘀𝘁𝗲𝗿'𝘀 𝘁𝗵𝗲𝘀𝗲𝘀, 𝗶𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗮𝗻𝗱 𝘀𝘁𝘂𝗱𝗲𝗻𝘁 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 in this exciting field: https://lnkd.in/ee7pu_df   #Cancer #ArtificialIntelligence #Imaging ©Frontiers in Physics, 2023, https://lnkd.in/e-TkpiNe

Glad to share my recent work at the American Association for Cancer Research conference on Expanding and Translating Cancer Synthetic Vulnerabilities. Many thanks to the AACR Scholar-in-Training Award for making this possible. In short, this work leverages the wealth of CRISPR data for cancer cell lines and cross-applies it to predict clinically relevant synthetic lethalities in tumors using interpretable machine learning. 1️⃣ The developed computational method provides an avenue to mitigate the challenge of carrying out genome-wide CRISPR screens in tumor samples, as the synthetic lethalities can be predicted using the genomics and transcriptomics data alone. Alongside predictions, the interpretable ML approach provides useful explanations of why a gene is synthetic lethal or not. 2️⃣ The predicted synthetic lethalities show cancer-specific enrichment of molecular functions; thus offering a glimpse into the unique mechanistic vulnerabilities that inhabit proliferating cancer cells. This work also highlights ➡️ the rich resource of paralogous duplicate genes for identifying synthetic lethalities, ➡️ the predictive power of functional redundancies, and ➡️ the importance of carrying out cancer subtype-wise predictions. To know more, please see the abstract in AACR Journals: Predicting targetable paralog synthetic lethalities and functional redundancies in cancer genomes 🔗 https://lnkd.in/ekNfnDDD

In a recent GEN interview, Dr. Christina Curtis, co-chair of AACR and a professor at Stanford University, highlights how the significant progress made in spatial profiling technology will be a major theme at this year's #AACR24 meeting. "Another theme is the huge advance in spatial profiling which allows us to look at human tissue at unprecedented resolution across different scales—from the subcellular, to cellular, and up to intercellular," says Dr. Curtis. If you're attending #AACR24, attend Dr. Arutha Kulasinghe's spatial profiling presentation on "Ultra high-plex profiling of the tumor microenvironment." He will discuss the development of ultrahigh-plex antibody panels to comprehensively characterize the tumor microenvironment on Saturday, April 6, from 12:36 PM to 12:54 PM PT in Session MW14—Choosing and Using Antibodies for Spatial Informed Protein Expression. Genetic Engineering & Biotechnology News American Association for Cancer Research #SpatialBiology #SpatialProfiling

Mohammad Jouybar, the PhD candidate at our section, was among the winners of the Materials-Driven Regeneration Gravitation program (MDR) Young Talent Incentives-2024!   His proposal “Immunotypic on-chip model for investigating CAR-T cell therapy,” will focus on generating an immunotypic vessel-on-chip model to study tumor vasculature targeting CAR-T cell therapy. Chimeric antigen receptor (CAR)-T cell therapy is a potentially promising approach for treatment of solid tumors. Organ-on-Chip models are suitable for modeling disease and test potential therapeutics, such as CAR-T cell therapy. This project will be a collaborative effort with experts in Boston University and Wyss Institute at Harvard University, and angiogenesis lab at Amsterdam UMC - Cancer Center Amsterdam. This funding will support Mohammad to do a 3-months internship in Christopher Chen Lab at Boston University. Read more in https://lnkd.in/e2QBq_W7    #Research #Innovation #Funding #CAR_Tcelltherapy #Organ_on_Chip #regenerative_medicine 

Could this be the END of cancer? In a groundbreaking development, chemist James Tour from Rice University has introduced a new generation of molecular machines, dubbed "molecular jackhammers." These innovative machines operate over a million times faster than previous Feringa-type motors, with the added advantage of being activated by near-infrared light instead of visible light. The significance of using near-infrared light cannot be overstated. It allows for deeper penetration into the body, opening up new possibilities for treating cancers located in bones and organs without the need for invasive surgery. This approach has the potential to revolutionize cancer treatment. In laboratory tests involving cultured cancer cells, these molecular jackhammers achieved a remarkable 99 percent success rate in destroying the cells. Further, when tested on mice with melanoma tumors, half of the animals were completely cured of cancer. The secret to the efficacy of these molecular machines lies in the structure and chemical properties of aminocyanine molecules. These molecules remain synchronously active under the right stimulus, such as near-infrared light. During activation, the electrons within the molecules generate plasmons, collectively vibrating entities that facilitate movement across the entire molecule. Chemist Ciceron Ayala-Orozco, also from Rice University, emphasizes the novel mechanism underpinning these machines. This is the first known instance where a molecular plasmon is used to energize an entire molecule, resulting in mechanical action aimed at a specific objective, namely destroying cancer cells' membranes. The plasmons in these molecules feature an arm-like structure on one side, which aids in attaching the molecules to cancer cell membranes. The vibrational movements then effectively break apart these membranes. While this research is still in its early stages, the initial results are highly promising, suggesting a bright future for this innovative cancer treatment approach. Have a read! https://lnkd.in/gEtCGH_F #Warehouseai #science #health #development Follow Me For More Posts at the Intersection of AI Gaming and Retail Andrew Green Warehouse AI

Dear LinkedIn Network, The time has come for me defend my thesis research from these past 5 years at Stanford! If you're interested in hearing about spectral cytometry and cancer multiomics, feel free to attend virtually on March 20th, 2:30pm PST: https://lnkd.in/g9xh3TBa. Looking forward to seeing you there! Defense Abstract: High-dimensional tissue profiling on the axes of time, cell type, and treatment is necessary to unravel the heterogeneity within systems-wide responses to disease. We are witnessing a renaissance in the development of these methods, which have enabled greater parameterization, accuracy, and throughput than previously achieved for studying systems biology. We first utilize techniques in spectral cytometry to study the transfection efficiency of T cell targeted LNPs as a function of time and tissue. We packaged reporter gene mRNA into CD3-targeted LNPs and successfully transfected murine T cells in vivo that were capable of migrating to tumors in the presence of immunotherapy. While promising, we discovered a variety of consequences associated with anti-CD3 coating that sparked questions about immune subsets beyond T cells. These questions prompted us to engineer a 40-color deep immunophenotyping panel for murine lymphoid tissues and tumors. We used this tool to unlock insights into tissue leukocyte composition and immune response mechanisms to combinatorial immunotherapy. Only after achieving this level of resolution did we fully appreciate how the spatial organization of immune, stromal, and tumor cells could inform their function. To explore this, we harmonized 51-plex CODEX with Visium spatial transcriptomics to deconvolve pancreatic ductal adenocarcinoma microenvironments. These insights culminated in the development of an anti-Claudin-4 peptide PET probe for PDAC imaging in mice, and I conclude this talk with microdosimetry modelling techniques to aid in future theragnostic development for PDAC treatment.