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Polygence Scholar2021
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Manasvi Pinnaka

Basis Independent Silicon ValleyClass of 2024Santa Clara, California

About

Projects

  • "How can lung microbiome composition, analyzed through 16s sequencing, be used to predict disease severity in a cystic fibrosis patient?" with mentor Brianna (Aug. 9, 2022)
  • "How can the lung microbiome be used to predict cystic fibrosis disease progression via FEV1 score?" with mentor Eric (Working project)

Project Portfolio

How can lung microbiome composition, analyzed through 16s sequencing, be used to predict disease severity in a cystic fibrosis patient?

Started Mar. 16, 2022

Abstract or project description

Cystic fibrosis patients often develop lung infections because of the presence of thick and sticky mucus that fills their airways, and some of the most common bacteria that cause these infections are pseudomonas aeruginosa and staphylococcus aureus. These bacterial infections can range anywhere in severity from mild to life-threatening, making it harder for patients to breathe and increasing the chance of mortality from respiratory failure. Thus, I examined the microbiology of cystic fibrosis patients, to predict the current condition or stage of lung function, as a way to guide doctors when planning the courses of treatment.

Using a publicly available dataset of DNA sequences from bacteria in the lung microbiomes of patients with cystic fibrosis, we investigate the existence of positive or negative correlations between the different microbial species in the lung and the extent of deterioration of lung function. I hypothesize that less diversity in the lung microbiome will be indicative of worsened lung function for cystic fibrosis patients. Furthermore, this decrease in diversity will be accompanied by the dominance of a specific type of bacteria over time. The outcome of this project will consist of a research paper, detailing whether this hypothesis was supported using 16s sequencing methodologies.

Project Portfolio

How can the lung microbiome be used to predict cystic fibrosis disease progression via FEV1 score?

Started Aug. 31, 2022

Abstract or project description

Cystic fibrosis patients often develop lung infections because of the presence of thick and sticky mucus that fills their airways. The presence of this thick mucus prevents the lungs from filtering out certain dominant bacterial types, making patients highly susceptible to infections that can range anywhere in severity from mild to life-threatening. These infections can cause great distress for patients as it becomes harder for patients to breathe and increases the chance of mortality by respiratory failure. It is important to be able to track the progression or regression of cystic fibrosis to determine the best course of treatment. Thus, this project focuses on the use of an AI model to examine the microbiology of cystic fibrosis patients and predict the condition or stage of lung function in the future, as a way to guide doctors with their treatment plan.