Bijun Kannadath
Early Career Tenure-track Researcher, University of Arizona
5 active projects
Solid tumor Research
Scientific Questions Being Studied
Cancer is the second most common cause of death in the United States. More than 600,000 Americans are expected to die of cancer in 2020. About 58% of these deaths could be prevented if cancer is detected at an early stage. At present population based screening tests are in place for early diagnosis of cancer. It is in part responsible for an overall reduction in cancer rates by 25% from 1990 to 2005. However population based screening still remains to be an imperfect method. In many parts of the world, participation of subjects in screening programs are at a lower level than desired. So additional methods for early cancer detection need to be employed. Machine learning algorithms have shown to help improve early detection of cancer. Our research effort aims to identify, evaluate and validate machine learning algorithms to predict the incidence, prognosis and complications of cancer, so as to create a more proactive approach to the management of cancer.
Project Purpose(s)
- Disease Focused Research (Solid tumors)
- Population Health
Scientific Approaches
The purpose of this study is to utilize machine learning algorithms to predict the incidence, prognosis and complications of solid tumors in adults.
Aims:
- to create an alternative and efficient screening tool for cancer detection
-to diagnose solid tumors at an early stage
-to reduce cancer morbidity and mortality
-to utilize lab values and basic patient information to detect people at risk for cancer
-to reduce healthcare costs, in the long term
Anticipated Findings
As a result of this study we anticipate detection of cancer at an early stage and thereby reduce the morbidity and mortality associated with it. All significant findings will be published in a high-impact journal and presented at academic conferences. The results of this study may give way to a new screening test for cancer.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered Tier
Checkpoint Inhibitors
Scientific Questions Being Studied
How many patients taking checkpoint inhibitors develop hypothyroidism after initiating therapy?
This question will help clinicians understand the likelihood of potential thyroid complications associated with checkpoint inhibitor therapy. In addition, it can guide diagnoses and treatment of patients and result in more comprehensive patient care.
Project Purpose(s)
- Disease Focused Research (hypothyroidism)
Scientific Approaches
We plan to use the All of Us research database to stratify patients by treatment with a checkpoint inhibitor and development of hypothyroidism after treatment initiation. We will evaluate data from 2012-2018 and calculate the incidence.
Anticipated Findings
We anticipate finding a higher incidence of hypothyroidism in patients on checkpoint inhibitor pharmacotherapy. These findings will help clinicians identify potential complications of treatment which will allow them to properly address concerns.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Nia Nikkhahmanesh - Graduate Trainee, University of Arizona
- Jiali Ling - Project Personnel, University of Arizona
- Bijun Kannadath - Early Career Tenure-track Researcher, University of Arizona
Collaborators:
- Anand Gandhi - Research Associate, Banner Health
- Emma Kar - Graduate Trainee, University of Arizona
Effect of Pyridoxine in Type 2 Diabetics V4
Scientific Questions Being Studied
The aim of this study is to investigate if Pyridoxine use can benefit diabetics in preventing long term complications by inhibiting formation of activated glycation end products and improving clinical outcomes.
Diabetes and hyperglycemia are affecting over 415 million people worldwide, and by 2040 the number is expected to increase to 642 million. Chronic hyperglycemia results in the glycation of proteins and other biomolecules resulting in generation of AGEs. Glycation can be identified as the core reason for diabetes associated disorders. The interaction of AGEs with their receptor elicits oxidative stress and as a result evokes proliferative, inflammatory, thrombotic and fibrotic reactions in a variety of cells. Therefore, inhibiting the glycation process might be an effective way to prevent the complications of chronic hyperglycemia.
Project Purpose(s)
- Disease Focused Research (Diabetes mellitus)
Scientific Approaches
Dataset: type 2 DM patients
Inclusion Criteria: Diabetics not on insulin, Age > 18 and < 70. A1c >6.5%
Exclusion Criteria: History of uncontrolled DM with A1c >8.5, hemoglobinopathy, sickle cell disease, thalassemia, anemia(iron def, pernicious anemia, B12 def., Folate def.) blood transfusion in the last 9 months, coagulopathy, blood thinner treatment, treatment with B6/B12/folate/iron in the last 3 months,treatment for TB or INH treatment, asplenia, pregnant or planning pregnancy in the next 6 months.
Research Method:
Blood and urine labs such as HGB A1c, HGB/HCT, fructosamine, fasting lipids, microalbuminuria, 24-hour creatinine/ protein, reticulocyte count and glycated albumin will be analysed.
Lab parameters and outcomes with patients on pyridoxine 100 mg po daily will be compared with subjects not on pyridoxine.
Anticipated Findings
1. Pyridoxine can decrease HbA1c in Type 2 diabetics
2. Pyridoxine can decrease Glycated albumin, Glycomark, microalbuminuria
If pyridoxine can reduce AGE without the risk of hypoglycemia and without other side effects , Pyridoxine should be used in diabetic patients. This will save diabetic patients from the known complications of hyperglycemia without the side effect of anti-diabetic medication.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Bijun Kannadath - Early Career Tenure-track Researcher, University of Arizona
Collaborators:
- Nia Nikkhahmanesh - Graduate Trainee, University of Arizona
- Jiali Ling - Project Personnel, University of Arizona
V5 of Checkpoint Inhibitors
Scientific Questions Being Studied
How many patients taking checkpoint inhibitors develop hypothyroidism after initiating therapy?
This question will help clinicians understand the likelihood of potential thyroid complications associated with checkpoint inhibitor therapy. In addition, it can guide diagnoses and treatment of patients and result in more comprehensive patient care.
Project Purpose(s)
- Disease Focused Research (hypothyroidism)
Scientific Approaches
We plan to use the All of Us research database to stratify patients by treatment with a checkpoint inhibitor and development of hypothyroidism after treatment initiation. We will evaluate data from 2012-2018 and calculate the incidence.
Anticipated Findings
We anticipate finding a higher incidence of hypothyroidism in patients on checkpoint inhibitor pharmacotherapy. These findings will help clinicians identify potential complications of treatment which will allow them to properly address concerns.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Nia Nikkhahmanesh - Graduate Trainee, University of Arizona
- Jiali Ling - Project Personnel, University of Arizona
- Bijun Kannadath - Early Career Tenure-track Researcher, University of Arizona
Collaborators:
- Anand Gandhi - Research Associate, Banner Health
- Emma Kar - Graduate Trainee, University of Arizona
Solid tumor Research
Scientific Questions Being Studied
Cancer is the second most common cause of death in the United States. More than 600,000 Americans are expected to die of cancer in 2020. About 58% of these deaths could be prevented if cancer is detected at an early stage. At present population based screening tests are in place for early diagnosis of cancer. It is in part responsible for an overall reduction in cancer rates by 25% from 1990 to 2005. However population based screening still remains to be an imperfect method. In many parts of the world, participation of subjects in screening programs are at a lower level than desired. So additional methods for early cancer detection need to be employed. Machine learning algorithms have shown to help improve early detection of cancer. Our research effort aims to identify, evaluate and validate machine learning algorithms to predict the incidence, prognosis and complications of cancer, so as to create a more proactive approach to the management of cancer.
Project Purpose(s)
- Disease Focused Research (Solid tumors)
- Population Health
Scientific Approaches
The purpose of this study is to utilize machine learning algorithms to predict the incidence, prognosis and complications of solid tumors in adults.
Aims:
- to create an alternative and efficient screening tool for cancer detection
-to diagnose solid tumors at an early stage
-to reduce cancer morbidity and mortality
-to utilize lab values and basic patient information to detect people at risk for cancer
-to reduce healthcare costs, in the long term
Anticipated Findings
As a result of this study we anticipate detection of cancer at an early stage and thereby reduce the morbidity and mortality associated with it. All significant findings will be published in a high-impact journal and presented at academic conferences. The results of this study may give way to a new screening test for cancer.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Bijun Kannadath - Early Career Tenure-track Researcher, University of Arizona
Collaborators:
- Jiali Ling - Project Personnel, University of Arizona
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