This study is for subjects who has been diagnosed with radioactive iodine refractory (RAIR) differentiated thyroid cancer. Subjects are expected to remain in the study for a minimum of 96 months. Drugs are FDA approved and is given in the form of Tablet to subjects. The procedures include urine protein test, CT, MRI. Risks include diarrhea, nausea, vomiting, tiredness, weight loss, loss of appetite, changes in taste, redness, pain or peeling of palms and soles, High blood pressure. There is evidence that dabrafenib, trametinib and cabozantinib are effective in stabilizing and shrinking the type of cancer, we do not know which of these approaches are better at prolonging time until tumor growth. However, information learned from the trial may help other people in the future.

Cutaneous lupus is a common manifestation of childhood-onset Systemic Lupus Erythematosus (cSLE), affecting up to 85% of patients. Skin involvement can cause irritation, scarring, hair loss, changes in skin color and appearance, which may negatively impact quality of life and mental health. This study aims to assess the impact of cutaneous lupus on quality of life and mental health in diverse pediatric populations, with the goal of identifying disparities and improving individualized care. We will use validated surveys to assess disease burden on quality of life.

BTX-302-001 is a research study investigating the safety (how many side effects participants may have) and tolerability (how tolerable the side effects are) of BEAM-302 for individuals with Alpha-1 Antitrypsin Deficiency (AATD)-associated lung and/or liver disease. This study also aims to gather additional information regarding how BEAM-302 moves through the participant's body, how long it stays, and how long it takes to eliminate it - which is defined as the study drug's pharmacokinetics or "PK". Researchers would like to determine through this research study how BEAM-302 impacts the disease course (progression) of AATD in terms of AATD blood biomarkers, which are substances in blood that the body normally makes and will help show if an individual's AATD is improving, staying the same, or getting worse, along with lung and liver function testing results and the quality of life of participants.

This research study will be split into two main parts, Part A (which is for individuals with AATD-associated lung disease with no clear evidence of AATD-associated liver disease) and Part B (which is for individuals with AATD-associated liver disease). Additionally, each Part will be split into two separate cohorts, where one cohort will receive a single intravenous (IV) infusion of BEAM-302 (single-dose cohort) and the other will receive two IV infusions of BEAM-302 approximately 8 weeks apart (multi-dose cohort). Within these cohorts (single-dose and multi-dose), there are also separate smaller cohorts that will vary by the dose of BEAM-302 administered to participants, so a participant in this study could receive any of the following dosages - 15mg, 30mg, 60mg, 75mg, or 90mg. Overall, the research study will last up to around 29 months for each participant, depending on which cohort they are in, and their participation will be split into three main study periods - Screening, Dose and Dose-limiting toxicity (DLT), and Follow-up. It is also important to note that when a participant is receives their infusion(s) of BEAM-302 during the Dose and DLT period, the administration of the study drug will be done as a part of an in-patient hospital stay that will last up to 48 hours so that they can be closely monitored by the study team.

The key eligibility criteria for this study are that individuals (male or female) must be 18 to 70 years old, possess the PiZZ type of AATD, and have either AATD-associated lung disease with no clear evidence of AATD-associated liver disease or AATD-associated liver disease. There are additional eligibility criteria that must be met in order to be able to participate in the study, which will be assessed across up to 2 study visits that will occur during the Screening period.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.