This study is the first time the drug PF-07868489 will be tested in humans. The goal is to see if the drug is safe and how the body reacts to it. In the first part, healthy adults will receive a single dose to check for any side effects and how the drug moves through the body. In the second part, patients with a specific lung condition called Pulmonary Arterial Hypertension (PAH) will receive multiple doses to see if the drug helps their condition and to further check its safety and effects.

As growing research suggests noninvasive brain stimulation techniques have the potential to adjunct current treatments or treat Seizure-Type Functional Neurologic Disorder (FND-seiz), also known as Psychogenic Non-Epileptic Seizures (PNES), we aim to evaluate whether a form of accelerated intermittent theta burst transcranial magnetic stimulation (a-iTBS-rTMS), is a practical and well-tolerated treatment for people with this disorder. Transcranial Magnetic Stimulation or TMS uses magnetic pulses to stimulate a part of the brain involved in mood and thinking, the left dorsolateral prefrontal cortex, which has established benefits in disorders known to coincide in patients with FND-seiz, such as depression.

As an open-label, early feasibility study, enrolled participants will receive 6 to 10 treatment sessions each day over 3 to 5 days, with the goal of completing 30 total sessions. This approach was selected because similar protocols have already been shown to be safe and effective in other conditions, and the shortened treatment schedule in comparison to other protocols may make participation easier for people living with FND-seiz. The main goal of the study is to see how many participants can safely and comfortably complete at least 20 of the 30 TMS sessions.

The researchers will also evaluate changes in seizure frequency, quality of life, mood, post-traumatic stress symptoms, physical health, social functioning, and overall satisfaction with treatment. These outcomes will be measured before treatment and again four weeks afterward. The researchers also aim to explore whether people with overlapping conditions, such as depression or PTSD, respond differently to the treatment. Finally, given the overlap between epilepsy and FND-seiz, not all TMS providers are comfortable treating patients with FND-seiz when TMS is indicated for other conditions, thus the researchers aim to outline a protocol to ensure safety and increase TMS access for FND-seiz patients.

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.

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.