This phase 2 study is screening patients who may have acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). This study is a non-treatment protocol and the first step of taking part in myeloMATCH, which is a clinical trial. The main purpose of the study is to see if testing on patient's bone marrow and blood results in finding certain biomarkers that will qualify participants for the treatment study or for Standard of Care (SOC) therapies. The study will enroll approximately 5000 patients. The study has two periods, initial and post-treatment screening. It takes about four days for the study doctor to receive the screening results and the patient's first treatment assignment in myeloMATCH. Patients will complete post-treatment screening after their participation in treatment trials or SOC therapies. It will take about 11 days for the study doctor to receive these results and decide their next treatment assignment. Further testing may match patients with myeloMATCH substudies in the future. The main risk is that biomarker test results may be wrong. Patients may have none, some, or all of the side effects listed or not listed in the protocol, and they may be mild, moderate, or severe. There is no direct benefit for them in participating in this study.

This study is for patients that have been diagnosed with previously untreated, locally advanced, and metastatic pancreatic ductal adenocarcinoma PDAC. This study is testing two treatment regimens: NALIRIFOX (5-fluorouracil, liposomal irinotecan, oxaliplatin, and leucovorin) vs mGAP (gemcitabine, nab-paclitaxel, and cisplatin).
The primary purpose of this study is to see which of the two regimens is more effective in PDAC. Participants will continue on study medications if seeing clinical benefit, and can expect to be on the study for a maximum of 6 months.

This phase 2 study is enrolling patients who have acute myeloid leukemia (AML) with certain biomarkers. This study is being done to see the effectiveness of different combinations of drugs to treat AML. It will involve 3 groups of patients receiving different combinations of Gilteritinib, Azacitidine and Venetoclax. Gilteritinib is an investigational drug, Azacitidine and Venetoclax are FDA approved. The main purpose of the study is to see if the amount of leukemia in the patient's body can be lowered by adding the drug Gilteritinib to the Standard of Care of treating AML with Azacitidine and Venetoclax. The study will include approximately 147 patients. The patients will be randomized into the three groups (like flipping a coin), Group 1 will receive just Azacitidine + Venetoclax, Groups 2 and 3 will also receive Gilteritinib but Group 2 will receive it for more time within a treatment cycle. Patients will complete screening after participating in this treatment trial or SOC therapies. Patients will continue treatment until disease progression, unacceptable toxicity, study closure, death, or withdrawal of consent. The main risk is that the study drugs may not be as good as the usual approach for their cancer or condition at shrinking or stabilizing their cancer. Patients may have none, some, or all of the side effects listed or not listed in the protocol, and they may be mild, moderate, or severe. There is no direct benefit for them in participating in this study.

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.