Tulane University School of Medicine

PLYMOUTH, Minn., Oct. 22, 2024 /PRNewswire/ -- Peytant Solutions, Inc. (Peytant) today announced that the U.S. Food and Drug Administration (FDA) granted marketing authorization (clearance to market in the United States as a Class II device) for the AMStent® Tracheobronchial Covered Stent System, a novel, proprietary therapy platform. The AMStent® System is indicated for use in the treatment of tracheobronchial strictures produced by malignant neoplasms in adult patients. Continue Reading The AMStent System® is designed to treat pulmonary obstructions caused by cancer. Post this The AMStent® System Implant includes a stent covered in a material created from amnion. The AMStent® System is the first product offering within Peytant's amnion-based covered stent platform. The system combines a minimally invasive catheter delivery system with a stent covered in a material created from amnion. Amnion has more than 100 years of clinical history, first as a transplant for eye surface reconstruction, and more recently as a therapeutic membrane for the treatment of chronic wounds and skin ulcers. The AMStent System® is designed to treat pulmonary obstructions caused by cancer. In the United States, lung cancer is the leading cause of cancer deaths, accounting for about 1 in 5 of all cancer deaths. Many patients with cancer in the lung develop malignant airway obstructions as the disease progresses. Palliative care is often pursued by increasing airflow in the trachea or tracheobronchial tree through dilation of the lumen. Once airflow is restored, it is routine to place airway stents in the passageway. The benefits of the AMStent® System, as seen in validation data, include reduced migration, reduced local inflammation and granuloma formation, and reduced mucus accumulation and occlusion – so important to achieving the desired palliative relief for the patient. "To effectively treat blockages caused by cancerous tumors, physicians require devices that maintain efficacy and reduce adverse events," said Craig M. Walker, MD, Founder, President and Medical Director of Cardiovascular Institute of the South in Houma, Louisiana and Clinical Professor of Medicine at Tulane University School of Medicine and LSU School of Medicine in New Orleans. "The AMStent® System is a unique option for patients who need it most," states Dr. Walker. Peytant is the pioneer in the development of human amnion-based covering for stents. "We are optimistic that the AMStent® System will prove to be a much-needed option for patients suffering from tracheobronchial obstruction due to malignant tumors," said John Schorgl, Peytant Cofounder and CEO. "With the availability of the AMStent® System, we hope to improve the palliative care and thereby the quality of life for these patients." About Peytant Solutions, Inc. Peytant is a medical technology manufacturer seeking to improve outcomes for patients with luminal blockages. Peytant, based in Plymouth, Minnesota, is focused on commercializing the AMStent® System, a platform technology comprising an amnion-based covered stent and catheter delivery system. Peytant's AMStent® System is the first step toward transforming the treatment of luminal diseases. SOURCE Peytant Solutions, Inc. WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

Researchers have developed a novel deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk. Osteoporosis is so difficult to detect in early stage it's called the "silent disease." What if artificial intelligence could help predict a patient's chances of having the bone-loss disease before ever stepping into a doctor's office? Tulane University researchers made progress toward that vision by developing a new deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk. Their results were recently published in Frontiers in Artificial Intelligence. Deep learning models have gained notice for their ability to mimic human neural networks and find trends within large datasets without being specifically programmed to do so. Researchers tested the deep neural network (DNN) model against four conventional machine learning algorithms and a traditional regression model, using data from over 8,000 participants aged 40 and older in the Louisiana Osteoporosis Study. The DNN achieved the best overall predictive performance, measured by scoring each model's ability to identify true positives and avoid mistakes. "The earlier osteoporosis risk is detected, the more time a patient has for preventative measures," said lead author Chuan Qiu, a research assistant professor at the Tulane School of Medicine Center for Biomedical Informatics and Genomics. "We were pleased to see our DNN model outperform other models in accurately predicting the risk of osteoporosis in an aging population." In testing the algorithms using a large sample size of real-world health data, the researchers were also able to identify the 10 most important factors for predicting osteoporosis risk: weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, years of smoking, and income level. Notably, the simplified DNN model using these top 10 risk factors performed nearly as well as the full model which included all risk factors. While Qiu admitted that there is much more work to be done before an AI platform can be used by the public to predict an individual's risk of osteoporosis, he said identifying the benefits of the deep learning model was a step in that direction. "Our final aim is to allow people to enter their information and receive highly accurate osteoporosis risk scores to empower them to seek treatment to strengthen their bones and reduce any further damage," Qiu said.

Researchers have designed a platform to perform blood-based diagnoses of nontuberculosis mycobacteria, simplifying and shortening a long-complicated procedure from 6 months to 2 hours. Rapid, accurate diagnosis has never been more important as cases of NTM infections have continued to increase annually, spreading from subtropical regions to more temperate zones in part due to global climate change. Inhaling nontuberculous mycobacteria is common for most people. The bacteria are found in water systems, soil and dust worldwide and, for many, cause no harm. For those with underlying conditions, nontuberculous mycobacteria (NTM) can infect the lungs, causing symptoms similar to tuberculosis. Inflammation can cause a chronic and sometimes bloody cough as well as scarring, which can make respiratory infections like bronchitis and pneumonia more common. Due to the slow growth of the bacteria, proper diagnosis and treatment can take months. In a new study, Tulane University researchers have developed a CRISPR-based platform for diagnosing NTM infections where blood testing can yield results in as little as two hours. The preliminary findings, published in the American Journal of Respiratory and Critical Care Medicine, showed that the blood test was able to accurately identify more than 93% of patients with an NTM infection. Rapid, accurate diagnosis has never been more important as cases of NTM infections have continued to increase annually, spreading from subtropical regions to more temperate zones in part due to global climate change. "NTM infection is highly underestimated, and due to the slow diagnosis of it, patients with NTM infection are not effectively treated," said Bo Ning, corresponding author and assistant professor of molecular biology at Tulane University School of Medicine. "Importantly, our blood test can analyze NTM DNA fragments in the bloodstream, thereby inferring a drug response, which is crucial for rapidly determining treatment plans, capabilities traditional diagnostic approaches cannot achieve." The blood test is specifically designed to detect mycobacteria avium complex (MAC), one of the most common types of NTM and the most common cause of NTM-induced pulmonary disease. To identify an infection, the test detects fragments of NTM DNA in the bloodstream. Current diagnosis methods rely on analysis of long-term cultures of the slow-growing bacteria, which can lead to delays in diagnosis and treatment. And with more than 190 species of NTM, correctly diagnosing an infection can be difficult. "Currently, correct diagnosis and treatment can last more than six months," Ning said. "Not only can our blood test provide same-day results, this test can be quickly performed in any clinics where blood can be drawn and does not require specialized training or equipment needed to analyze bacteria cultures." Ning said the United States may face rising NTM infections in the future and that accessible tests like this one are vital to staying ahead of it. Going forward, he hopes to expand the types of NTM that CRISPR can detect and develop point-of-care tests for NTM detection. "The goal is to reduce barriers to testing and diagnosis and prepare for a potential surge of NTM infections," Ning said. The study was conducted in collaboration with Oregon Health & Science University.