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Category: biotech/medical – Page 1,069

Scientists at the University of Pittsburgh School of Medicine have discovered the missing puzzle piece in the mystery of how melanoma tumors control their mortality.

In a paper published in Science this week, Jonathan Alder, Ph.D. and his team describe how they discovered the perfect combination of genetic alterations that tumors use to promote explosive growth and prevent their own demise, a development that could change the way oncologists understand and treat melanoma.

“We did something that was, in essence, obvious based on previous basic research and connected back to something that is happening in patients,” said Alder, assistant professor in the Division of Pulmonary, Allergy and Critical Care Medicine at Pitt’s School of Medicine.

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Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collaboration that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s larges | Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collab that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s largest deal to date.

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Recently, two patients in the United Kingdom received two small doses of lab-grown blood samples as part of the RESTORE trial.

Image Credit: Sashkin / Shutterstock.com

About the RESTORE trial

The RESTORE trial is a single-center, randomized, controlled, phase I cross-over trial that is a joint effort between the National Health Service (NHS) Blood and Transplant (NHSBT) and the University of Bristol. The aim of this trial is to determine whether blood cells manufactured from donor stem cells perform better than red blood cells (RBCs) obtained from the same donor in recipients.

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The findings come, in part, from nine patients involved in an ongoing Swiss study that is seeking to restore movement to people with paralysis.

All nine rapidly regained the ability to stand and walk with the help of implants that electrically stimulate spinal nerves that control lower-body movement.

Now the researchers are reporting that they’ve identified a specific group of cells in the lower spine that appear necessary for that movement recovery to happen.

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Researchers at UC Riverside are paving the way for diabetes and cancer patients to forget needles and injections, and instead take pills to manage their conditions.

Some drugs for these diseases dissolve in water, so transporting them through the intestines, which receive what we drink and eat, is not feasible. As a result, these drugs cannot be administered by mouth. However, UCR scientists have created a chemical “tag” that can be added to these drugs, allowing them to enter via the intestines.

The details of how they found the tag, and demonstrations of its effectiveness, are described in a new Journal of the American Chemical Society paper.

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This past Monday (Nov. 7th) I was given a behind the scenes look at Dr. Alfonso Fasano’s neuromodulation clinic, one of the busiest of its kind in the world.

On his schedule for that day were about 25 patients, which is fairly typical for him. But these were not exactly 25 patients that a typical doctor might see in a given day. They were patients whom the Canadian medical system had funneled to him, individuals who not even most movement disorder specialists, let alone neurologists or general practitioners, could properly treat. For him and his team though, it was just another Monday.

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The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. ML models are constantly being developed to improve healthcare efficiency and outcomes, from classification to semantic segmentation, object detection, and image generation. Numerous published reports in diagnostic radiology, for example, indicate that ML models have the capability to perform as good as or even better than medical experts in specific tasks, such as anomaly detection and pathology screening.

It is thus undeniable that, when used correctly, AI can assist radiologists and drastically reduce their labor. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias. Data scarcity and data imbalance are two of these challenges. On the one hand, medical imaging datasets are frequently much more minor than natural photograph datasets such as ImageNet, and pooling institutional datasets or making them public may be impossible due to patient privacy concerns. On the other hand, even the medical imaging datasets that data scientists have access to could be more balanced.

In other words, the volume of medical imaging data for patients with specific pathologies is significantly lower than for patients with common pathologies or healthy people. Using insufficiently large or imbalanced datasets to train or evaluate a machine learning model may result in systemic biases in model performance. Synthetic image generation is one of the primary strategies to combat data scarcity and data imbalance, in addition to the public release of deidentified medical imaging datasets and the endorsement of strategies such as federated learning, enabling machine learning (ML) model development on multi-institutional datasets without data sharing.

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Tohid Didar and Jeff Weitz had a solution, but they also had a problem.

Didar, an associate professor of engineering and Weitz, a hematologist, professor of medicine and executive director of the Thrombosis & Atherosclerosis Research Institute, had collaborated to create a novel and highly promising material to improve the success of vascular grafts, but they needed a better way to test how well it worked.

Their revolutionary idea was an engineered non-stick surface combined with biological components that can repel all but a targeted group of cells — those that form the natural lining of the body’s veins and arteries.

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