The article presents a promising strategy to prevent or slow the progression of Type 1 diabetes by targeting an inflammation-related protein known to drive the disease. Researchers have found that applying a molecular method to block inflammation signaling through the tyrosine kinase 2 (TYK2) protein reduces harmful inflammation in the pancreas, protecting insulin-producing beta cells and calming the immune system’s attack on those cells.

Type 1 diabetes is a lifelong condition where the immune system mistakenly attacks and destroys insulin-producing beta cells in the pancreas. This leads to high blood sugar levels, requiring ongoing insulin therapy and careful monitoring to avoid severe health complications.

The study, co-led by Indiana University School of Medicine researchers, presents a potential new strategy using a medication that inhibits TYK2, which is already approved for the treatment of psoriasis, an autoimmune condition causing skin inflammation. This finding is exciting because there is already a drug on the market that can do this for psoriasis, which could help move toward testing it for Type 1 diabetes more quickly.

Past genetic studies have shown that people with naturally lower TYK2 activity are less likely to develop Type 1 diabetes, further supporting the group’s approach for future treatments using this TYK2 inhibitor approach.

The researchers hope their findings will support future clinical trials to safely assess the efficacy of a new drug or drug combination in humans. They emphasize the importance of initiating translational studies to evaluate the impact of TYK2 inhibition alone or in combination with other already approved drugs in individuals at-risk or with recent onset Type 1 diabetes.

The study’s lead author, Farooq Syed, PhD, notes that their preclinical models suggest that the treatment might work in people as well. The next step is to initiate translational studies to evaluate the impact of TYK2 inhibition alone or in combination with other already approved drugs in individuals at-risk or with recent onset Type 1 diabetes.

The research team hopes to support future clinical trials to safely assess the efficacy of a new drug or drug combination in humans, offering hope for a potential treatment approach for Type 1 diabetes.

For decades, medical researchers have been searching for ways to prevent organ transplant rejection. Current treatments focus on suppressing T cells, part of the adaptive immune system. However, this approach has its limitations. A new study from Mass General Brigham sheds light on a previously untapped area: the innate immune system. Researchers identified a natural “brake” within this system – the inhibitory receptor Siglec-E (SigE) and its human counterparts, Siglec-7 and Siglec-9. This receptor plays a crucial role in preventing overactivation of immune cells that drive rejection.

When this brake is missing or malfunctioning, inflammation worsens, leading to faster rejection in preclinical models. Importantly, transplant patients with higher levels of Siglec-7 and Siglec-9 showed better graft survival, highlighting this pathway as a promising target for new therapies. Results were published in Science Translational Medicine.

“For decades, we’ve focused almost exclusively on controlling T cells to prevent rejection,” said Leonardo Riella, MD, PhD, medical director of Kidney Transplantation at Massachusetts General Hospital (MGH). “Our research shows that the innate immune system plays a pivotal role. By harnessing natural inhibitory pathways like Siglec-E, we can develop safer, more precise therapies that protect transplanted organs without compromising overall immune health.”

To conduct their studies, the researchers used mouse models of heart, kidney, and skin transplantation to study the roles of SigE. They found that recipients deficient in SigE had accelerated acute rejection and increased inflammation. The researchers also looked at the levels of the receptors in samples from human transplant biopsies, finding that higher levels of the receptors were associated with improved allograft survival.

“This discovery paves the way for next-generation treatments that address both arms of the immune system, offering hope for longer-lasting transplant success and reducing the need for lifelong immunosuppression,” said Riella.

Early Cancer Detection: New Algorithms Revolutionize Primary Care

Two groundbreaking predictive algorithms have been developed to help General Practitioners (GPs) identify patients who may have undiagnosed cancer, including hard-to-detect liver and oral cancers. These advanced models use information about a patient’s health conditions and simple blood tests to accurately predict their chances of having an undiagnosed cancer.

The National Health Service (NHS) currently uses algorithms like the QCancer scores to combine relevant patient data and identify individuals at high risk of having undiagnosed cancer, allowing GPs and specialists to call them in for further testing. Researchers from Queen Mary University of London and the University of Oxford have created two new algorithms using anonymized electronic health records from over 7.4 million adults in England.

The new models are significantly more sensitive than existing ones, potentially leading to better clinical decision-making and earlier cancer diagnosis. Crucially, these algorithms incorporate the results of seven routine blood tests as biomarkers to improve early cancer detection. This approach makes it easier for patients to receive treatment at much earlier stages, increasing their chances of survival.

Compared to the QCancer algorithms, the new models identified four additional medical conditions associated with an increased risk of 15 different cancers, including liver, kidney, and pancreatic cancers. The researchers also found two additional associations between family history and lung cancer and blood cancer, as well as seven new symptoms of concern (itching, bruising, back pain, hoarseness, flatulence, abdominal mass, dark urine) associated with multiple cancer types.

The study’s lead author, Professor Julia Hippisley-Cox, said: “These algorithms are designed to be embedded into clinical systems and used during routine GP consultations. They offer a substantial improvement over current models, with higher accuracy in identifying cancers – especially at early, more treatable stages.”

Dr Carol Coupland, senior researcher and co-author, added: “These new algorithms for assessing individuals’ risks of having currently undiagnosed cancer show improved capability of identifying people most at risk of having one of 15 types of cancer based on their symptoms, blood test results, lifestyle factors, and other information recorded in their medical records.”