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New research found that the protein MANF helps cells manage toxic protein clumps, improving cellular health and potentially aiding treatments for age-related diseases like Alzheimer’s and Parkinson’s.

Researchers at McMaster University have uncovered a previously unidentified cell-protective role of a protein, potentially paving the way for new treatments for age-related diseases and promoting healthier aging.

The team has found that a class of protective proteins known as MANF plays a role in the process that keep cells efficient and working well.

A newly published research study from the UNC Lineberger Comprehensive Cancer Center describes how the absence of the protein NLRP12 significantly increases susceptibility to colitis-associated colon cancer in pre-clinical models.

A family of proteins is yielding new information about how it contributes to the development of gastrointestinal disease and cancer. A team of UNC scientists reports that in pre-clinical models, the absence of a protein called NLRP12 significantly increases susceptibility to colitis-associated colon cancer.

The NLR family of proteins is very complex and scientists have determined that the majority of them act as activators of inflammation. However, scientists at UNC and elsewhere have recently reported that one NLR protein, NLRP12, actually functions to reduce disease by inhibiting a major inflammatory pathway mediated by a protein called NF-Kappa B activation has been long associated with inflammation and cancer promotion. But NF-Kappa B has an alternate signaling pathway that is not as well understood. This alternative pathway was the focus of the UNC team’s study. Their study was published in the April 12, 2012 online issue of the journal Immunity.

The new study demonstrates the ability to learn new skills without conscious effort.

Researchers have developed a groundbreaking technique using brain imaging and neurofeedback to induce learning without conscious effort.

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]).

Two years ago, a medical professional approached scientists at the University of Tabriz in Iran with an interesting problem: Patients were having headaches after pacemaker implants. Working together to investigate, they began to wonder if the underlying issue is the materials used in the pacemakers.

“Managing external noise that affects patients is crucial,” author Baraa Chasib Mezher said. “For example, a person with a brain pacemaker may experience interference from external electrical fields from phones or the sounds of cars, as well as various electromagnetic forces present in daily life. It is essential to develop novel biomaterials for the outlet gate of brain pacemakers that can effectively handle electrical signals.”

In an article published this week in AIP Advances, Mezher, who is an Iraqi doctoral student studying in Iran, and her colleagues at the Nanostructured and Novel Materials Laboratory at the University of Tabriz created organic materials for brain and heart pacemakers, which rely on uninterrupted signal delivery to be effective.

Cellular death is a fundamental concept in biological sciences. Despite its importance, its definition varies depending on the context in which it occurs and lacks a general mathematical definition.

Researchers from the University of Tokyo propose a new mathematical definition of death based on whether a potentially dead cell can return to a predefined “representative state of living,” which are the states of being that we can confidently call “alive.” The researchers’ work could be useful for biological researchers and future medical research.

While it’s not something we like to think about, death comes for us all eventually, whether you’re an animal, a plant, or even a cell. And even though we can all differentiate between what is alive and dead, it might be surprising to know that death at a cellular level lacks a widely recognized mathematical definition.

CAR-T and NK cells engineered using a non-viral circular single-stranded DNA system show effective anti-tumor function.

Higher levels of omega-6 fatty acids often found in ultraprocessed foods may interfere with the immune system’s fight against cancer cells, a new study says.

Researchers have developed a device that can simultaneously measure six markers of brain health. The sensor, which is inserted through the skull into the brain, can pull off this feat thanks to an artificial intelligence (AI) system that pieces apart the six signals in real time.

Being able to continuously monitor biomarkers in patients with traumatic brain injury could improve outcomes by catching swelling or bleeding early enough for doctors to intervene. But most existing devices measure just one marker at a time. They also tend to be made with metal, so they can’t easily be used in combination with magnetic resonance imaging.

Simultaneous access to measurements could improve outcomes for brain injuries.

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