A study has revealed that gluten sensitivity, which affects approximately 10% of the global population, is not actually about gluten but part of the way the gut and brain interact.
The findings are expected to set a new benchmark for how gluten sensitivity is defined, diagnosed and treated.
The research review, published today in The Lancet, examined current published evidence for non-celiac gluten sensitivity (NCGS) to better understand this highly prevalent condition.
Identifying the factors that contribute to psychopathology and increase the risk of experiencing specific mental health conditions is a long-standing goal for many psychology researchers. While past studies have highlighted the crucial role of some experiences, particularly challenging events unfolding during childhood and adolescence, in the development of mental health disorders, their influence is often difficult to quantify and differentiate from other factors that could contribute to psychopathology.
Recent technological advances, particularly the development of increasingly sophisticated machine learning and computational analysis tools, have opened new possibilities for the study of mental health disorders and their underlying patterns. When used to analyze the large amounts of data collected by mental health services and professionals over the past decades, these methods could help to uncover correlations between specific variables and hidden trends that are associated with psychopathology.
Researchers at Washington University in St. Louis and Washington University School of Medicine recently set out to explore the possible contribution of different factors to poor mental health among teenagers using data mining techniques (i.e., computational approaches to uncover patterns in data). Their findings, published in Nature Mental Health, suggest that social experiences, particularly conflicts between family members, bullying or a loss of reputation among peers, are the strongest predictors of psychopathology in adolescents.
Imagine the magnificent glaciers of Greenland, the eternal snow of the Tibetan high mountains, and the permanently ice-cold groundwater in Finland. As cold and beautiful as these are, for the structural biologist Kirill Kovalev, they are more importantly home to unusual molecules that could control brain cells’ activity.
Kovalev, EIPOD Postdoctoral Fellow at EMBL Hamburg’s Schneider Group and EMBL-EBI’s Bateman Group, is a physicist passionate about solving biological problems. He is particularly hooked by rhodopsins, a group of colorful proteins that enable aquatic microorganisms to harness sunlight for energy.
“In my work, I search for unusual rhodopsins and try to understand what they do,” said Kovalev. “Such molecules could have undiscovered functions that we could benefit from.”
Chronic traumatic encephalopathy (CTE) is more common in people who experience extensive repetitive head impacts, and infrequent among individuals with isolated brain injuries or less extensive impacts, researchers from the Brain Injury Research Center of Mount Sinai have found.
The study, published in the Journal of Neuropathology & Experimental Neurology, adds to knowledge of CTE, which has received extensive media attention amidst limited research in representative samples.
CTE is characterized by a neurodegenerative pathology involving abnormal accumulations of tau protein in the brain associated with head trauma, primarily identified in deceased people who sustained extensive exposure to repetitive head impacts while playing contact sports—especially American-style football. CTE has been reported more rarely in individuals who sustained repetitive head impacts through head-banging, military service, or intimate partner violence.
A nice primer article discussing the biology, structure, and dysfunction of the blood-brain-barrier. Quite useful for learning. #neurophysiology #biomedicine
Here, Audrey Chagnot and Axel Montagne discuss the structural and functional features of the blood–brain barrier and the association of its disruption with various pathologies.
Stanford Medicine researchers found cells that keep a speech-linked protein called FOXP2 from clumping; its tricks could break apart clumps of proteins that cause devastating brain diseases.
This week, researchers reported the discovery of four Late Bronze Age stone megastructures likely used for trapping herds of wild animals. Physicists have proven that a central law of thermodynamics does not apply to atomic-scale objects that are linked via quantum correlation. And two Australian Ph.D. students coded a software solution for the James Webb Space Telescope’s Aperture Masking Interferometer, which has been producing blurry images.
Additionally, researchers are networking tiny human brain organoids into a computing substrate; evolutionary biologists have proposed that environmental lead exposure may have influenced early human brain evolution; and physicists have provided a predictive model to explain accelerating universal expansion without dark matter:
The number of people living with dementia worldwide was estimated at 57 million in 2021 with nearly 10 million new cases recorded each year. In the U.S., dementia impacts more than 6 million lives, and the number of new cases is expected to double over the next few decades, according to a 2025 study. Despite advancements in the field, a full understanding of disease-causing mechanisms is still lacking.
To address this gap, Rice University researchers and collaborators at Boston University have developed a computational tool that can help identify which specific types of cells in the body are genetically linked to complex human traits and diseases, including in forms of dementia such as Alzheimer’s and Parkinson’s.
Known as “Single-cell Expression Integration System for Mapping genetically implicated Cell types,” or seismic, the tool helped the team hone in on genetic vulnerabilities in memory-making brain cells that link them to Alzheimer’s ⎯ the first to establish an association based on a genetic link between the disease and these specific neurons. The algorithm outperforms existing tools for identifying cell types that are potentially relevant in complex diseases and is applicable in disease contexts beyond dementia.