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New tool predicts cardiovascular disease risk more accurately

A new risk prediction tool developed by the American Heart Association (AHA) estimated cardiovascular disease (CVD) risk in a diverse patient cohort more accurately than current models, according to a recent study published in Nature Medicine.

The tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations which was developed in 2023, could help health care providers more accurately identify patients who have higher CVD risk and enhance preventive care efforts, according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author of the study.

“Evaluating the new PREVENT equations in a diverse sample of patients is critical to provide primary care providers and cardiologists with further assurance that they can utilize these equations to accurately predict patients’ CVD risk, particularly in vulnerable populations,” said Khan, who is also an associate professor of Medical Social Sciences in the Division of Determinants of Health and of Preventive Medicine in the Division of Epidemiology.

Blood plasma reveals shared pathways in neurodegenerative diseases

Scientists know that many proteins and pathways are involved in the development and progression of neurodegenerative conditions such as Alzheimer’s disease, Parkinson’s disease and frontotemporal dementia (FTD), and that these proteins can be detected in the plasma of people with the conditions.

But it hasn’t been clear exactly which proteins are distinct to one disease vs. shared among two or more of them, adding to the difficulty both of diagnosing these complex diseases from and of developing effective treatments.

A new study by Washington University School of Medicine in St. Louis researchers, published in Nature Medicine, provides some answers. Led by Carlos Cruchaga, the Barbara Burton & Reuben Morriss III Professor in the Department of Psychiatry and director of the NeuroGenomics and Informatics Center at WashU Medicine, the researchers analyzed in more than 10,500 blood plasma samples from patients with Alzheimer’s disease, Parkinson’s disease or FTD.

APOE ε4 variant reveals hidden risk factors beyond Alzheimer’s

Further analysis for immune-specific processes revealed APOE ε4 enrichment in various infection-related pathways, including herpes, influenza A, hepatitis, measles, and Epstein-Barr virus (EBV). Significant enrichment was also observed for B-cell, T-cell, and inflammatory signaling cascades. Next, immune cell subtype enrichment analysis revealed the most APOE ε4 enrichment in intermediate and non-classical monocytes among innate immune cells.

Among adaptive immune cells, memory cluster of differentiation 8 (CD8) T cells, regulatory T (Treg) cells, and memory CD4 T cells were the most enriched. Besides, γδ T cells and natural killer (NK) cells showed APOE ε4 enrichment. In the liver, a cell-type-specific enrichment analysis revealed the most APOE ε4 enrichment in Kupffer cells and hepatocytes.

Next, the researchers examined whether APOE ε4 CSF proteome changes were reflected in the plasma and used the GNPC dataset for plasma proteome profiling of AD, PDD, FTD, PD, ALS, and non-impaired controls. Fifty-eight plasma proteins associated with the APOE genotype were identified in non-impaired controls. CART modeling revealed that these 58 proteins could strongly differentiate between APOE ε4 carriers and non-carriers across neurodegenerative diseases, and this signature was found to be consistent across different sexes and racial groups.

Muscle-derived vesicles heal damaged cells and reverse disease in new study

Researchers discovered that mitochondria-rich extracellular vesicles (Ti-mitoEVs) from skeletal muscle can deliver healthy mitochondria to damaged cells, restoring energy and promoting tissue repair in mice. The approach reversed acute muscle injury and chronic kidney disease, offering a promising new regenerative therapy.

Self-powered solar panels remove dust using wind-generated electricity

A collaborative research team has successfully developed a self-powered pollution prevention technology that can remove pollutants from the surface of solar panels without external power. This technology uses a wind-powered rotational triboelectric nanogenerator to generate power and combines said power with electrodynamic screen (EDS) technology to move dust in the desired direction for removal.

The findings are published in the journal Nano Energy. The team was led by Professor Juhyuck Lee from the Department of Energy Science and Engineering, Daegu Gyeongbuk Institute of Science & Technology, along with Dr. Wanchul Seung at Global Technology Research, Samsung Electronics.

The dust that gathers on the surface of solar panels causes a significant reduction in power production efficiency. EDS technology, designed to address this problem, uses electric fields to remove dust from the surface, and it is noted for environments that are not easily accessible, such as deserts, mountains, and space, as it does not require cleaning equipment or personnel. Traditional EDS technology, however, requires and, consequently, external power, and it has the disadvantage of additional maintenance costs.

Liver drives cancer cachexia through systemic signaling response, study finds

Many people with cancer experience dramatic loss of muscle and fat tissue. In many cases, even the heart muscle is affected, which further weakens the body. This wasting syndrome, known as cachexia, affects around half of all cancer patients. It is a major cause of therapy resistance, complications, and increased mortality.

Researchers from Helmholtz Munich, in collaboration with Heidelberg University Hospital, the Technical University of Munich, and the German Center for Diabetes Research have now identified a previously overlooked driver of cachexia: the liver. It responds systemically to tumors in other organs—such as the intestine or pancreas—and contributes to tissue wasting by releasing specific signaling molecules.

The study is published in the journal Cell.

Psychopathic traits linked to distinct brain networks in new neuroscience research

Psychopathy is often associated with impulsivity, aggression, and antisocial behavior. While past studies have focused heavily on how different brain regions function in people with psychopathic traits, less is known about how these regions are structurally connected. Structural connectivity refers to the physical links between brain areas—similar to the brain’s wiring system. The researchers aimed to go beyond earlier work that focused only on specific brain circuits and instead look across the entire brain to identify any structural patterns linked to psychopathy.

The researchers were especially interested in understanding whether structural differences in the brain might explain the relationship between psychopathic traits and externalizing behaviors. Previous models have suggested two possible brain-based explanations for these behaviors. One theory emphasizes problems in how people process emotional threats, while another highlights difficulties in attention control. Both theories have some support, but no study had comprehensively examined how structural brain networks might connect psychopathy with real-world behavioral problems.

The research team analyzed data from 82 young adults who participated in the Leipzig Mind-Brain-Body study. All participants were screened to rule out medical or psychological conditions that might affect the results. Psychopathic traits were assessed using a questionnaire designed to capture both interpersonal-affective characteristics (like manipulation and lack of empathy) and behavioral traits (like impulsivity and rule-breaking). Externalizing behaviors were also measured with a separate questionnaire that included items on aggression, defiance, and similar tendencies.

Each participant underwent high-resolution brain imaging using diffusion MRI, a technique that maps the white matter tracts—essentially the brain’s wiring—connecting different regions. The researchers used a method called connectome-based predictive modeling, which relies on machine learning to identify patterns in the brain’s structural connectivity that relate to individual differences in behavior.

This method allowed them to identify two kinds of networks: positive networks, where stronger connections were linked to higher psychopathy scores, and negative networks, where weaker connections were related to those same scores. They also tested whether specific connections within these networks helped explain the relationship between psychopathic traits and externalizing behaviors.

The results showed that psychopathic traits were significantly associated with both stronger and weaker connections in different parts of the brain. The positive network—made up of connections that increased with psychopathy—was better at predicting psychopathic traits than the negative network alone. But when both networks were combined, the prediction became even more accurate.

Many of the connections in the positive network were located within the brain’s frontal and parietal lobes, which are involved in decision-making, emotional processing, and attention. These connections included pathways like the uncinate fasciculus, which links the frontal cortex with areas involved in emotion, and the arcuate fasciculus, which supports language and auditory processing. Other connections involved the cingulum bundle, associated with emotional regulation and social behavior, and the posterior corticostriatal pathway, which plays a role in reward processing and learning.