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Category: biotech/medical – Page 379

From social to biological networks: New algorithm uncovers key proteins in human disease

Researchers at Ben-Gurion University of the Negev have developed a machine-learning algorithm that could enhance our understanding of human biology and disease. The new method, Weighted Graph Anomalous Node Detection (WGAND), takes inspiration from social network analysis and is designed to identify proteins with significant roles in various human tissues.

Proteins are essential molecules in our bodies, and they interact with each other in , known as (PPI) networks. Studying these networks helps scientists understand how proteins function and how they contribute to health and disease.

Prof. Esti Yeger-Lotem, Dr. Michael Fire, Dr. Jubran Juman, and Dr. Dima Kagan developed the algorithm to analyze these PPI networks to detect “anomalous” proteins—those that stand out due to their unique pattern of weighted interactions. This implies that the amount of the protein and its protein interactors is greater in that particular network, allowing them to carry out more functions and drive more processes. This also indicates the great importance that these proteins have in a particular network, because the body will not waste energy on their production for no reason.

Human ‘mini-brains’ reveal protein GRAMD1B’s potential role in neurodegeneration

Researchers at The Ohio State University Wexner Medical Center and College of Medicine have discovered a new way that neurons act in neurodegeneration by using human neural organoids—also known as “mini-brain” models—from patients with frontotemporal lobar degeneration (FTLD).

Understanding this new pathway could help researchers find better treatments for FTLD and Alzheimer’s, the two most common forms of dementia that lead to .

Researchers used advanced techniques to study from patients and mice, including growing human neural organoids (mini-brains) that can feature several cell types found in the brain.

Challenging Decades of Neuroscience: Brain Cells Are More Plastic Than Previously Thought

Neurons are specialized brain cells responsible for transmitting signals throughout the body. For a long time, scientists believed that once neurons develop from stem cells into a specific subtype, their identity remains fixed, regardless of changes in their surrounding environment.

However, new research from the Braingeneers, a collaborative team of scientists from UC Santa Cruz and UC San Francisco, challenges this long-held belief.

In a study published in iScience, the Braingeneers report that neuronal subtype identity may be more flexible than previously thought. The team used cerebral organoids, 3D models of brain tissue, to investigate how neurons develop and adapt. Their findings offer new insights into how different neuron subtypes influence brain function and may play a role in neurodevelopmental disorders.

Study uncovers a brain circuit linked to the intensity of political behavior

People diagnosed with various mental health disorders can sometimes start engaging in intense political behavior, such as violent protests, civil disobedience and the aggressive expression of political views. So far, however, the link between political behavior and the brain has been rarely explored, as it was not viewed as central to the understanding of mental health disorders.

Researchers at Harvard Medical School, Stanford University School of Medicine and Northwestern University Feinberg School of Medicine recently carried out a study investigating the neural underpinnings of political behavior. Their findings, published in Brain, unveil the existence of a brain circuit that is associated with the intensity of people’s political involvement, irrespective of their ideology or party affiliation.

“This paper started out as a collaborative effort that focused on learning how to help people better come together and thrive, alongside Stephanie Balters at Stanford,” Shan H. Siddiqi, first author of the paper, told Phys.org.

Survival prediction with radiomics for patients with IDH mutated lower-grade glioma

Purpose Adult patients with diffuse lower-grade gliomas (dLGG) show heterogeneous survival outcomes, complicating postoperative treatment planning. Treating all patients early increases the risk of long-term side effects, while delayed treatment may lead to impaired survival. Refinement of prognostic models could optimize timing of treatment. Conventional radiological features are prognostic in dLGG, but MRI could carry more prognostic information. This study aimed to investigate MRI-based radiomics survival models and compare them with clinical models. Methods Two clinical survival models were created: a preoperative model (tumor volume) and a full clinical model (tumor volume, extent of resection, tumor subtype). Radiomics features were extracted from preoperative MRI. The dataset was divided into training set and unseen test set (70:30). Model performance was evaluated on test set with Uno’s concordance index (c-index). Risk groups were created by the best performing model’s predictions. Results 207 patients with mutated IDH (mIDH) dLGG were included. The preoperative clinical, full clinical and radiomics models showed c-indexes of 0.70, 0.71 and 0.75 respectively on test set for overall survival. The radiomics model included four features of tumor diameter and tumor heterogeneity. The combined full clinical and radiomics model showed best performance with c-index = 0.79. The survival difference between high- and low-risk patients according to the combined model was both statistically significant and clinically relevant. Conclusion Radiomics can capture quantitative prognostic information in patients with dLGG. Combined models show promise of synergetic effects and should be studied further in astrocytoma and oligodendroglioma patients separately for optimal modelling of individual risks.

Macrophages harness hepatocyte glutamate to boost liver regeneration

Liver regeneration after hepatectomy follows accurate coordination with the body’s specific requirements1–3. However, the molecular mechanisms, factors and particular hepatocyte population influencing its efficiency remain unclear. Here we report on a unique regeneration mechanism involving unconventional RPB5 prefoldin interactor 1 (URI1), which exclusively colocalizes with, binds to and activates glutamine synthase (GS) in pericentral hepatocytes. Genetic GS or URI1 depletion in mouse pericentral hepatocytes increases circulating glutamate levels, accelerating liver regeneration after two-third hepatectomy. Conversely, mouse hepatocytic URI1 overexpression hinders liver restoration, which can be reversed by elevating glutamate through supplementation or genetic GS depletion. Glutamate metabolically reprograms bone-marrow-derived macrophages, stabilizing HIF1α, which transcriptionally activates WNT3 to promote YAP1-dependent hepatocyte proliferation, boosting liver regeneration. GS regulation by URI1 is a mechanism that maintains optimal glutamate levels, probably to spatiotemporally fine-tune liver growth in accordance with the body’s homeostasis and nutrient supply. Accordingly, in acute and chronic injury models, including in cirrhotic mice with low glutamate levels and in early mortality after liver resection, as well as in mice undergoing 90% hepatectomy, glutamate addition enhances hepatocyte proliferation and survival. Furthermore, URI1 and GS expression co-localize in human hepatocytes and correlate with WNT3 in immune cells across liver disease stages. Glutamate supplementation may therefore support liver regeneration, benefiting patients awaiting transplants or recovering from hepatectomy.

© 2025. The Author(s), under exclusive licence to Springer Nature Limited.

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Your season of conception could influence how your body stores fat

Individuals who were conceived in colder seasons are more likely to show higher brown adipose tissue activity, increased energy expenditure and a lower body mass index (BMI), and lower fat accumulation around internal organs, compared with those conceived in warmer seasons, suggests a study published in Nature Metabolism. The findings, based on an analysis involving more than 500 participants, indicate a potential role for meteorological conditions influencing human physiology.

Although eating habits and exercise are key indicators of fat loss, exposure to cold and warmth also plays a part. In colder temperatures, the body generates more heat (cold-induced thermogenesis) via brown adipose tissue activity and stores less fat in the form of white adipose tissue than it does in hotter temperatures. However, underlying factors contributing to in brown adipose tissue activity remain poorly understood, particularly in humans.

Takeshi Yoneshiro and colleagues analyzed brown adipose tissue density, activity and thermogenesis in 683 healthy male and female individuals between ages 3 and 78 in Japan, whose parents were exposed to (defined in the study as between 17 October and 15 April) or warm temperatures (between 16 April and 16 October) during the fertilization and birth periods.

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