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Abdominal obesity and muscle loss increase the risk of death by 83% after age 50, study finds

A study by researchers at the Federal University of São Carlos (UFSCar) in Brazil, in partnership with University College London (UCL) in the United Kingdom, concluded that the combination of abdominal fat and muscle loss increases the risk of death by 83%, compared to people without these conditions.

This combination is so dangerous that it identifies an even greater problem: sarcopenic obesity. This condition is characterized by loss of muscle mass while gaining fat throughout the body. It is a difficult condition to diagnose, and it is related to loss of autonomy and a worsening quality of life in older adults. It is also known as frailty syndrome and is associated with an increased risk of falls and other comorbidities.

“In addition to assessing the risk of death associated with abdominal obesity and low muscle mass, we were able to prove that simple methods can be used to detect sarcopenic obesity. This is important because the lack of consensus on diagnostic criteria for this disease makes it difficult to detect and treat,” says Tiago da Silva Alexandre, a professor in the Department of Gerontology at UFSCar and one of the authors of the study.

Study Sheds New Light on Tanning Bed Melanoma Risks

Frequent tanning bed users may have up to an eight times greater risk for melanoma than people considered at high risk for melanoma who don’t use tanning beds, according to a new study that also showed how tanning beds alter melanoma-linked DNA on the molecular level and damage areas of skin not usually exposed to the sun.


A case-control cohort study of patients considered at high risk for melanoma finds that tanning bed users have higher rates of melanoma and disease with significantly more mutations.

Vascular Lamb1 guides the migration of retinal microglial precursors via Itga6-Rac1 signaling

Zhan et al. show that blood vessels serve as a migratory scaffold directing retinal microglial precursor infiltration into the developing retina. Vascular Lamb1 signaling orchestrates this precise migration, indicating a key mechanism guiding retinal microglial colonization during development.

Lipofuscin accumulation in aging and neurodegeneration: a potential “timebomb” overlooked in Alzheimer’s disease

Lipofuscin, a marker of aging, is the accumulation of autofluorescent granules within microglia and postmitotic cells such as neurons. Lipofuscin has traditionally been regarded as an inert byproduct of cellular degradation. However, recent findings suggest that lipofuscin may play a role in modulating age-related neurodegenerative processes, and several questions remain unanswered. For instance, why do lipofuscin granules accumulate preferentially in aged neurons and microglia? What happens to these pigments upon neuronal demise? Particularly in neurodegenerative diseases like Alzheimer’s disease (AD), why does amyloid β (Aβ) deposition usually begin in late adulthood or during aging? Why do lipofuscin and amyloid plaques appear preferentially in grey matter and rarely in white matter? In this review, we argue that lipofuscin should be revisited not as a simple biomarker of aging, but as a potential modulator of neurodegenerative diseases. We synthesize emerging evidence linking lipofuscin to lysosomal dysfunction, oxidative stress, lipid peroxidation and disease onset—mechanisms critically implicated in neurodegeneration. We also explore the potential interactions of lipofuscin with Aβ and their spatial location, and summarize evidence showing that lipofuscin may influence disease progression via feedback loops affecting cellular clearance and inflammation. Finally, we propose future research directions toward better understanding of the mechanisms of lipofuscin accumulation and improved lysosomal waste clearance in aging.

A new vulnerability of asthma immune cells discovered

Why do certain immune cells remain permanently active in allergic asthma – even in an environment that should actually damage them? A research team has discovered that these cells only survive because they activate a special antioxidant protection mechanism. When this mechanism is blocked, allergic inflammation in mouse models decreases significantly. The results have now been published in the scientific journal Immunity.

In allergic asthma, so-called ILC2 and Th2 cells are key drivers of inflammation. They produce messenger substances that increase mucus formation and the influx of immune cells. At the same time, the inflamed lung tissue is rich in free fatty acids and oxidative molecules — conditions that normally endanger cells.

The study shows that pathogenic ILC2s absorb large amounts of these fats and incorporate them into their membranes. In order to avoid dying from ferroptosis, an iron-dependent form of cell death caused by oxidized lipids, they activate their antioxidant systems. The enzymes GPX4 and TXNRD1 play a central role in this process. They neutralize harmful lipid peroxides and enable the cells to survive and multiply despite the stressful environment.

To test this approach, the Bonn team inhibited the thioredoxin metabolic pathway using a drug that blocks the enzyme TXNRD1. In mouse models, this led to significantly less ILC2 accumulating in the lungs. As a result, both the production of the typical type 2 cytokines IL-5 and IL-13 and the number of eosinophils and mucus production decreased. Overall, the allergic reaction was significantly less severe.

Forecasting Spoken Language Development in Children With Cochlear Implants Using Preimplant Magnetic Resonance Imaging

Deep transfer learning using presurgical brain MRI features predicted post–cochlear implant language improvement in children with 92% accuracy, outperforming traditional ML.


Importance Cochlear implants substantially improve spoken language in children with severe to profound sensorineural hearing loss, yet outcomes remain more variable than in children with healthy hearing. This variability cannot be reliably predicted for individual children using age at implant or residual hearing. Development of an artificial intelligence clinical tool to predict which patients will exhibit poorer improvements in language skills may enable an individualized approach to improve language outcomes.

Objective To compare the accuracy of traditional machine learning (ML) with deep transfer learning (DTL) algorithms to predict post–cochlear implant spoken language development in children with bilateral sensorineural hearing loss using a binary classification model of high vs low language improvers.

Design, Setting, and Participants This multicenter diagnostic study enrolled children from English-, Spanish-, and Cantonese-speaking families across 3 independent clinical centers in the US, Australia, and Hong Kong. A total of 278 children with cochlear implants were enrolled from July 2009 to March 2022 with 1 to 3 years of post–cochlear implant outcomes data. All children underwent pre–cochlear implant 3-dimensional volumetric brain magnetic resonance imaging (MRI). ML and DTL algorithms were trained to predict high vs low language improvers in children with cochlear implants using neuroanatomical features from presurgical brain MRI. Data were analyzed from August 2023 to April 2025.

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