The lab will beam back data to train AI models to predict how proteins behind age-related diseases like Alzheimer's and certain cancers behave. Space is becoming the next frontier in longevity research. A British startup just launched self-run chemical experiments into orbit, in the hopes zero-gravity data might shine a light on a group of disease-causing proteins too difficult to study on Earth. But first they need to check their autonomous laboratory will work in space. Mass Balance's grapefruit-sized apparatus containing chemicals, sensors and control elements to keep the chemicals functioning launched on a SpaceX transporter on Tuesday morning.

How healthy is your brain? In our efforts to keep our brains healthy, how do we know what is working? It shouldn't have been difficult: 72 x 72. From the back seat, my daughter, newly confident in mental maths, wanted to check her answer. Whether it was because it was the end of the day, I was trying to park or something else, I stalled, cognitively speaking. Lately, though, I have had the sense that my brain isn't firing on all cylinders.

A large-scale clinical trial has shown that even long-term consumption of DHA--an omega-3 fatty acid found in abundance in oily fish--may not lead to improvements in cognitive function. Docosahexaenoic acid (DHA), an omega-3 fatty acid found in abundance in oily fish such as mackerel and sardines, is thought to improve cognitive function by supporting connections between brain cells. However, it has never been conclusively demonstrated that DHA taken as a dietary supplement actually reaches the brain or provides measurable benefits against dementia . Against this backdrop, a research team at the USC School of Medicine has published the results of a large, two-year clinical trial involving older adults at elevated risk of developing Alzheimer's disease . The study found that while high-dose DHA supplements do indeed reach the brain, they did not improve memory or cognitive function, nor did they slow brain atrophy.

How some people's brains make an extraordinary recovery from stroke A well-known actor who had experienced a stroke was treated by stroke specialist Sandor Nardai. The actor had been left with aphasia, or an impaired ability to speak - brutal for anyone, but "probably the most devastating thing that could happen to an actor", says Nardai. After three months of recovery, though, the actor was able to say some words. After a year, he voiced a commercial. Remarkably, he eventually got well enough to return to live theatre, says Nardai, who is at Semmelweis University in Hungary.

Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi-view contrastive framework that integrates external normal samples and enhances feature learning through adaptive contrastive strategies. DAAC consists of two key modules: (1) a Discrepancy Estimator, built upon a GAN-enhanced encoder-decoder architecture, captures the distribution of normal data and computes reconstruction errors as indicators of abnormality. These discrepancy features augment the target dataset to mitigate overfitting.

BrainODElearns a deformation space over anatomically meaningful brain regions to facilitate early prediction of neurodegenerative disease progression. Addressing inherent challenges of longitudinal neuroimaging data--such as limited sample sizes, irregular temporal sampling, and substantial inter-subject variability--we propose a conditional neural ODE architecture that models shape dynamics with subject-specific age and cognitive status. To enable autoregressive forecasting of brain morphology from a single observation, we propose a pseudo-cognitive status embedding that allows progressive shape prediction across intermediate time points with predicted cognitive decline. Experiments show that BrainODE outperforms time-aware baselines in predicting future brain shapes, demonstrating strong generalization across longitudinal datasets with both regular and irregular time intervals.

Alzheimer's disease (AD) is marked by cognitive decline along with the widespread of tau aggregates across the brain cortex. Due to the challenges of imaging pathology spreading flows in vivo, however, quantitative analysis on the cortical pathways of tau propagation and its interaction with the cascade of amyloid-beta (Aβ) plaques lags behind the experimental insights of underlying pathophysiological mechanisms. To address this challenge, we present a physics-informed neural network, empowered by mean-field theory, to uncover the biologically meaningful spreading pathways of tau aggregates between two longitudinal snapshots. Following the notion of'prion-like' mechanism in AD, we first formulate the dynamics of tau propagation as a mean-field game (MFG), where the spread of tau aggregate at each location (aka.

Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehensive joint diagnosis, yet practical clinical scenarios frequently encounter incomplete modalities due to factors like high acquisition costs or radiation risks.

Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehensive joint diagnosis, yet practical clinical scenarios frequently encounter incomplete modalities due to factors like high acquisition costs or radiation risks.

Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomarker histories are irregular and incompletely observed. We develop an anchor-based analysis of 24-month Clinical Dementia Rating Sum of Boxes (CDR-SB) change using harmonized Alzheimer's Disease Neuroimaging Initiative (ADNI) tables. Each labeled sample is anchored at a mild cognitive impairment visit, uses only clinical and biomarker history observed at or before that anchor, and defines the response as CDR-SB at the future visit closest to 24 months within an 18--30 month window minus anchor CDR-SB. The analytic cohort contains 2,600 labeled anchors from 858 participants and 7,276 longitudinal rows. We propose a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning from pre-anchor clinical and biomarker histories. The model uses participant-level random intercepts in the mixed-effects reference, observation-level triplet tokenization for irregular histories, and a learned nonnegative time-gap penalty inside self-attention. We compare the proposed model with a Bayesian-information-criterion-selected linear mixed-effects baseline, GRU-D, and STraTS under repeated participant-level train--test splits. Across five participant-level random seeds, the proposed model achieves the best mean test performance across all reported metrics, reducing MSE by 13.1% and increasing prediction--observation correlation by 26.4% relative to the mixed-effects baseline. It also improves over both GRU-D and STraTS in mean error and correlation. These results show that statistical anchoring and gap-aware residual learning provide a useful structure for medium-horizon Alzheimer's disease progression prediction.