Artificial intelligence chatbots have achieved unprecedented adoption, with millions now using these systems for emotional support and companionship in contexts of widespread social isolation and capacity-constrained mental health services. While some users report psychological benefits, concerning edge cases are emerging, including reports of suicide, violence, and delusional thinking linked to emotional relationships with chatbots. To understand these risks we need to consider the interaction between human cognitive-emotional biases and chatbot behavioural tendencies, the latter including companionship-reinforcing behaviours such as sycophancy, role-play and anthropomimesis. Individuals with preexisting mental health conditions may face increased risks of chatbot-induced changes in beliefs and behaviour, particularly where these conditions manifest in altered belief-updating, reality-testing, and social isolation. To address this emerging public health concern, we need coordinated action across clinical practice, AI development, and regulatory frameworks.

United States (US) Veterans are significantly more likely to die by suicide than civilians. Machine learning (ML) models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce Veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most Veteran suicides, and firearm-specific preventions exist. We used data from US Army Veterans from 2010-2019 (N = 800,579) to develop and compare firearm-specific ML models with a method-agnostic model to predict firearm suicides among transitioning Army Veterans up to 10 years after discharge. The models performed comparably overall (AU-ROC=0.710-0.708; ICI=0.0003-0.0005% for firearm-specific and method-agnostic models, respectively), with the best model depending on the intervention threshold. Results from the current study show the method-agnostic model was better at predicting firearm suicides at the highest intervention threshold, whereas the firearm-specific model was better at lower thresholds. When considering fairness with respect to sex and race/ethnicity, the firearm-specific model was best across all thresholds. Thus, model choice depends on weighing numerous factors and optimal thresholds might differ for coordinated firearm-specific and method-agnostic interventions.

Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here, we combine source space connectivity (EEG) with generative brain modeling in healthy controls (HCs) from the global south and north, alongside Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patients (N=1,399). BAGs in aging were influenced by geography (south>north), income (low>high), sex (female>male), and education (low>high), with larger BAGs in patients, especially females with AD. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations.

Axon regeneration is limited in the mammalian central nervous system1. Neurons must balance stress responses with regenerative demands after axonal injury2, but the mechanisms remain unclear. Here we identify aryl hydrocarbon receptor (AhR), a ligand-activated basic helix-loop-helix/PER-ARNT-SIM (bHLH-PAS) transcription factor, as a key regulator of this stress-growth switch. We show that ligand-mediated AhR signalling restrains axon growth, whereas neuronal deletion or pharmacological inhibition of AhR promotes axonal regeneration and functional recovery in both peripheral nerve and spinal cord injury models. Mechanistic studies reveal that axotomy-induced AhR activation in dorsal root ganglion neurons enforces proteostasis and stress-response programs to preserve tissue integrity. By contrast, AhR ablation redirects the neuronal response towards elevated de novo translation and pro-growth signalling, enabling axon regeneration. This growth-promoting effect requires HIF1α, with shared transcriptional targets enriched for metabolic and regenerative pathways. Single-cell and epigenomic analyses further revealed that the AhR regulon engages the integrated stress response and DNA hydroxymethylation to rewire neuronal injury-response programs. Together, our findings establish AhR as a neuronal brake on axon regeneration, integrating environmental sensing, protein homeostasis and metabolic signalling to control the balance between stress adaptation and axonal repair.

The discovery of high-temperature superconductivity in bulk La3Ni2O7 under high hydrostatic pressure1-4 and biaxial compression in epitaxial thin films5-8 has ignited significant interest in understanding the interplay between atomic and electronic structure in these compounds. Subtle changes in the nickel-oxygen bonding environment are thought to be key drivers for stabilizing superconductivity, but specific details of which bonds and which modifications are most relevant remains so far unresolved. While direct, atomic-scale structural characterization under hydrostatic pressure is beyond current experimental capabilities, static stabilization of strained La3Ni2O7 films provides a platform well-suited to investigation with new picometer-resolution electron microscopy methods. Here, we use multislice electron ptychography (MEP)9,10 to directly measure the atomic-scale structural evolution of La3Ni2O7 thin films across a wide range of biaxial strains tuned via substrate choice. By resolving both the cation and oxygen sublattices, we study the strain-dependent evolution of atomic bonds, providing the opportunity to isolate and disentangle the effects of specific structural motifs for stabilizing superconductivity. We identify the lifting of crystalline symmetry through modification of the nickel-oxygen octahedral distortions under compressive strain as a key structural ingredient for superconductivity and identify in-plane lattice compression as a common attribute between bulk and thin film superconductivity. Building upon the detailed structures obtained by MEP, we introduce a theoretical framework to disentangle coupled structural distortions in corner-sharing octahedra11, which suggest that both known superconducting geometries of La3Ni2O7 (hydrostatic pressure and compressive strain) suppress local t2g orbital mixing in the low-energy Ni bands by raising the octahedral symmetry.