AI News Archive: June 25, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Text Splitter
Chop long text into clean, model-sized chunks
- Horsey AI
The AI Co-Founder That Runs Your Business on WhatsApp
- East Blue Gym
Full-stack gym SaaS with AI coach, live chat & payments
- Zylio
AI Procurement Optimization & Invoice Compliance Platform
- Warmspace AI Companion
AI Companion
- Siteglade
AI Website Builder |Specialized in Design, 3D & Landing page
- Accenda
The AI-powered learning OS for personalized pathways
- AskBase
Build your enterprise AI assistant in minutes, not months
- JupiterAstro-AI
Know Your Life
- Free Tool for Dream Interpretation
AI-powered dream interpretation across spiritual traditions
- Smart Booking System AI
AI assistant automating orders & bookings with precision
- ChatFlow by Novynix
AI WhatsApp automation that replies & books 24/7
- AI Scheduler
Schedule Claude AI sessions like cron jobs
- ScalpAnalysis AI
An honest hair & scalp baseline from 4 phone photos
- Animegenius Hub
AI Manga Translator — AnimeGenius Hub
- SHIVA
Guardrails for AI agents — proof, not promises
- Timestamped
AI alpha from podcasts.
- AgentsCoin
The first coin AI agents mine themselves
- XeanVI
Automated algorithmic trading powered by AI
- SkinAI AI-Powered Skin Scanner & Tracker
Scan 62+ skin conditions & track changes over time
- AI website & app builder | Hostinger
Launch your idea today
- OpenToon
Read every manhwa in your language.
- VivifyAll | Image Generator
27 AI models, one creative workflow.
- Scientific discovery as meta-optimization: a combinatorial optimization case study
Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. Large language models (LLMs) have enabled automated exploration of this space, but we argue that simultaneo...
- Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks
This work studies resilient output containment for heterogeneous linear multi-agent systems with actuator cyber-attacks over directed network topologies. The leaders generate bounded locally absolutely continuous trajectories; however, their dynamics, velocity bounds, and motion envelopes are undisc...
- Decomposition of task-specific responses in the multiple demand network
Neuroimaging evidence suggests that a distributed network of brain areas, known as the multiple demand (MD) network, is consistently recruited across a wide range of cognitive tasks. The MD network includes specific areas in prefrontal and parietal cortices. However, the fine-grained organization of this network is poorly understood. Here we aim to comprehensively characterize the functional subdivisions within the MD network using a naturalistic fMRI paradigm. 20 subjects were instructed to perform 14 different tasks on a set of movie stimuli. These tasks were designed to target a wide range of cognitive domains including visual, spatial, categorical, emotional, auditory, linguistic, social, and semantic processes. fMRI data were also collected while subjects passively watched the movies. The MD network was first delineated by localizing cortical areas that were significantly more active in all 14 tasks compared to the passive-viewing condition. The principal component analysis was then applied on task-specific responses of cortical points within the MD network. The first component was correlated with activities for all tasks, and its spatial map revealed the core, highly multimodal subregions of the MD network. The other components showed preferences for a subset of tasks. In particular, the second component revealed a sharp distinction between MD regions that were preferentially active in visual/spatial versus linguistic/semantic tasks. A graph analysis on the entire cortex also showed a large-scale distinction between visual/spatial and linguistic/semantic areas, with MD regions linking the two communities of cortical areas. Overall, our results provide new insights into how the MD network and its fine-grained architecture contribute to the human intelligent behavior.
- An extraglomerular relay circuit for multimodal integration within the Drosophila antennal lobe
The central integration of sensory information within and between brain hemispheres is critical for efficient behavioral responses, but at which level of information processing multimodal integration occurs is poorly understood. In olfactory systems an array of receptor-specific synaptic glomeruli with corresponding projection neurons (PNs) form separate sensory channels within each hemisphere, which run in parallel with other sensory modalities to converge at higher brain regions. We recently identified a small cluster of commissural pioneer neurons (cPINs) in the Drosophila olfactory system, which controls the formation of bilateral sensory circuits to support interhemispheric integration at the first synaptic layer. Here we show that cPINs also mediate the integration of sensory channels by relaying class-specific input within the antennal lobe. Functional studies showed that medial cPINs converge olfactory amine/ammonia input with a class of non-olfactory PNs to trigger attraction. During olfactory circuit formation, growing cPINs specify separate dendritic input/output domains, which merge into distinct glomeruli of different sensory modalities. Mutant analysis of the Wnt5 pathway revealed that cPINs display an initial PN-related growth pattern, which becomes redirected to organize lateral relay between sensory channels. These results identified a small cluster of olfactory interneurons as a central coordinator for fast convergence of sensory information, providing a mechanistic model of neural circuit evolution.
- The Human Pancreas Cell Atlas defines a healthy reference framework for disease contextualization and translational benchmarking
A central challenge in single-cell biology is distinguishing disease-associated remodeling from normal cellular heterogeneity. Addressing this challenge requires healthy reference frameworks that capture cellular diversity across individuals, technologies, and biological contexts. Here we present the Human Pancreas Cell Atlas (HPCA), a reference atlas of the healthy human pancreas integrating 815,126 single-cell and single-nucleus transcriptomes from 109 donors across 12 studies, diverse technologies, and demographics. Using benchmarked integration and community-driven annotations, HPCA defines 94 cell types and transcriptional states spanning endocrine, exocrine, immune, and stromal compartments. The atlas identifies rare endocrine populations, including a putative, spatially supported polyhormonal alpha-beta-delta state, and provides a unified framework for interpreting pancreatic cellular variation across diverse biological and demographic covariates. Projection of disease and model-system datasets onto HPCA contextualized endocrine and epithelial remodeling relative to healthy pancreatic states. Diabetes-associated endocrine cells remained embedded within the healthy endocrine state space while exhibiting disease-specific changes, as supported by spatial and eQTL concordance analyses. Integration with a pancreatic ductal adenocarcinoma atlas resolved injury-associated and malignant epithelial ecosystem regions across donors. Finally, the HPCA enables quantitative benchmarking of murine diabetes models and stem-cell-derived islets against human pancreatic reference states. Together, the HPCA establishes a healthy transcriptional coordinate system for interpreting disease-associated pathophysiology , experimental perturbation, and regenerative fidelity, illustrating how reference atlases can function as analytical frameworks rather than static cell catalogs.
- Bamsnap-LRS: an automated batch visualization tool for long-read sequencing alignments
Summary: Long-read sequencing (LRS) has become essential for genome assembly, structural variations (SVs) detection, haplotype phasing and transcript isoform characterization. However, these applications often require manual inspection of read alignment for validation. Existing visualization tools are either interactive genome browsers that are difficult to scale to large datasets or batch-oriented tools that are not optimized for the unique alignment patterns of long-read data. We developed Bamsnap-LRS, an automated command-line tool for high-throughput LRS alignment visualization. It supports long-read-specific features, phased SNP inspection, and publication-ready batch figure generation within a unified framework for genomic, transcriptomic, and haplotype-aware analyses. Availability and Implementation: All codes and examples are freely available at https://github.com/comery/Bamsnap-LRS.
- Spatial Transcriptomic Profiling Reveals Microenvironment-Dependent Immune Signatures in a Lyme Arthritis model
Lyme arthritis, a manifestation of Lyme disease, is triggered by the spirochetal bacterium Borrelia burgdorferi (Bb), which is transmitted through the bite of the Ixodes tick. Although multiple studies have been conducted on the complex host immune response in Lyme arthritis, the spatial gene expression environment in the joint tissue remains unexplored. Here, we applied spatial transcriptomics to ankle joints of C3H mice infected with Bb, profiling tissues at peak inflammation (two weeks post infection) and after antibiotics (four weeks post-infection) during inflammation resolution. Analysis revealed spatially restricted signatures: pro-inflammatory responses dominated synovial and fibroblast populations two weeks post-infection, with elevated levels of Vimentin and I-Ek gene - and Vimentin protein - expression localized to these regions. By four weeks post-infection during the inflammation resolution phase, levels of Vimentin and I-Ek related gene and protein expression were reduced. Further, we noted an increase in the CD54+ and CD106+ double-positive population in infected mice joints compared to the vehicle treated controls. Notably, fibroblasts and synoviocytes in the medial joint regions adopted immune-like phenotypes during peak inflammation, while the same cell types in the exterior humeroradial joint displayed a more infection-resilient phenotype. These spatially resolved maps demonstrate that joint microenvironments play a crucial role in pathogenesis, offering unique insights into Lyme arthritis pathology.
- Isolation and characterisation of novel fruit bat alphaherpesvirus from Rousettus aegyptiacus bats in Coastal Kenya
Background: Herpesviruses are a group of double-stranded DNA viruses known to infect a wide range of vertebrates and establish life-long latent infections. While bats serve as natural reservoir hosts for numerous viral families, relatively few bat herpesviruses have been successfully isolated. In this study, we report the isolation and characterization of two novel alphaherpesvirus strains obtained from Rousettus aegyptiacus bats in Coastal Kenya. Methods: The samples of oral and rectal swabs were collected from three different species of bats from coastal Kenya between October 2024 and April 2025; the bat species collected include Hipposideros spp., Coleura afra, and Rousettus aegyptiacus. Virus isolation was performed by inoculation of samples in Vero E6 cells and subsequent monitoring for cytopathic effects (CPE). Total nucleic acids were extracted from CPE positive cultures and subjected to library preparation to enable unbiased detection of both RNA and DNA viruses. The libraries were sequenced using next-generation sequencing with Illumina MiSeq platform. Subsequently, bioinformatic analysis was carried out to identify the virus, generate consensus genomes as well as phylogenetic analysis to determine the placement of identified viruses. Results: Two samples from R. aegyptiacus (KIK_460_O and KIK_465_O) induced typical CPE within five days. Sequencing and assembly yielded partial consensus sequences of approximately 60 kb (KIK_460_O) and 70 kb (KIK_465_O), representing extended genomic data for a bat-associated alphaherpesvirus. This virus has a genome of about 140kb, indicating that our partial assemblies account for about 43-50% of the total genome. Both isolates were found to be closely related to Dzifa herpesvirus, an alphaherpesvirus previously identified in Kilifi, Kenya. Alphaherpesvirus was identified based on partial sequencing of UL19 (3,787 bp) and UL30 (2,846 bp) genes. The two isolates were found to be identical at the UL19 gene, showing that they belonged to the same virus strain. Phylogenetic analysis showed that the novel alphaherpesvirus belongs to primate alphaherpesviruses under the subfamily Alphaherpesvirinae. Conclusion: This study reports the isolation and genomic characterization of a novel fruit bat alphaherpesvirus from Kenyan Rousettus aegyptiacus bats. The partial genome assembly (60-70 kb) represent the first extended genomic data for this virus, covering approximately 43-50% of the estimated 140 kb complete genome. The phylogenetic placement of this alphaherpesvirus near primate viruses, especially Pteropodid alphaherpesvirus 1, suggests bat-association and needs further investigation into its zoonotic potential.
- Development of GS-441524 Derivatives as Potent SARS-CoV-2 Mac1 Inhibitors via a Direct-to-Biology Approach
Targeting viral macrodomains (Mac) has emerged as a promising strategy for antiviral drug development, especially after the outbreak of COVID-19 that claimed millions of lives worldwide. Several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Mac1 inhibitors have been reported in the past few years. In the present work, we converted GS-441524 (IC50 of ~10 M for SARS-CoV-2 Mac1) to KP-S54 (18c), a potent inhibitor of both SARS-CoV-2 Mac1 (IC50: 44 nM) and Middle East respiratory syndrome coronavirus (MERS-CoV) Mac1 (IC50: 91 nM) through an iterative direct-to-biology approach. This approach leverages efficient amide-coupling reaction and the mix-and-read fluorescence polarization (FP) assays where reaction mixtures could be screened directly without purification. Cocrystal structure of a selected derivative (12p) binding to SARS-CoV-2 Mac1 revealed the binding mode, which will guide future drug development against viral macrodomains.
- Agnostic material classification using differential de Bruijn graphs of DNA imprints
The wide variety of physical and chemical properties in materials makes the study of unknown substances challenging. We have previously proposed a theoretical framework for agnostic material characterization based on using nucleic acid imprints of the materials and then analyzing material-specific patterns of derived sequences. Here we demonstrate an experimental and computational pipeline that can agnostically identify and distinguish varied materials based on DNA k-mer imprints and validate the ability of these imprints to distinguish closely related materials. This work lays the foundation for expansion of purely agnostic sensing technologies for the unbiased characterization and categorization of a much wider variety of biotic and abiotic materials.
- Specific killing of Ewing sarcoma by TCR-T cells targeting public neogene-encoded antigens
EWSR1::FLI1, the oncogenic chimeric transcription factor driving Ewing sarcoma (EwS) induces expression of exquisitely EwS-specific neogenes (Ew_NGs) through neomorphic binding and transcription activation at GGAA microsatellites in genomic regions that are silent in normal tissues. We show that peptides encoded by Ew_NGs are presented on HLA-I complexes on EwS cells. The cytokine secretion of CD8+ T cells specific for Ew_NG-encoded HLA-I-bound peptides is activated by all HLA-I-matched EwS cells but not by non-EwS cells. These T cells kill EwS cells in an HLA-I restricted manner. This cytotoxicity is dependent on the expression of EWSR1::FLI1 and of the corresponding Ew_NG. It can be reproduced by transduction of the TCR into donor T cells (TCR-T) which kill EwS cells in vivo. Moreover, we show that neither off target nor allogeneic activation are observed with TCR-T thus paving the way for cell therapy in relapsed/resistant EwS patients for which therapeutic options are very limited.
- Medullary epithelium-free areas in the rat thymus are specialized niches enriched for mature thymocytes and distinct stromal subsets
The thymic medulla provides the microenvironment for negative selection, late thymocyte maturation, and thymocyte egress, and is generally characterized by widespread distribution of medullary thymic epithelial cells (mTECs). In contrast, rat thymic medulla contains medullary epithelium-free areas (mEFAs), but the cellular composition and functional significance of these regions remain unclear. Here, we combined spatial transcriptomics and scRNA-seq, using robust cell-type decomposition (RCTD) to characterize mEFAs in Lewis-strain rat thymus. These analyses revealed that more mature-phenotypes of CD4SP, CD8SP, and regulatory T-cell-lineage thymocytes were preferentially localized in mEFAs, whereas immature SP subsets were enriched in medullary epithelium-containing areas. Newly found rat thymic mesenchymal cell-3 and -4 (TMC3 and TMC4) subsets were also enriched in mEFAs. These subsets were broadly similar to mouse medullary fibroblasts but displayed distinct predicted interactions with SP thymocytes, including costimulatory molecule-receptor, chemokine-receptor, and ECM-integrin axes. In addition, the venous endothelial cells (vECs) expressing portal endothelial cell markers were accumulated in mEFAs. The S1P transporter gene Spns2 was preferentially expressed in both TMC4 and vEC subsets, suggesting increased local concentration in mEFAs. These findings indicate that rat mEFAs are specialized medullary niches linking stromal organization, thymocyte maturation, and thymic egress.
- Btbd11 regulates glutamatergic synapse organization in GABAergic inhibitory interneurons
The mechanisms that underlie glutamatergic synapse organization and function in GABAergic inhibitory interneurons (INs) are not well described, despite evidence that impaired glutamatergic excitation of INs is implicated in psychiatric disorders such as schizophrenia and anxiety. Glutamatergic synapses received by INs have unique basal transmission properties and exhibit distinct synaptic plasticity compared to those received by excitatory neurons, likely due to cell-type specific differences in postsynaptic density (PSD) composition and maintenance mechanisms. In the present study, we show that the interneuron-specific protein Btbd11 regulates excitatory synapse transmission in hippocampal interneurons through promotion of phase separation and support of postsynaptic nanoarchitecture. Btbd11 forms a phase separated protein complex with Psd-95 and TARP{gamma}2 and impacts the stability of TARP{gamma}2 and GluA1 within glutamatergic IN synapses in an expression- and phase separation-dependent manner. Using super resolution imaging, we show that Btbd11 displays nanoscale clustering properties within IN synapses that correlate with Psd-95 nanostructure. Furthermore, genetic deletion of Btbd11 decreases PSD protein expression, reduces synapse size, and disrupts Psd-95 nanocluster organization. These effects manifest as a drastic reduction in glutamatergic synaptic transmission onto INs when Btbd11 is deleted. Together, these data provide insights into a novel cell type-specific synaptic regulatory mechanism in an understudied synapse population.
- Sexually dimorphic behavioural signatures of tau toxicity in adult Drosophila
Tau pathology is central to Alzheimers disease and related tauopathies, yet mechanisms driving neuronal dysfunction and degeneration downstream of pathological changes in tau remain poorly understood. Drosophila melanogaster models provide a genetically tractable system with an intact nervous system and short lifespan that allows investigation of mechanisms of many diseases. However, in Drosophila, developmental expression of human tau frequently causes lethality and developmental phenotypes, limiting the study of neurodegenerative disease processes. Further, sex is rarely considered in Drosophila studies of tau pathology despite clear sex differences being observed in many aspects of human tauopathies. Here, we used an inducible, pan-neuronal GeneSwitch system to express human tau isoforms exclusively in adulthood, enabling the dissection of tau toxicity independent of development. We combined longitudinal behavioural monitoring with lifespan and neurodegeneration analyses, and performed a targeted genetic screen to identify modifiers of tau-induced dysfunction. Adult-onset tau expression produced striking, sexually dimorphic effects on survival and behaviour. Neuronal expression of the human tau isoform with 4 microtubule binding repeats and neither alternatively spliced N-terminal exon (0N4R tau) caused pronounced neurodegeneration and reduced lifespan, which was exacerbated in flies expressing the phospho-mimetic 0N4R-TauE14 variant. Tau expression produced sexually dimorphic effects on survival and behaviour, with females exhibiting a greater reduction in lifespan, while the induction-dependent increase in vacuolar neurodegeneration was broadly comparable between sexes. Behaviourally, tau expression induced elevated daytime inactivity in females, whereas males exhibited hyperactivity, revealing opposing functional outcomes between sexes. A targeted genetic screen further identified modifiers of tau-dependent behavioural impairment. APOE2 expression in glia, syndecan overexpression in neurons, and increased global expression of the chaperone heat shock protein 90 all reduced 0N4R-TauE14-induced behavioural changes. Seventeen candidate perturbations enhanced the TauE14-induced behavioural phenotype, including manipulations of APOE3, CLU, INPP5D/INPP5K, BIN1/Amph, synaptogyrin, LRP1, NPC1, and Hsp90 pathways. Together, these findings establish an adult-onset Drosophila model of tauopathy that uncouples neurotoxicity from development, reveals sex as a major determinant of tau-induced behavioural outcomes in flies, and uncovers genetic modulators of tau-induced dysfunction. This work highlights the importance of incorporating sex as a biological variable and provides a platform for mechanistic and translational studies of tauopathy.
- Dopamine projections to the basolateral amygdala enable reward prediction
Reward predictions are critical to both adaptive learning and decision making. Such predictions are supported by environmental cues that signal the availability and identity of rewarding events. Here we used fiber photometry, cell-type and pathway-specific optogenetic inhibition, Pavlovian cue-reward conditioning, and decision-making tests in male and female rats to reveal that ventral tegmental area dopamine (VTADA) projections to the basolateral amygdala (BLA) support cue-reward predictions. Reward-predictive cues trigger dopamine release in the BLA that encodes the value of the predicted reward. This cue-evoked VTADA[->]BLA activity mediates the ability of cue-reward predictions to bias action selection and adapt cue-response decisions based on the current value of the predicted reward. Cue-evoked VTADA[->]BLA activity also mediates the constraining influence of cue-reward predictions on new learning. Thus, cue-evoked BLA dopamine supports the reward predictions that both enable adaptive decision making and constrain learning.
- Methamphetamine-induced disruption of neuropeptide expression in mice
Psychoactive and psychotoxic drugs are particularly harmful, if their use coincides with critical developmental windows of brain maturation. Methamphetamine is one such stimulant with developmental exposure increasing seizure susceptibility and long-term neuronal maladaptation in children. Nevertheless, the extent at which infant and adult vulnerability to methamphetamine could differ in time-course and severity remains incompletely understood. Here, we developed a method to monitor methamphetamine-induced hyperactivity in infant mice at high temporal resolution, differentiate it from a biphasic response in adults, and link it to activity changes in cortical areas executing goal-directed (escape) behaviors in infant subjects when using Fos expression as a molecular surrogate. Subsequently, we hypothesized that methamphetamine could alter the expression and cellular distribution of inhibitory neuropeptides, which, when co-released with fast neurotransmitters, could protect circuit plasticity by counteracting methamphetamine-induced hyperexcitability. Methamphetamine differentially altered somatostatin, cholecystokinin, and galanin expression in corticolimbic areas. These data suggest that methamphetamine can evoke age-specific neurocircuit modifications, at least in mice.
- Dysfunction of the colliculus-pulvinar pathway in children with developmental dyslexia
While evidence suggests magnocellular deficits in the geniculostriate pathway in adults with dyslexia, neural deficits in the subcortical pathways during childhood remain unclear. Here, we used high-resolution fMRI to investigate subcortical abnormalities in Chinese children with developmental dyslexia. Fast achromatic motion stimuli and slowly drifting chromatic gratings were used to assess magnocellular (M) and parvocellular (P) functions, respectively. Relative to controls, children with dyslexia showed a selective reduction in responses to the M stimulus in the ventromedial pulvinar (vmPul) and the superficial layers of the superior colliculus (SCs), along with significantly reduced SCs-vmPul connectivity. Importantly, while vmPul responses to the M stimulus were positively associated with reading skills in healthy controls, this correlation was absent in children with dyslexia. Unlike previous findings in adults, the lateral geniculate nucleus (LGN) exhibited a non-selective reduction in responses to both stimuli, no volume reduction, and no correlation with reading ability. These findings demonstrate a selective deficit to achromatic motion processing in the colliculus-pulvinar pathway in children with dyslexia, which contributes to their reading difficulties. This early subcortical disruption differs from, and precedes, the neural deficits previously reported in the adult LGN, offering new insight into the developmental trajectory of dyslexia.
- Exploring the functionality of market-available tools for neural recording
Low-cost and open-source neural recording systems are increasingly important for expanding access to electrophysiological research. However, many existing platforms still rely on specialized hardware or limited modularity, restricting flexibility for laboratories seeking customizable solutions. Here, we developed and evaluated a modular neural recording platform constructed entirely from commercially available components. Recordings were compared against the ground truth. The platform successfully recovered local field potential (LFP)-like waveforms in most conditions and detected spike-like activity during direct connection recordings. Principal component analysis and k-means clustering further demonstrated the ability to distinguish multiple simulated spike waveforms. Signal quality varied across configurations, with saline recordings and preamplifier integration introducing increased noise and reduced detectability. These findings demonstrate the feasibility of building affordable and modular electrophysiology systems using widely accessible hardware. Although the current implementation has limitations in sampling rate, noise performance, and in vivo validation, the presented framework provides a practical foundation for future customizable open-source neural recording.
- JNJ-42153605, a mGluR2 PAM, potentiates Levetiracetam treatments of TBI to mitigate subsequent tau aggregation in a larval zebrafish model
Traumatic brain injury (TBI) has long-term consequences that include chronic traumatic encephalopathy (CTE) and an elevated risk for Alzheimer Disease (AD). These dementias ultimately manifest as tauopathies but may begin with acute neuronal dysfunction including post-traumatic seizures. Provocative evidence suggests that these prodromal seizures are a viable target to mitigate the later onset of dementias, and anti-epileptic drugs (AED) that increase the threshold of action potentials have indeed been shown to mitigate later tauopathies[1, 2]. Here, we test whether AEDs and other compounds that modulate synaptic transmission, applied immediately after TBI, can also act as prophylactics that block subsequent CTE-like tau aggregation and neurodegeneration in a larval zebrafish model. Levetiracetam (LEV) is an AED that modulates synaptic vesicle release. Application of LEV immediately following TBI abrogated TBI-induced tau tau aggregation (IC50 = 3.168 x10-3 mM) and cell death in the larval zebrafish TBI model. We next considered a polypharmacy approach involving mGluR2, because mGluR2 positively allosteric modulators (PAMs) such as JNJ-42153605 have previously been able to improve LEV's action in reducing some recalcitrant forms of seizure in a mouse model. We found that JNJ-42153605 was itself effective at blocking TBI-induced tau aggregation (IC50 = 8.691 x10-5 mM). Moreover, a subeffective dose of JNJ-42153605 (10-5 mM) was able to substantially improve the efficacy of LEV (~16-fold) in its prophylactic actions. Thus, LEV and JNJ-42153605 applied briefly after TBI offer a potent polypharmacy approach, at least in our preclinical animal model, to tackle the later tau aggregation and neurodegeneration that follows from TBI neurotrauma. These results warrant further investigation, including testing into mammalian TBI models (with longer disease course).
- NeuroEnergetics-on-Chip: a novel 3-compartment microfluidic platform to study metabolic interactions between brain parenchyma and cerebral vasculature
Neurological disorders are a major cause of death and disability worldwide. The brain's energy metabolism is essential to its proper function, yet the mechanisms driving neuroenergetic dysfunction remain poorly understood. A key challenge is the limited availability of human-relevant models that can reproduce the complexity of brain physiology. An Organ-on-Chip (OoC) system was developed to mimic the neurovascular unit metabolic coupling by incorporating human isogenic iPSC-derived endothelial-like cells, pericyte-like cells, astrocytes, and a cerebral organoid, representing the main cellular components of the NVU. The novel, customized microfluidic platform enables research on neurovascular coupling by interconnecting a blood-brain barrier-on-a-chip model with a 3D brain parenchymal compartment to mimic physiological conditions.
- A next-generation electronic frailty index leveraging deep learning on unstructured health records extends risk prediction across the full frailty spectrum
Background: Existing electronic frailty indices (eFI) are typically based on structured data and designed for older adults. We developed an eFI that integrates structured and unstructured electronic health records (EHRs) across adulthood and assessed its longitudinal trajectories and associations with adverse outcomes. Methods: We used longitudinal EHR data from 193629 individuals aged 35-103 in the Wellbeing Services County of Central Finland (2010-2023) and constructed a 53-item eFI including diagnosis codes, laboratory tests and items extracted from free-text clinical notes using deep-learning-based natural language processing. Associations with all-cause mortality, severe infections, fractures, and healthcare utilization were assessed using Cox and count models. Predictive performance was compared with Hospital Frailty Risk Score (HFRS) and Charlson Comorbidity Index (CCI). Findings: eFI trajectories accelerated notably from age 65 onwards. Using the eFI as a categorical variable, severe frailty was associated with higher risks of mortality (hazard ratio [HR] 7.31, 95% confidence interval [CI] 6.83-7.83), severe infections (HR 9.22, 95%CI 8.52-9.98), fractures (HR 2.75, 95%CI 2.52-3.01) and increased healthcare utilization (odds ratio [OR] 3.15, 95%CI 2.96-3.35) compared with non-frail. The risks were relatively greater in younger age groups and persisted when using the continuous eFI restricted to non-frail individuals. Across all outcomes, the eFI showed greater model discrimination than HFRS and CCI. Interpretation: An eFI using structured and unstructured EHR data improves risk stratification even in younger adults and at very low levels of frailty. Funding: Research Council of Finland, Instrumentarium Science Foundation, Sigrid Juselius Foundation and Samfundet Folkhalsan.
- Neurodevelopment Trajectory of Electrophysiological Functional Connectivity Following Alcohol Use Initiation
Background: Adolescence is characterized by profound neurodevelopmental changes that shape large-scale brain network organization and may confer vulnerability to risk-taking behaviors, including alcohol use. While cross-sectional and prospective studies have examined functional connectivity (FC) alterations before and after consumption, there is little evidence of how networks evolve during adolescence. Methods: The present longitudinal study investigated electrophysiological FC trajectories during alcohol initiation using resting-state magnetoencephalography (MEG). 61 alcohol-naive adolescents (mean age at baseline = 14.4) were assessed and re-evaluated two years later (mean age = 16.4). Results: At baseline, stronger FC in theta (4-8 Hz), alpha (8-12 Hz), and high-beta (20-30 Hz) bands predicted greater alcohol consumption at follow-up, replicating previous findings. Longitudinal analyses with linear mixed-effects models revealed significant stage-SAUs interactions across all three frequency bands. Adolescents with low-to-moderate alcohol use showed normative increases in FC over time, consistent with typical neurodevelopmental maturation. In contrast, heavier drinkers exhibited stabilization or reduction of FC, suggesting a divergence from normative trajectories. Notably, theta-band hyperconnectivity persisted after alcohol initiation and remained positively associated with current alcohol consumption, particularly across anteroposterior connections. Conclusion: These findings indicate heterogeneous neurodevelopmental trajectories associated with alcohol use severity. Elevated pre-consumption connectivity, especially in the theta band, may reflect a vulnerability marker rather than solely a consequence of alcohol exposure. Overall, results highlight the importance of considering individual variability in brain maturation when examining adolescent substance use and suggest that early hyperconnectivity may signal increased risk for heavier alcohol involvement.
- Stimulus and circuit contributions to the information geometry of neural manifolds
Understanding how network connectivity shapes neural representations is central to systems neuroscience. While dimensionality reduction methods uncover low-dimensional manifold structure in population recordings, a rigorous framework connecting manifold geometry to network mechanisms and information encoding remains lacking. We develop a differential geometric approach for analyzing neural manifolds in rate-based recurrent networks receiving tuned feedforward inputs. We derive expressions for the pullback metric of neural manifolds, showing how input tuning curves, feedforward and recurrent synaptic connectivity shape manifold geometry. Critically, we establish that the Fisher information matrix at steady states also has the structure of a pullback metric, directly linking intrinsic manifold geometry to stimulus discriminability and information encoding. For noise with slow temporal correlations propagated through the network, we show that recurrent effects on information geometry cancel: Fisher information depends only on the feedforward connectivity. Thus, feedforward connectivity critically determines representational geometry. As an example, we demonstrate that the representation of space by a module of hexagonal grid cells is approximately isometric for random distribution of grid phases. Moreover, a linear feedforward transformation can map spatially random input tuning curves into a population of hexagonal grid cells, forming a toroidal manifold. Thus, feedforward connectivity alone can generate structured spatial representations without requiring carefully tuned recurrent connectivity or continuous attractor dynamics. Recurrent connectivity, however, is shown to improve stimulus encoding under fast noise, thereby implementing a selective noise reduction.
- ST3GAL3 loss-of-function disrupts synaptic integrity and excitatory/inhibitory cortical dynamics
This study examined the role of ST3GAL3 as a regulator of excitatory/inhibitory (E/I) synaptic homeostasis using a human iPSC-based model. Neurodevelopmental disorders (NDDs) are increasingly linked to disruptions in the E/I balance, yet the molecular determinants remain poorly defined. ST3GAL3, a sialyltransferase associated with both rare monogenic disorders, including intellectual disability and infantile epilepsy, and complex polygenic conditions, such as ADHD, represents a strong candidate gene for involvement in synaptic regulation. To investigate this, isogenic ST3GAL3 knockout (ST3GAL3 KO) and wildtype (WT) iPSC lines were generated through CRISPR/Cas9 editing and diRerentiated into cortical neurons using both directed and induced protocols. This dual strategy enabled robust comparisons across cellular contexts and minimised methodological bias. To this end, we conducted functional characterisation using microelectrode array (MEA) technology alongside transcriptomic profiling through RNA sequencing (RNAseq), directly comparing ST3GAL3 KO-derived neurons with their isogenic controls. Functional assays using MEA revealed aberrant bursting patterns, particularly prolonged burst durations and heightened variability in S3GAL3KO neurons. Complementary transcriptomic profiling performed via RNAseq demonstrated downregulation in ST3GAL3 KO lines of genes involved in cognition, memory, as well as glutamatergic and GABAergic synaptic plasticity and functionality, providing molecular evidence for widespread synaptic dysregulation. Together, these findings establish ST3GAL3 as a key regulator of E/I balance in the cortices, advancing current knowledge on the pathophysiological involvement of ST3GAL3 deficiencies in the development of NDDs.
- A p53-ΔNp73 signaling axis drives selective motor neuron degeneration in spinal muscular atrophy
Selective neuronal vulnerability is a hallmark of many neurodegenerative diseases, yet how ubiquitous genetic insults cause highly selective neuronal loss remains poorly understood. In spinal muscular atrophy (SMA), reduced SMN levels trigger degeneration of specific motor neuron pools. Although non-apoptotic, p53-mediated death pathways have been implicated, p53 is expressed in both vulnerable and resistant neurons, leaving the downstream determinants of selective vulnerability unresolved. Here, we identify a p53-{Delta}Np73 signaling axis as a previously unrecognized execution pathway driving motor neuron degeneration. Using differential transcriptional profiling of SMA motor neurons following pharmacological modulation of p53 activity, we uncover p73 as a critical downstream mediator of neuronal death. Notably, SMN deficiency induces cell-autonomous, p53-dependent expression of the {Delta}Np73 isoform selectively in vulnerable, but not resistant, motor neurons. {Delta}Np73 induction precisely parallels the spatial and temporal pattern of degeneration in mouse models and is also detected in motor neurons from SMA patients. Strikingly, despite its established role as a pro-survival antagonist of p53, depletion of {Delta}Np73 improves motor neuron survival and partially preserves neuromuscular junction integrity in SMA mice. These findings reveal a context-dependent, isoform-specific functional switch in p53 family signaling that redirects a canonical survival factor into a driver of neurodegeneration, identifying a novel molecular mechanism underlying selective neuronal vulnerability in SMA and a potential therapeutic target for neuroprotection.
- Can Demographic Information Be Reduced in Retinal Fundus Images While Preserving Glaucoma-Relevant Features?
Purpose: To determine whether disease-aware adversarial perturbations can reduce demographic recoverability encoded in color fundus photographs (CFPs) while preserving glaucoma-related diagnostic features. Design: Retrospective analysis of a single-institution retinal imaging dataset using adversarial machine-learning experiments. Participants: A total of 4,271 patients contributing 13,959 CFPs from Massachusetts Eye and Ear. Methods: Vision Transformer (ViT) was trained for glaucoma detection and for prediction of race, sex, and ethnicity. Standard and disease-aware (DA) variants of four adversarial attacks--Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini & Wagner (C&W), and a diffusion-based attack--were applied to suppress demographic prediction; DA attacks augmented the adversarial objective with a disease-preservation term. Cross-architecture transferability was assessed by generating perturbations on ViT and applying them to ResNet50 and EfficientNetB0. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) and accuracy for glaucoma and demographic classification before and after perturbation, and disease-preservation and attack transferability across architectures. Results: At baseline, CFPs encoded both glaucoma-related and demographic information. Glaucoma detection AUCs were 0.958 (95% CI, 0.949-0.967), 0.960 (95% CI, 0.951-0.967), and 0.963 (95% CI, 0.955-0.971) in the race, sex, and ethnicity analysis cohorts, respectively. Demographic prediction performance was also high, with AUCs of 0.955 (95% CI, 0.945-0.963) for race, 0.983 (95% CI, 0.977-0.988) for sex, and 0.992 (95% CI, 0.987-0.996) for ethnicity. Standard attacks substantially reduced demographic AUC but often degraded glaucoma detection. Disease-aware optimization improved disease preservation while maintaining demographic suppression. Using a prespecified success criterion of at least 90% disease AUC preservation and demographic AUC reduction to 30% or less of baseline, DA-PGD and DA-Diffusion succeeded across race, sex, and ethnicity; DA-C&W succeeded for sex and ethnicity. Cross-architecture transferability experiments demonstrated that disease preservation transferred more robustly than demographic suppression. Conclusions: Disease-aware adversarial perturbations reduced the recoverability of demographic information in CFPs under white-box conditions while preserving glaucoma-relevant features, suggesting these representations are partially separable. Reduced demographic recoverability did not fully transfer across architectures, highlighting the need for architecture-agnostic methods.
- Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study
Summary Background A comprehensive transthoracic echocardiogram involves the assessment of over 70 parameters, placing a substantial burden on sonographers and physicians for manual annotation with considerable inter-observer variability. Prior open-source segmentation models have largely addressed 2D B-mode ventricular function, leaving a gap in the spectral Doppler and atrial measurements required for valvular and diastolic assessment such as velocity-time integral (VTI) and atrial chamber size. Methods In this retrospective multi-cohort study, we developed EchoNet-Segmentation, comprehensive task-specific deep learning segmentation models for left and right atrial area and VTI Doppler measurements. Training used 186,712 sonographer-annotated images from 93,978 studies (56,855 patients) at Cedars-Sinai Medical Center (CSMC). Performance was evaluated on a held-out CSMC test set, a CSMC temporal split, an external Kaiser Permanente Northern California cohort, and the public MIMIC-Echo dataset. Findings On the CSMC held-out test set, our AI models showed strong agreement with sonographer measurements, with R2 of 0.817-0.882 and mean absolute error (MAE) of 1.13-3.80 cm for automated VTI measurements, and R2 of 0.675-0.747 and MAE of 2.48-2.52 cm2 for left and right atrial area segmentation. Performance was consistently confirmed on the CSMC temporal split (VTI: R2 0.606-0.866, atrial area: R2 0.694-0.705) and on the KPNC external cohort (VTI: R2 0.575-0.859, atrial area: R2 0.803-0.876), on the MIMIC-Echo dataset. Robustness was demonstrated on a different vendor's machines and across subgroups. EchoNet-Segmentation outperformed an open-source medical image foundation model with bounding-box, point prompt configurations on R2, MAE, and Dice score on both held-out test dataset and MIMIC apical four-chamber data. Interpretation EchoNet-Segmentation is the first open-source framework that delivers accurate, generalizable automated measurement across several key routine echocardiographic parameters, supporting end-to-end automation of clinically important echocardiographic assessments. Public release of model weights, code, and demonstration tools can facilitate reproducibility, research use and clinical deployment.