Browse Articles

Synergistic Doping and Stabilization of Magnetically Tunable <i>Ln</i> Ti ₃ (Sb,Sn) ₄ ( <i>Ln</i> : Ce–Gd) Kagome Metals

Development of high-early-strength engineered cementitious composite with GGBS and nano-silica for bonding with self-compacting concrete

Beyond Thermal Limits: Manipulating Reactive Intermediate Coverages and Turnover Rates via Visible Photon-Mediated Catalysis on Rh-Doped Perovskite Oxides

Lignin-based nanoparticles as sustainable agents for enhanced oil recovery in sandstone and carbonate reservoirs

Probing the Thermal Stability of the In Situ Ring-Opening Polymerized Solid Polymer Electrolytes

In silico pipeline for GSK 3β inhibitor discovery in Alzheimer’s disease using pharmacophore screening, docking, ADME filtering, and MD validation

Abstract Glycogen synthase kinase-3β (GSK-3β) is a key therapeutic target for Alzheimer’s disease, but identifying safe, brain-penetrant inhibitors remains difficult. This study aimed to discover novel CNS-active GSK-3β inhibitors using a rigorous multi-tier computational pipeline. The workflow combined ligand-based and structure-based pharmacophore modeling, virtual screening of the ZINCPharmer database, AutoDock Vina docking, ADME and blood-brain barrier (BBB) filtering with SwissADME, toxicity prediction using ProTox-3.0, and validation by 100-ns molecular dynamics simulations with MM/GBSA and MM/PBSA free energy calculations. Pharmacophore screening with a ≤ 1.0 Å RMSD cutoff identified 1,085 ligand-based and 36 structure-based hits. After docking and developability filtering, two BBB-permeant candidates were prioritized: SB1 , a structure-based hit (predicted LD 50  = 2500 mg/kg, toxicity class 5), and LB1 , a ligand-based hit (predicted LD 50  = 521 mg/kg, toxicity class 4). Molecular dynamics confirmed stable binding for both compounds. MM/GBSA analysis showed favorable binding free energies for SB1 (-27.68 kcal/mol) and LB1 (-25.74 kcal/mol), both surpassing the co-crystallized reference (-8.75 kcal/mol). These findings identify SB1 and LB1 as promising, safe, and brain-penetrant GSK-3β lead compounds for experimental validation in Alzheimer’s disease.

Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning

Field-Coupled Water Splitting with Metal-Free Donor–Acceptor Covalent Organic-Framework Junctions

Plant membranes shuffle lipids around to stay firm under heat stress

Interfacial Ligand Engineering Breaks the Activity-Selectivity Trade-Off in Electrochemical Alkynol Semihydrogenation and -Deuteration

Formal Markovnikov Hydroazolation of Alkenes via Reductive Activation of Alkylthianthrenium Salts

Hybrid Hydrogen-Bond Networks Steer Proton-Coupled Pathway for Acidic CO ₂ -To-Ethanol Electrosynthesis

Balancing N ₂ Activation in Two-Dimensional Electrides for Ammonia Synthesis

SIRT7 regulates dosage compensation and safeguards the female X chromosome

A deep-learning framework reveals whole-body perturbations at cell level

Abstract Many diseases, including obesity, have systemic effects that perturb multiple organ systems throughout the body 1,2 . However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here we developed MouseMapper, a suite of foundation-model-based deep-learning algorithms enabling multi-system analysis of disease across the entire mouse body. MouseMapper enables whole-body quantitative analysis of nerves and immune cells, resolving fine axonal branches and immune-cell clusters while automatically segmenting 31 organs and tissues. We used MouseMapper to study diet-induced obesity, and identified structural alterations of the infraorbital branch of the trigeminal ganglia. This structural impairment in infraorbital nerves was associated with functional sensory deficits in whisker sensing. Furthermore, we identified proteomic changes in the trigeminal ganglion affecting axon remodelling and complement pathways both in mice and humans. MouseMapper also generated detailed three-dimensional inflammation maps by characterizing immune cell cluster compositions across tissues. The MouseMapper framework demonstrates robust generalizability across different imaging resolutions and datasets. Our study provides a powerful, scalable approach for identifying and quantifying systemic pathologies, bridging molecular insights from animal models to human conditions.

Rising stars of mathematics awarded prestigious 2026 Fields Medal

This El Niño is set to be the largest on record by a ‘mind-blowing margin’

A critical initialization for biological neural networks

Abstract Intrinsically generated, brainwide neural activity displays macroscopic coordination among large populations of neurons that persists beyond the biophysical timescales of individual neurons 1–3 . It is not well understood how these macroscopic behaviours arise from microscopic, short-lived interactions between pairs of neurons. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was population activity in hippocampal area CA1, which resembled an efficient, uncorrelated neural code that may be optimized for information storage capacity. High-dimensional, global activity modes emerged in critically normalized artificial networks and persisted under sparse, clustered or spatial connectivity. These dynamics were useful for solving time-dependent tasks such as a zero-shot working memory task.

Could small-team science be dying out?

Breathalyser can tell if you are burning fat from a single exhalation