Esophageal squamous cell carcinoma (ESCC) is the predominant histological subtype of esophageal cancer, accounting for approximately 90% of cases worldwide, with a particularly high incidence in Asian populations. ESCC is characterized by aggressive behavior and pronounced tumor heterogeneity. Although surgical resection remains the primary curative treatment, patients with locally advanced disease frequently experience recurrence and distant metastasis, resulting in poor clinical outcomes.

As the “Method of the Year 2024”, spatial proteomics (SP) enables in situ characterization of protein localization, abundance, and interactions across subcellular to tissue scales, surpassing conventional bulk proteomics. This work systematically summarizes key technological advances in imaging-based and mass spectrometry-based SP platforms, AI-driven bioinformatics innovations, and multi-omics integration strategies, while highlighting transformative applications in disease stratification, therapeutic target discovery, and drug development. It also outlines current challenges and future directions, providing a comprehensive roadmap for advancing SP toward clinical translation and personalized healthcare.

A high-resolution proteomic landscape of minor glomerular abnormalities (MGAs) is established via pressure cycling technology (PCT)-assisted data-independent acquisition (DIA) proteomics, which uncovers distinct molecular alterations and 13 core upregulated nuclear proteins in MGA tissues. These findings provide novel insights into MGA’s molecular pathogenesis and identify potential tissue biomarkers and therapeutic targets, while the PCT-assisted DIA workflow offers a robust technical framework for proteomic analysis of microscale renal biopsy samples.

Birds that put more energy into parenthood age faster and die younger, new research shows.

Researchers analyzed communication across the animal kingdom, including firefly flashes, cricket chirps, frog croaks, birds’ mating displays and more. Across species, many communication signals repeat at two beats per second. Brains are most effective at processing signals that arrive about twice per second. Findings suggest communication signals may have evolved to match the rhythms the brain processes most easily.

Insilico Medicine has expanded its Science MMAI Gym, a large-scale training and benchmarking platform for artificial intelligence, with the launch of three public leaderboard portals designed to evaluate AI performance across scientific research and drug discovery. Positioned as both a training environment and benchmarking system, MMAI Gym enables the development of domain-specific AI models while rigorously assessing their capabilities on real-world tasks.

The newly launched benchmark categories include ScienceAI Bench, which evaluates general scientific reasoning across disciplines such as biology, chemistry, and materials science; the Drug Discovery Benchmark (DDB), focused on end-to-end pharmaceutical R&D tasks; and Insilico Bench, a proprietary suite targeting complex and emerging scientific challenges. Together, these benchmarks draw from both curated industry datasets and proprietary, experimentally grounded data, enabling multi-dimensional evaluation across more than 200 tasks.

The platform reflects a broader shift toward standardized, scalable evaluation of scientific AI systems. Previous results from Insilico demonstrate that models trained within MMAI Gym can achieve up to tenfold performance improvements on key drug discovery benchmarks. In collaboration with Liquid AI, Insilico also developed a compact foundation model that achieved state-of-the-art performance across multiple drug discovery tasks, with findings presented at ICLR 2026.

By integrating training, benchmarking, and public evaluation, MMAI Gym aims to accelerate the adoption of reliable, high-performance AI systems across pharmaceutical research and beyond.