This study evaluates the clinical efficacy and safety of modified deep lateral wall decompression surgery in thyroid-associated ophthalmopathy and optic neuropathy, demonstrating significant postoperative improvements in vision, exophthalmos, and intraocular pressure with a low rate of complications.

Foundation and multimodal models now match expert-level performance on many diagnostic, prognostic, and biomarker tasks in pathology. Yet only a handful of AI systems are used in routine clinical practice, and the products that have reached patients differ substantially from research prototypes. We define this mismatch as the adoption paradox of computational pathology.

We first survey the technical landscape from task-specific deep learning to large unimodal foundation models, multimodal systems, and early agentic architectures. We then examine what has actually entered the clinic, identifying four product archetypes (digital pathology platforms, population scale cytology screening, assistive detection in surgical pathology, and quantitative immunohistochemistry scoring). Using a three stage maturity model algorithmic capability (Stage 1), system integration (Stage 2), and institutional adoption (Stage 3), we analyze the structural barriers that gate each transition.

Three interconnected barriers explain most of the gap: (1) Data and infrastructure fragility [(pre-analytical variability, scanner-induced domain shift, format fragmentation, annotation scarcity, manual quality control (QC)]; (2) Workflow misalignment (cognitive rhythm of pathologists, automation bias, scenario-dependent latency); (3) Institutional trust deficits (shallow interpretability, incomplete prospective validation, unclear reimbursement, unsettled liability, and regulatory gaps for generative/adaptive systems).

We outline system-level pathways for each stage, including infrastructure first, AI, workflow-embedded intelligence, and adaptive governance. Our central claim is that the next phase of progress will depend less on architectural novelty than on the slower institutional work that turns capability into clinical benefit. The framework provides an actionable lens for regulators, developers, and healthcare organizations to diagnose why a given AI system remains a prototype and what is needed to move it into routine use.

This review provides a critical assessment of the ongoing debate surrounding altermagnetism in ruthenium dioxide (RuO₂), synthesizes a decade of experimental and theoretical efforts, offering a clear roadmap of the studies conducted on RuO₂.

Type 2 diabetes (T2D) is marked by insulin resistance together with the progressive failure of pancreatic β-cells. Increasing evidence suggests that this failure is shaped not only by β-cells themselves, but also by the local islet immune microenvironment, where resident and recruited macrophages can influence inflammation, islet architecture, and insulin secretion. Exercise is a cornerstone of diabetes management, yet how it acts within this islet microenvironment has remained unclear.

While optical computing systems (OCS) with high bandwidth, low latency, and inherent parallelism are promising accelerators for artificial intelligence, the existing OCS implementations require direct participation of physical hardware, tightly coupling development workflows to device access and limiting offline design. Now, researchers have developed a Digital Twin OCS, a system-level, measurement-driven digital surrogate that enables a hardware-decoupled and fully offline development paradigm for OCS.

Researchers from Zhejiang Normal University and collaborating institutions in China have revealed how crown architecture mediate neighborhood interactions and growth strategies across a shade tolerance gradient. By analyzing nearly 3,600 trees in a subtropical forest, they show that light-demanding species compete mainly through crown trait dissimilarity among neighbors, while shade-tolerant species are more influenced by neighbor density, offering new insights for forest dynamics models.