Optical computing is attracting strong interest because light can carry and process information at extremely high speed while consuming less energy than many electronic approaches. In principle, optical systems can accelerate demanding tasks such as artificial intelligence inference, image processing, scientific simulation, and high-bandwidth signal processing. However, most proposed optical processors still depend on electronic memory to hold intermediate data. Each time information is converted between light and electronics, the system pays a penalty in speed, energy, and complexity.

A study in Chinese Medical Journal reports that SLC38A7, a lysosomal glutamine transporter, is overexpressed in gastric cancer and associated with poor prognosis. Using patient tissues, cell models, mutational analyses, and mouse xenografts, the researchers show that SLC38A7 binds MYH9, promotes GSK-3β ubiquitination and degradation, and activates Wnt/β-catenin signaling. The findings identify a metabolically linked oncogenic pathway, clarify a previously unrecognized disease mechanism, and suggest a potential therapeutic target for gastric cancer.

A new flat fiber fabrication technique produces a high fidelity sensor for a wide range of applications, from batteries to bridges, according to a recently-published study.

Researchers from KTH Royal Institute of Technology in Stockholm and the University of Southampton in the UK have developed a new type of flat silica optical fiber that performs differently from conventional round fiber. The work, published in Nature Communications, introduces a high-aspect-ratio platform that can be engineered to respond strongly to pressure, temperature and other physical changes.

This collection features articles on Functional Materials published in Smart Materials in Manufacturing. All articles are free to read and download.

Fluorescence molecular imaging is an evolving technology for precision tumor diagnosis and therapy. By means of fluorescence probes, it enables the visualization of tumor-associated molecules, cells, or microenvironmental components, thereby facilitating the efficient identification and localization of lesions. With recent advances in the near-infrared II (NIR-II) window, targeted probes, microenvironment-responsive probes, multimodal imaging strategies, and theranostic integrated probes, fluorescence molecular imaging is shifting from a modality primarily used to “visualize lesions” toward a more comprehensive platform for tumor identification, therapy facilitation, and treatment-response monitoring. Meanwhile, artificial intelligence, particularly deep learning, can support image denoising, enhancement, resolution improvement, and three-dimensional localization, and may further assist in tumor recognition and grading, minimal residual disease assessment, and metastatic risk estimation. Overall, the integration of fluorescence imaging with AI is advancing oncology toward a more precise, intelligent, and integrated paradigm; however, key challenges remain, including probe biocompatibility and safety, clinical translation, and the lack of standardized imaging protocols.