Colorectal cancer (CRC) represents one of the most urgent yet addressable oncology challenges confronting Latin America. In Mexico, digestive tract cancers remain a leading contributor to cancer mortality, while CRC continues to rank as the second-most common malignant neoplasm among Latino populations in the Americas.

This crisis stems less from a lack of therapeutic advances than from severe diagnostic bottlenecks. In several large regional hospital networks, the median interval from initial symptom onset to definitive treatment stretches to seven months. The longest portion of that delay occurs between initial clinical evaluation and diagnostic confirmation. When health systems rely on late stage symptomatic presentation, oncology care becomes reactive, expensive, and frequently palliative.

A research team has uncovered genetic clues to flowering time, salt stress responses, and seed characteristics in soybean landraces from Zhejiang Province, China. 

Improving X-ray imaging under photon-starved conditions could enable lower-dose and higher-resolution imaging for medical diagnosis, security inspection, and industrial testing. Researchers from Chongqing University, the 26th Institute of CETC, and Stevens Institute of Technology developed a scalable photonic crystal scintillator using Marangoni effect-driven interfacial self-assembly. The approach increased average light output by 4.64-fold and improved the X-ray imaging signal-to-noise ratio by 13 dB while maintaining spatial resolution better than 25 μm.

A new review published in PhotoniX Synergy examines how artificial intelligence is transforming the design of terahertz (THz) metamaterial devices (meta-devices). The authors survey conventional and AI-assisted approaches for THz meta-absorbers, modulators, filters, sensors, detectors, and multifunctional devices, and show how machine learning, evolutionary algorithms, deep learning, generative models, and large language models can establish data-driven links between structural geometry and electromagnetic response. The review also discusses challenges including computational cost, limited interpretability, data scarcity, and manufacturability, and outlines future directions such as physics-informed learning, multi-scale optimization, and AI-assisted optical computing hardware for 6G communication, imaging, biosensing, spectroscopy, and security inspection.

Oil-rich food waste leachate is difficult to treat. Researchers at Zhejiang A&F University found that combining Pseudomonas aeruginosa STQ-2 with rhamnolipid-containing fermentation supernatant improved oil degradation and chemical oxygen demand (COD) removal. At an initial used cooking oil concentration of 5.0 g/L, the combined treatment achieved rates of approximately 89.9% and 95.6%, respectively, after 96 hours. The findings point to a potential biological approach for treating oily food waste leachate.