Human-Relevant Ptpn6 mutation reveals complex links between immunity, metabolism and liver aging
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Updates every hour. Last Updated: 27-Oct-2026 14:17 ET (27-Oct-2026 18:17 GMT/UTC)
Researchers at City of Hope®, one of the largest and most advanced cancer research and treatment organizations in the country, led the development of a new, investigational blood test that could detect pancreatic cancer at its earliest stages, when the disease is more treatable and potentially curable.
One reason pancreatic cancer remains so deadly is because it’s usually discovered late, after it has spread. A large, international study, published in Nature Medicine today, found that this liquid biopsy, called PANXEON*, was highly sensitive in detecting stage 1 and 2 pancreatic cancer and had a low rate of false positives. The experimental test was also able to identify high-grade dysplasia, a precancerous condition of the pancreas, potentially enabling intervention before cancer develops.
As brain-computer interfaces become increasingly precise in reading human emotions, a practical question arises: Will a model trained today still be able to recognize the emotions of the same person tomorrow, in a few weeks, or even months later? Recently, the team led by Shuang Liu from the Medical School of Tianjin University/Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration published a research paper titled "Toward Sustainable Cross-Time Affective Brain–Computer Interfaces: Explicit Modeling and Structural Calibration of Temporal Variability" in the National Science Review (NSR). This study offers a cross-time "EEG ruler" for affective brain-computer interfaces (aBCIs). To address temporal variability in cross-time emotion recognition, the researchers propose a Multi-level Dynamic Integrated Perception Network (MDIN), which explicitly models and structurally calibrates temporal variability on three levels: emotional neural representation, dynamic functional connectivity, and cross-time discriminative structures, offering a new technical pathway for long-term stable affective brain-computer interfaces. This work may further accelerate the transition of aBCIs from short-term experimental validation to long-term, adaptive use in real-world mental health monitoring and human-machine interaction.