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<p>This study explores the intervention effect of <i>Sparassis Latifolia</i> Polysaccharides (SLPs) on abnormal glucose metabolism in C57BL/6J mice, induced by a high-fat diet and streptozotocin (STZ). The results show that high-concentration SLPs significantly improve abnormal glucose metabolism. A mouse facial expression dataset was constructed, covering five glucose metabolic states: Norm, Pre-stage of Abnormal Glucose Metabolism, Abnormal Glucose Metabolism, Early Stage of SLPs Intervention, and Late Stage of SLPs Intervention, based on fasting blood glucose and facial expression features. To achieve non-invasive detection of abnormal glucose metabolism, a lightweight deep learning model, LFPP-YOLO, was proposed. This model incorporates a Partial Self-Attention (PSA) module to enhance global context information extraction and utilizes the L-FFCA structure for multi-level feature enhancement and background suppression, improving its adaptability to complex expressions. The PIoU v2 loss function was employed to optimize facial expression localization accuracy and robustness. Experimental results show that the LFPP-YOLO model achieves an average facial detection accuracy of 95.1% across the five metabolic states, with a compact size of 2.4&#xa0;MB and a real-time inference speed of 5ms, significantly outperforming existing models. This model provides a novel approach and technical support for non-invasive screening and personalized intervention of abnormal glucose metabolism in mice.</p>

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Research on improved models for facial expression recognition in mice with abnormal glucose metabolism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Xiaofeng Guo,
  • Lijian Shi,
  • Boyang Ma,
  • Cuiping Feng,
  • Zhenyu Liu

摘要

This study explores the intervention effect of Sparassis Latifolia Polysaccharides (SLPs) on abnormal glucose metabolism in C57BL/6J mice, induced by a high-fat diet and streptozotocin (STZ). The results show that high-concentration SLPs significantly improve abnormal glucose metabolism. A mouse facial expression dataset was constructed, covering five glucose metabolic states: Norm, Pre-stage of Abnormal Glucose Metabolism, Abnormal Glucose Metabolism, Early Stage of SLPs Intervention, and Late Stage of SLPs Intervention, based on fasting blood glucose and facial expression features. To achieve non-invasive detection of abnormal glucose metabolism, a lightweight deep learning model, LFPP-YOLO, was proposed. This model incorporates a Partial Self-Attention (PSA) module to enhance global context information extraction and utilizes the L-FFCA structure for multi-level feature enhancement and background suppression, improving its adaptability to complex expressions. The PIoU v2 loss function was employed to optimize facial expression localization accuracy and robustness. Experimental results show that the LFPP-YOLO model achieves an average facial detection accuracy of 95.1% across the five metabolic states, with a compact size of 2.4 MB and a real-time inference speed of 5ms, significantly outperforming existing models. This model provides a novel approach and technical support for non-invasive screening and personalized intervention of abnormal glucose metabolism in mice.