Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm−1). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm−1). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.

低强度激光治疗(LLLT)是一种通过低强度激光照射相关皮肤、穴位等人体部位治疗心脑血管疾病、缓解疼痛、促进伤口愈合的新型物理方法。它能够刺激线粒体呼吸链的复合物Ⅳ(细胞色素c氧化酶)并增加腺苷三磷酸酯、活性氧化物、一氧化氮等物质的合成, 有助于定向调节细胞行为。高血压、高血糖和高血脂(三高)是最常见的血液疾病, 其导致的血液各参数的变化将引起其他脏器功能异常。目前, “三高”的发病群体数量日益增加, 患者偏年轻化, 因此迫切需要一种便携有效的治疗技术来应对该疾病。近年来研究发现, LLLT在血液系统疾病中有明显的作用, 能有效降低高血压。此外, LLLT还可以调节血糖, 并对因血糖过高导致的相关并发症起到一定的改善, 同时还可调节血脂的浓度, 但更多的应用侧重于前两者。这种治疗技术具有无创和便携等优势, 因此有望成为新的治疗方法。本文将对有关LLLT技术在“三高”中的应用及相关的机制进行综述。