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.

深度学习使辅助诊断的软件能够更积极有效地开发和应用，但是组织病理学图像的颜色变化降低了这些算法的性能。染色归一化可以解决扫描仪效应、不同的染色方法、患者的疾病状态、染色时间等因素产生的图像异质性。虚拟染色可以摆脱载玻片染色，减少载玻片的制备步骤，为临床缩短样本的制备时间，节省大量的成本。在缺乏注释训练数据的情况下，病理图像数据增强可用于创建具有纹理和颜色、样式逼真的人工样本来促进网络训练。本文就组织学病理图像在深度学习病理分析中染色处理的染色归一化、虚拟染色和数据增强等方面展开综述，为组织学病理图像在临床上的应用和研究提供参考。

In this paper, optical coherence tomography (OCT) and surface-enhanced Raman spectroscopy (SERS) were used to characterize normal knee joint (NKJ) tissue and knee osteoarthritis (KOA) tissue ex vivo. OCT images show that there is a clear hierarchical structure in NKJ tissue, including surface layer, transitional layer, radiation layer and cartilage matrix calcification layer tissue structure, while the hierarchical structure of KOA tissue is not clear and unevenly distributed, and the pathological tissues at different stages also show significant differences. SERS shows that NKJ tissue and mild osteoarthritic knee cartilage (MiKOA) tissue have strong characteristic Raman peaks at 964, 1073 (1086), 1271, 1305, 1442, 1660 and 1763cm?1. Compared with the Raman spectrum of NKJ tissue, the Raman characteristic peaks of MiKOA tissue have some shifts, moving from 1073cm?1 to 1086cm?1 and from 1542cm?1 to 1442cm?1. There is a characteristic Raman peak of 1271cm?1 in MiKOA tissue, but not in NKJ tissue. Compared with NKJ tissue, severely degenerated cartilage (SdKOA) tissues show some new SERS peaks at 1008, 1245, 1285, 1311 and 1321cm?1, which are not seen in SERS spectra of NKJ tissue. Principal component analysis (PCA) was used to analyze the Raman spectra of 1245–1345cm?1 region. The results show that PCA can distinguish NKJ, MiKOA and SdKOA tissues and the accuracy is about 90%. These results indicate that OCT can clearly distinguish NKJ, MiKOA, moderate osteoarthritic knee cartilage (MoKOA) and SdKOA tissue, while SERS can provide further judgment basis. The results also prove that the contents of protein and polysaccharide in knee tissue are changed during the pathological process of knee tissue, which is the cause of pain caused by poor friction in knee joint during movement.In this paper, optical coherence tomography (OCT) and surface-enhanced Raman spectroscopy (SERS) were used to characterize normal knee joint (NKJ) tissue and knee osteoarthritis (KOA) tissue ex vivo. OCT images show that there is a clear hierarchical structure in NKJ tissue, including surface layer, transitional layer, radiation layer and cartilage matrix calcification layer tissue structure, while the hierarchical structure of KOA tissue is not clear and unevenly distributed, and the pathological tissues at different stages also show significant differences. SERS shows that NKJ tissue and mild osteoarthritic knee cartilage (MiKOA) tissue have strong characteristic Raman peaks at 964, 1073 (1086), 1271, 1305, 1442, 1660 and 1763cm?1. Compared with the Raman spectrum of NKJ tissue, the Raman characteristic peaks of MiKOA tissue have some shifts, moving from 1073cm?1 to 1086cm?1 and from 1542cm?1 to 1442cm?1. There is a characteristic Raman peak of 1271cm?1 in MiKOA tissue, but not in NKJ tissue. Compared with NKJ tissue, severely degenerated cartilage (SdKOA) tissues show some new SERS peaks at 1008, 1245, 1285, 1311 and 1321cm?1, which are not seen in SERS spectra of NKJ tissue. Principal component analysis (PCA) was used to analyze the Raman spectra of 1245–1345cm?1 region. The results show that PCA can distinguish NKJ, MiKOA and SdKOA tissues and the accuracy is about 90%. These results indicate that OCT can clearly distinguish NKJ, MiKOA, moderate osteoarthritic knee cartilage (MoKOA) and SdKOA tissue, while SERS can provide further judgment basis. The results also prove that the contents of protein and polysaccharide in knee tissue are changed during the pathological process of knee tissue, which is the cause of pain caused by poor friction in knee joint during movement.

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。

宽场光学相干层析成像技术(WF-OCT)是光学相干成像技术领域的一个新方向。其接收干涉信号获得样品的二维信息, 一次性建立样品整个断面的图像, 可快速高效地获得样品的成像信息, 以实现对物体特质的研究。本文就WF-OCT的基本原理、系统的基本构成及特点, 以及近年来在眼科疾病诊断方面的应用进行了综述, 可为WF-OCT仪器的设计及其在眼科医学中的应用提供思路和参考。

光学相干层析血管造影(OCTA)以光学相干层析(OCT)为基础, 是一种新型、无损、快速、安全的三维血管成像技术。OCTA成像的方法有多种, 根据其算法使用的OCT信号信息可将其分为基于相位、振幅和复合信号３种类型。本文在描述各类型的成像原理、算法及其优缺点后, 对３种类型的OCTA方法进行了比较, 其中使用复合信号的类型成像质量最好。同时, 结合OCTA的研究进展对其未来的发展进行了展望。

Two-dimensional (2D) nanomaterials have captured an increasing attention in biophotonics owing to their excellent optical features. Herein, 2D hafnium ditelluride (HfTe₂), a new member of transition metal tellurides, is exploited to support gold nanoparticles fabricatingHfTe₂-Au nanocomposites.The nanohybrids can serve as novel 2D surface-enhanced Raman scattering (SERS) substrate for the labelfree detection of analyte with high sensitivity and reproducibility. Chemical mechanism originated from HfTe₂ nanosheets and the electromagnetic enhancement induced by the hot spots on the nanohybrids may largely contribute to the superior SERS effect of HfTe₂-Au nanocomposites. Finally, HfTe₂-Au nanocomposites are utilized for the label-free SERS analysis of foodborne pathogenic bacteria, which realize the rapid and ultrasensitive Raman test of Escherichia coli, Listeria monocytogenes, Staphylococcus aureus and Salmonella with the limit of detection of 10 CFU/mL and the maximumRaman enhancement factor up to 1.7 × 10⁸.Combined with principal component analysis, HfTe₂-Au-based SERS analysis also completes the bacterial classification without extra treatment.

巨噬细胞极化在骨修复中起着非常重要的作用。骨修复材料在植入体内后,会引起巨噬细胞的响应,使其向M1/M2极化,从而影响材料的成骨效果。本文制备了掺锶量分别为0%,5%,10%和15%摩尔百分比的锶掺杂微纳米生物活性玻璃(Sr-MNBG),并研究了不同掺锶量的Sr-MNBG对巨噬细胞极化的影响；随后,在此基础上研究了不同掺锶量Sr-MNBG调控的巨噬细胞分泌成分对小鼠骨髓间充质干细胞(mMSCs)向成骨分化的影响；最后,通过体内原位骨缺损修复实验验证了不同掺锶量Sr-MNBG对巨噬细胞极化的调控作用及其体内成骨作用。体外实验结果显示,掺锶量为5%,10%和15%的Sr-MNBG较掺锶量为0%的Sr-MNBG更能促进巨噬细胞向M2极化,其中10%掺锶量的Sr-MNBG具有最强的促巨噬细胞M2极化作用,且10%掺锶量的Sr-MNBG调控的巨噬细胞分泌成分更有利于成骨。体内实验结果表明,不同掺锶量的Sr-MNBG植入到体内后,10%掺锶量的Sr-MNBG周围有更多的M2巨噬细胞浸润,且表现出更好的骨修复效果,与体外实验结果相一致。因此,Sr-MNBG有望作为一种富有前景的免疫调控材料应用于骨修复。

通过对LED在生物医学领域的应用分析发现, LED光存在与激光相类似的生物刺激效应。目前弱激光血疗已由血管内照射发展到体表照射、黏膜照射。这为LED光照射体表、黏膜实现血疗提供了良好的预期。为了证明LED光可以代替激光作为净血治疗仪的光源, 本实验设计了激光与LED光对自由基损伤模型红细胞变形性作用的对比实验, 结果表明, 两种光照方法均对红细胞变形性有改善作用。LED相比激光安全性更好、成本更加低廉, 特别适合往家用小型化方向发展。