Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering

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.

Rapid label-free SERS detection of foodborne pathogenic bacteria based on hafnium ditelluride-Au nanocomposites

Two-dimensional(2D)nanomaterials have captured an increasing attention in biophotonics owing to their excellent optical features.Herein,2D hafnium ditelluride(HfTe_(2)),a new member of transition metal tellurides,is exploited to support gold nanoparticles fabricating HfTe_(2)-Au nanocomposites.The nanohybrids can serve as novel 2D surface-enhanced Raman scattering(SERS)substrate for the label-free detection of analyte with high sensitivity and reproducibility.Chemical mechanism originated from HfTe_(2) nanosheets and the electromagnetic enhancement induced by the hot spots on the nano-hybrids may largely contribute to the superior SERS effect of HfTe_(2)-Au nanocomposites.Finally,HfTe_(2)-Au nanocomposites are utilized for the label-free SERS analysis of foodborne pathogenic bac-teria,which realize the rapid and ultrasensitive Raman test of Escherichia coli,Listeria mono-cytogenes,Staphylococcus aureus and Salmonella with the limit of detection of 10 CFU/mL and the maximum Raman enhancement factor up to 1.7×10^(8).Combined with principal component analysis,HfTe_(2)-Au-based SERS analysis also completes the bacterial classification without extra treatment.

基于肿瘤靶向MoO2纳米聚集体的近红外区深组织光热治疗

近红外II区(NIR-II,1000–1350 nm)的光热治疗(PTT)近年来发展迅速,其最大允许照射量和组织穿透深度均高于近红外I区(NIR-I,650–950 nm).金纳米结构、单壁碳纳米管、钯纳米颗粒等材料已作为高效的NIR-II光热消融肿瘤的治疗剂被探索,而关于NIR-II PTT后的深部组织转化的细节信息还有待发掘.本文系统地研究了NIR-II深层组织PTT术后肿瘤的深度分布.基于NIR响应的氧化钼(MoO2)纳米聚集体的肿瘤靶向治疗纳米系统,我们开展了光热肿瘤治疗.为了验证PTT后的组织深度相关细节,我们建立了三个不同层次的模型:组织模型、三维细胞系统模型与荷瘤动物模型.NIR-II激光在组织模型中表现出较低的光热衰减系数(1064 nm时为0.541),而在808 nm时该值为0.959,这使得它在体内外都能更好地进行深层组织PTT.深度剖面分析表明肿瘤组织的显微结构破坏与穿透深度呈负相关.同时,我们利用拉曼光谱对PTT后组织深度图谱的生化指纹变化进行解码,揭示了光热消融肿瘤组织中主要的生化成分紊乱,为NIR-II深层组织光热治疗提供了理论基础.

Lamellar hafnium ditelluride as an ultrasensitive surface-enhanced Raman scattering platform for label-free detection of uric acid

The development of two-dimensional(2D)transition metal dichalcogenides has been in a rapid growth phase for the utilization in surface-enhanced Raman scattering(SERS)analysis.Here,we report a promising 2D transition metal tellurides(TMTs)material,hafnium ditelluride(HfTe2),as an ultrasensitive platform for Raman identification of trace molecules,which demonstrates extraordinary SERS activity in sensitivity,uniformity,and reproducibility.The highest Raman enhancement factor of 2.32×10^(6)is attained for a rhodamine 6G molecule through the highly efficient charge transfer process at the interface between the HfTe2 layered structure and the adsorbed molecules.At the same time,we provide an effective route for large-scale preparation of SERS substrates in practical applications via a facile stripping strategy.Further application of the nanosheets for reliable,rapid,and label-free SERS fingerprint analysis of uric acid molecules,one of the biomarkers associated with gout disease,is performed,which indicates arresting SERS signals with the limits of detection as low as 0.1 mmol/L.The study based on this type of 2D SERS substrate not only reveals the feasibility of applying TMTs to SERS analysis,but also paves the way for nanodiagnostics,especially early marker detection.