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.

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.