Label-Free Analysis of Protein Biomarkers Using Pattern-Optimized Graphene-Nanopyramid SERS for the Rapid Diagnosis of Alzheimer’s Disease

The quantitative and highly sensitive detection of biomarkers such as Tau proteins and Aβ polypeptides is considered one of the most effective methods for the early diagnosis of Alzheimer’s disease (AD). Surface-enhanced Raman spectroscopy (SERS) detection is a promising method that faces, however, challenges like insufficient sensitivity due to the non-optimized nanostructures for specialized analyte sizes and insufficient control of the location of SERS hot spots. Thus, the SERS detection of AD biomarkers is restricted. We reported here an in-depth study of the analytical Raman enhancement factor (EF) of the wafer-scale graphene-Au nanopyramid hybrid SERS substrates using a combination of both theoretical calculation and experimental measurements. Experimental results show that larger nanopyramids and smaller gap spacing lead to a larger SERS EF, with an optimized analytical EF up to 1.1 × 1010. The hybrid SERS substrate exhibited detection limits of 10–15 M for Tau and phospho-Tau (P-Tau) proteins and 10–14 M for Aβ polypeptides, respectively. Principal component analysis correctly categorized the SERS spectra of different biomarkers at ultralow concentrations (10–13 M) using the optimized substrate. Amide III bands at 1200–1300 cm–1 reflect different structural conformations of proteins or polypeptides. Tau and P-Tau proteins are inherently disordered with a few α-helix residuals. The structure of Aβ42 polypeptides transitioned from the α-helix to the β-sheet as the concentration increased. These results demonstrate that the hybrid SERS method could be a simple and effective way for the label-free detection of protein biomarkers to enable the rapid early diagnosis of AD and other diseases

DataSheet_1_Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid.doc

BackgroundIt is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.ObjectiveThis study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.MethodThe cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.ResultsWith respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.ConclusionA deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.</p