Detection of K562 Leukemia Cells in Different States Using a Graphene-SERS Platform

The detection of intact cells in their original state is of great importance for early diagnosis of disease. In this work, we report the detection of living K562 chronic myeloid leukemia cells using an innovative label-free hybrid graphene/gold nanopyramid based surface-enhanced Raman scattering (SERS) trough grid method. The position of the cells can be well controlled by the trough grid so that they can remain unchanged in the liquid at room temperature for up to 2 h. The graphene sheet permits SERS hot spot identification and provides a chemical enhancement for the biological constituents. Different states of K562 cells were thus detected. Two types of SERS signals of K562 cells can be clearly distinguished using principal component analysis (PCA), corresponding to two different states of the K562 cell, namely, the living state and dead state. And their SERS signal ratio severely varies as a function of the storage time. PCA analysis shows that, within 2 h after taking K562 cells out of the incubator, living cells account for more than 60% of the total cells. The SERS signal variation has been attributed to an increased number of dead cells suspended in the cell culture fluid. We demonstrate a proof-of-concept study of the clarification of the state variation, from the molecular level, of the K562 cells as a function of time using a graphene-SERS platform. This method is of great potential for leukemia diagnosis and on the development of leukemia drugs

Data_Sheet_1_Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells.pdf

Fast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a deep neural network (DNN) for cell counting and classification of cerebrospinal fluid cytology. The May-Grünwald-Giemsa (MGG) stained image is annotated and input into the DNN network. The main cell types include lymphocytes, monocytes, neutrophils, and red blood cells. In clinical practice, the use of DNN is compared with the results of expert examinations in the professional cerebrospinal fluid room of a First-line 3A Hospital. The results show that the report produced by the DNN network is more accurate, with an accuracy of 95% and a reduction in turnaround time by 86%. This study shows the feasibility of applying DNN to clinical cerebrospinal fluid cytology.</p

Multiwavelength High-Detectivity MoS₂ Photodetectors with Schottky Contacts

Photodetection is one of the vital functions for the multifunctional “More than Moore” (MtM) microchips urgently required by Internet of Things (IoT) and artificial intelligence (AI) applications. The further improvement of the performance of photodetectors faces various challenges, including materials, fabrication processes, and device structures. We demonstrate in this work MoS2 photodetectors with a nanoscale channel length and a back-gate device structure. With the mechanically exfoliated six-monolayer-thick MoS2, a Schottky contact between source/drain electrodes and MoS2, a high responsivity of 4.1 × 103 A W–1, and a detectivity of 1.34 × 1013 cm Hz1/2 W–1 at 650 nm were achieved. The devices are also sensitive to multiwavelength lights, including 520 and 405 nm. The electrical and optoelectronic properties of the MoS2 photodetectors were studied in depth, and the working mechanism of the devices was analyzed. The photoinduced Schottky barrier lowering (PIBL) was found to be important for the high performance of the phototransistor

Large-Area and Clean Graphene Transfer on Gold-Nanopyramid-Structured Substrates: Implications for Surface-Enhanced Raman Scattering Detection

The transfer of large-area and clean graphene to arbitrary substrates, especially to those with raised nanostructures, represents a great challenge. Polymer-based supporting layers generally lead to organic residues, while graphene transfer using alternative supporting materials like paraffin suffers from breaking and thus limits the transfer area. We demonstrated an improved poly­(methyl methacrylate) (PMMA)/paraffin double layer, enabling the large-area transfer of graphene with high cleanliness and high coverage (81%) onto gold nanopyramid (AuNP)-structured substrates. The impact of supporting layers including single PMMA or paraffin and mixed PMMA/paraffin was clarified. The properties of graphene on AuNPs were theoretically and experimentally examined in detail. Raman spectra show a polarization-dependent D peak due to the folding of large-curvature graphene. The graphene on AuNPs shows a slightly tensile strain and provides extra surface-enhanced Raman scattering (SERS) with an enhancement factor of ∼20 times. These findings open a pathway to extend the applications of transferred graphene on raised nanostructures in many fields, such as SERS detection, catalysis, biosensors, light-emitting diodes, solar cells, and advanced transparent conductors

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