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.

Specnuezhenide suppresses diabetes-induced bone loss by inhibiting RANKL-induced osteoclastogenesis.

Diabetes osteoporosis is a chronic complication of diabetes mellitus (DM) and is associated with osteoclast formation and enhanced bone resorption. Specnuezhenide (SPN) is an active compound with anti-inflammatory and immunomodulatory properties. However, the roles of SPN in diabetic osteoporosis remain unknown. In this study, primary bone marrow macrophages (BMMs) were pretreated with SPN and were stimulated with receptor activator of nuclear factor kappa B ligand (RANKL; 50 ng/mL) to induce osteoclastogenesis. The number of osteoclasts was detected by tartrate-resistant acid phosphatase (TRAP) staining. The protein levels of cellular oncogene fos/nuclear factor of activated T cells c1 (c-Fos/NFATc1), nuclear factor kappa-B (NF-κB), and mitogen-activated protein kinases (MAPKs) were evaluated by western blot analysis. NF-κB luciferase assays were used to examine the role of SPN in NF-κB activation. The DM model group received a high-glucose, high-fat diet and was then intraperitoneally injected with streptozotocin (STZ). Micro-CT scanning, serum biochemical analysis, histological analysis were used to assess bone loss. We found that SPN suppressed RANKL-induced osteoclast formation and that SPN inhibited the expression of osteoclast-related genes and c-Fos/ NFATc1. SPN inhibited RANKL-induced activation of NF-κB and MAPKs. In vivo experiments revealed that SPN suppressed diabetes-induced bone loss and the number of osteoclasts. Furthermore, SPN decreased the levels of bone turnover markers and increased the levels of runt-related transcription factor 2 (RUNX2), osteoprotegerin (OPG), calcium (Ca) and phosphorus (P). SPN also regulated diabetes-related markers. This study suggests that SPN suppresses diabetes-induced bone loss by inhibiting RANKL-induced osteoclastogenesis, and provides an experimental basis for the treatment of diabetic osteoporosis.

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

Background: It 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. Objective: This 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. Method: The 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. Results: With 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. Conclusion: A 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.

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

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.

Crucial Impact of Hydrophilicity on the Self-Assembled 2D Colloidal Crystals Using Langmuir-Blodgett Method.

Large-scale close-packed two-dimensional (2D) colloidal crystal with high coverage is indispensable for various promising applications. The Langmuir-Blodgett (LB) method is a powerful technique to prepare 2D colloidal crystals. However, the self-assembly and movement of microspheres during the whole LB process are less analyzed. In this study, we clarify the crucial impact of hydrophilicity of the microspheres on their self-assembly in the LB process and on the properties of the prepared 2D colloidal crystals. The characteristic surface pressure-area isotherms of the microspheres have been analyzed and adjusted by only counting the quantity of the microspheres on the water surface, which leads to more accurate results. The critical surface pressures for hydrophilic and hydrophobic microspheres are about 61 and 46 mN/m, respectively. The decrease of the surface hydrophilicity of microspheres facilitates their self-assembly on the water surface, which further leads to higher coverage and less defects of the 2D colloidal crystals. A coverage of as high as 97% was obtained using hydrophobic microspheres. Entropy and intersphere capillary forces drive the self-assembly and transportation of the microspheres, respectively. Caused by the diffraction of visible light, opposite contrasts at local adjacent regions on the surface of the 2D colloidal crystals have been observed. The understanding of self-assembly of the microspheres during the LB process paves the way to fabricate the high-quality 2D colloidal crystals for various applications such as photonic papers and inks, stealth materials, biomimetic coatings, and related nanostructures.