Enhance Carrier Diffusion of Monolayer MoSe2 by Interface Engineering.

Two-dimensional materials hold great potentials for beyond-CMOS (complementary metal-oxide-semiconductor) electronical and optoelectrical applications, and the development of field effect transistors (FET) with excellent performance using such materials is of particular interest. How to improve the performance of devices thus becomes an urgent issue. The performance of FETs depends greatly on the intrinsic electrical properties of the channel materials, meanwhile the device interface quality, such as extrinsic scattering of charged impurities, charge traps, and substrate surface roughness have a great influence on the performance. In this paper, the impact of the interface quality on the carrier diffusion behaviors of monolayer (ML) MoSe2 has been investigated by using an in situ ultrafast laser technique to avoid the surface contamination during device fabrication process. Two types of self-assembled monolayers (SAMs) are introduced to modify the gate dielectric surface through an interface engineering approach to obtain chemical-stable interfaces. The results showed that the transport properties of ML MoSe2 were enhanced after interface engineering, for example, the carrier mobility of ML MoSe2 was improved from ∼59.4 to ∼166.5 cm2 V-1 s-1 after the SAM modification. Meanwhile, the photocarrier dynamics of ML MoSe2 before and after interfacial engineering were also carefully studied. Our studies provide a feasible method for improving the carrier diffusion behaviors of such materials, and making them suited for application in future integrated circuit.

M-Ni-Co MOF (M=Zn, Fe, Mn) for high-performance supercapacitors by adjusting its morphology.

Metal-organic frameworks (MOF) have been wildly synthesised and studied as electrode materials for supercapacitors, and bimetallic MOF of Ni and Co has been broadly studied to enhance both specific capacitance and stability of supercapacitors. Herein, a best performance (about 320 F/g) of Ni-Co bimetallic MOF was found in a uniform preparation condition by adjusting the ratio of Ni to Co. Then tiny third metal ion was introduced, and we found that the morphology of material has a significant change on the original basis. Furthermore, certain ions (Zn, Fe, Mn) introduced make a huge improvement in capacitance based on Ni-Co MOF of 320 F/g. The result shows that Zn-Ni-Co MOF, Fe-Ni-Co MOF and Mn-Ni-Co MOF perform specific capacitance of 1135 F/g, 870 F/g and 760F/g at 1 A/g, respectively. Meanwhile, the asymmetric supercapacitor (ASC) was constructed by Zn-Ni-Co MOF as positive electrode and active carbon (AC) as negative electrode. The Zn-Ni-Co MOF//AC ASC possesses a energy density of 58 Wh/kg at a power density of 775 W/kg. This research provides a new methods to regulate the morphology of MOF and a novel viewpoint for assembling high-performance, low-price, and eco-friendly green energy storage devices.

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

Opposite regulation effects of Al3+ on different types of carbon quantum dots and potential applications in information encryption.

Regulating the photoluminescence (PL) of carbon quantum dots (CQDs) through ion modification is a well-established and effective approach. Herein, we report the opposite regulation effects of Al3+ ions on the PL properties of two distinct types of CQDs (graphene quantum dots, GQDs, and nitrogen-doped carbon quantum dots of 2,3-diaminophenazine, DAP), and elucidate the underlying mechanism of the binding of Al3+ ions to different PL sites on CQDs by employing ultraviolet-visible spectroscopy, X-ray photoelectron spectroscopy, and density functional theory calculations. Specifically, Al3+ ions are primarily situated around the oxygen-containing groups, which do not impact the π-π regions of GQDs. However, Al3+ ions are preferentially adsorbed on the top of pyridine nitrogen in the phenazine rings of DAP, thus reducing the PL regions of DAP. Based on the opposite PL effects of Al3+ on GQDs and DAP, we explore potential applications of information encryption and successfully realize multi-level information encryption and decryption, which may provide new strategies for CQDs in information security.

Bioinformatics Identification of Regulatory Genes and Mechanism Related to Hypoxia-Induced PD-L1 Inhibitor Resistance in Hepatocellular Carcinoma.

The combination of a PD-L1 inhibitor and an anti-angiogenic agent has become the new reference standard in the first-line treatment of non-excisable hepatocellular carcinoma (HCC) due to the survival advantage, but its objective response rate remains low at 36%. Evidence shows that PD-L1 inhibitor resistance is attributed to hypoxic tumor microenvironment. In this study, we performed bioinformatics analysis to identify genes and the underlying mechanisms that improve the efficacy of PD-L1 inhibition. Two public datasets of gene expression profiles, (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6), were collected from Gene Expression Omnibus (GEO) database. We identified HCC-signature and hypoxia-related genes, using differential expression analysis, and their 52 overlapping genes. Of these 52 genes, 14 PD-L1 regulator genes were further identified through the multiple regression analysis of TCGA-LIHC dataset (N = 371), and 10 hub genes were indicated in the protein-protein interaction (PPI) network. It was found that POLE2, GABARAPL1, PIK3R1, NDC80, and TPX2 play critical roles in the response and overall survival in cancer patients under PD-L1 inhibitor treatment. Our study provides new insights and potential biomarkers to enhance the immunotherapeutic role of PD-L1 inhibitors in HCC, which can help in exploring new therapeutic strategies.

Interfacial Coupling and Modulation of van der Waals Heterostructures for Nanodevices.

In recent years, van der Waals heterostructures (vdWHs) of two-dimensional (2D) materials have attracted extensive research interest. By stacking various 2D materials together to form vdWHs, it is interesting to see that new and fascinating properties are formed beyond single 2D materials; thus, 2D heterostructures-based nanodevices, especially for potential optoelectronic applications, were successfully constructed in the past few decades. With the dramatically increased demand for well-controlled heterostructures for nanodevices with desired performance in recent years, various interfacial modulation methods have been carried out to regulate the interfacial coupling of such heterostructures. Here, the research progress in the study of interfacial coupling of vdWHs (investigated by Photoluminescence, Raman, and Pump-probe spectroscopies as well as other techniques), the modulation of interfacial coupling by applying various external fields (including electrical, optical, mechanical fields), as well as the related applications for future electrics and optoelectronics, have been briefly reviewed. By summarizing the recent progress, discussing the recent advances, and looking forward to future trends and existing challenges, this review is aimed at providing an overall picture of the importance of interfacial modulation in vdWHs for possible strategies to optimize the device's performance.

Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer.

BACKGROUND: Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited. METHODS: Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People's Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO). RESULTS: The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar's test p = 0.0003). CONCLUSIONS: A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.

Charge Transfer Properties of Heterostructures Formed by Bi2 O2 Se and Transition Metal Dichalcogenide Monolayers.

Atomically thin bismuth oxyselenide (Bi2 O2 Se) exhibits attractive properties for electronic and optoelectronic applications, such as high charge-carrier mobility and good air stability. Recently, the development of Bi2 O2 Se-based heterostructures have attracted enormous interests with promising prospects for diverse device applications. Although the electrical properties of Bi2 O2 Se-based heterostructures have been widely studied, the interlayer charge transfer in these heterostructures remains elusive, despite its importance in harnessing their emergent functionalities. Here, a comprehensive experimental investigation on the interlayer charge transfer properties of two heterostructures formed by Bi2 O2 Se and representative transition metal dichalcogenides (namely, WS2 /Bi2 O2 Se and MoS2 /Bi2 O2 Se) is reported. Kelvin probe force microscopy is used to measure the work functions of the samples, which are further employed to establish type-II band alignment of both heterostructures. Photoluminescence quenching is observed in each heterostructure, suggesting high charge transfer efficiency. Time-resolved and layer-selective pump-probe measurements further prove the ultrafast interlayer charge transfer processes and formation of long-lived interlayer excitons. These results establish the feasibility of integrating 2D Bi2 O2 Se with other 2D semiconductors to fabricate heterostructures with novel charge transfer properties and provide insight for understanding the performance of optoelectronic devices based on such 2D heterostructures.

Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography.

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.

Discovery of novel N-aryl pyrrothine derivatives as bacterial RNA polymerase inhibitors.

Bacterial RNA polymerase (RNAP) is a validated drug target for broad-spectrum antibiotics, and its "switch region" is considered as the promising binding site for novel antibiotics. Based on the core scaffold of dithiolopyrrolone, a series of N-aryl pyrrothine derivatives was designed, synthesized, and evaluated for their antibacterial activity. Compounds generally displayed more active against Gram-positive bacteria, but less against Gram-negative bacteria. Among them, compound 6e exhibited moderate antibacterial activity against clinical isolates of rifampin-resistant Staphylococcus aureus with minimum inhibition concentration value of 1-2 µg/ml and inhibited Escherichia coli RNAP with IC50 value of 12.0 ± 0.9 µM. In addition, compound 6e showed certain degree of cytotoxicity against HepG2 and LO2 cells. Furthermore, molecular docking studies suggested that compound 6e might interact with the switch region of bacterial RNAP in a similar conformation to myxopyronin A. Together, the N-aryl pyrrothine scaffold is a promising lead for discovery of antibacterial drugs acting against bacterial RNAP.

Design, synthesis and biological evaluation of novel hybrids of N-aryl pyrrothine-base α-pyrone as bacterial RNA polymerase inhibitors.

Antibiotic resistance in bacteria has been an emerging public health problem, thus discovery of novel and effective antibiotics is urgent. A series of novel hybrids of N-aryl pyrrothine-base α-pyrone hybrids was designed, synthesized and evaluated as bacterial RNA polymerase (RNAP) inhibitors. Among them, compound 13c exhibited potent antibacterial activity against antibiotic-resistant S. aureus with the minimum inhibitory concentration (MIC) in the range of 1-4 µg/mL. Moreover, compound 13c exhibited strong inhibitory activity against E.coli RNAP with IC50 value of 16.06 µM, and cytotoxicity in HepG2 cells with IC50 value of 7.04 µM. The molecular docking study further suggested that compound 13c binds to the switch region of bacterial RNAP. In summary, compound 13c is a novel bacterial RNAP inhibitor, and a promising lead compound for further optimization.

Synthesis and biological evaluation of novel SIPI-7623 derivatives as farnesoid X receptor (FXR) antagonists.

Most of reported steroidal FXR antagonists are restricted due to low potency. We described the design and synthesis of novel nonsteroidal scaffold SIPI-7623 derivatives as FXR antagonists. The most potent compound A-11 (IC50 = 7.8 ± 1.1 µM) showed better activity compared to SIPI-7623 (IC50 = 40.8 ± 1.7 µM) and guggulsterone (IC50 = 45.9 ± 1.1 µM). Docking of A-11 in FXR's ligand-binding domain was also studied.