Publications by authors named "D C Guo"

4,999 Publications

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Employing synergetic effect of ZnSe quantum dots and layered Ni(OH)2 to boost the performance of lithium-sulfur cathodes.

Nanotechnology 2021 Sep 23. Epub 2021 Sep 23.

Department of Electronic Engineering, East China Normal University, Dongchuan Road 500, Shanghai 200062, shanghai, 200062, CHINA.

The low sulfur utilization, cycling instability, and sluggish kinetics are the critical obstructions to practical applications of lithium-sulfur batteries (LSBs). Constructing sulfur hosts with high conductivity, suppressed shuttle effect, and rapid kinetics is essential for their practical application in LSBs. Here, we synthetically utilized the merits of ZnSe quantum dots (QDs) and layered Ni(OH)2 to boost the performance of LSBs. A novel core-shell ZnSe-CNTs/[email protected](OH)2 was constructed using the ZnSe-CNTs network as framework to load sulfur and following with Ni(OH)2 encapsulation. The CNT network decorated with ZnSe QDs not only serves as a conductive framework providing fast electron/ion transfer channels, but also limites polysulfide diffusion physically and chemically. Layered Ni(OH)2, the wrinkled encapsulation, not only permits fast electron/ion transfer, but also buffers the expansion, confines active materials, and limits the polysulfide dissolution chemically. When used as a cathode, ZnSe-CNTs/[email protected](OH)2 presents enhanced electrochemistry performance compared with ZnSe-CNTs/S and CNTs/S. The average specific capacity decreases from 1021.9 mAh g-1 at 0.2 C to 665.0 mAh g-1 at 2 C, showing rate capacity much higher than ZnSe-CNTs/S and CNTs/S. After 150 cycles, the capacity at 0.5 C slowly reduces from 926.7 mAh g-1 to 789.0 mAh g-1, showing high retention of 85.1%. Therefore, our investigation provides a new strategy to construct a promising sulfur cathode for LSBs.
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http://dx.doi.org/10.1088/1361-6528/ac2982DOI Listing
September 2021

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

Eur Radiol 2021 Sep 22. Epub 2021 Sep 22.

Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Rd, Yuzhong District, Chongqing, 400010, China.

Objectives: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.

Methods: Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning. VCFs were categorized as fresh or old according to MRI results, and the VCF grade and type were assessed. The raw DR data were sent to InferScholar Center for annotation. A DL-based prediction model was built, and its diagnostic performance was evaluated. The DeLong test was applied to assess differences in ROC curves between different models.

Results: A total of 1877 VCFs in 1099 patients were included in our study and randomly divided into development (n = 824 patients) and test (n = 275 patients) datasets. The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77-0.83), an accuracy of 74% (95% CI, 72-77%), a sensitivity of 80% (95% CI, 77-83%), and a specificity of 68% (95% CI, 63-72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77), and the best performance among respective subgroupings was obtained for grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups.

Conclusion: The proposed DL model achieved adequate performance in identifying fresh VCFs from DR.

Key Points: • The ensemble deep-learning model identified fresh VCFs from DR, reaching an AUC of 0.80, an accuracy of 74%, a sensitivity of 80%, and a specificity of 68% with the reference standard of MRI. • The lateral views (AUC, 0.83) exhibited better performance than anteroposterior views (AUC, 0.77). • The grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups showed the best performance among their respective subgroupings.
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http://dx.doi.org/10.1007/s00330-021-08247-4DOI Listing
September 2021

State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression.

Comput Intell Neurosci 2021 13;2021:9441649. Epub 2021 Sep 13.

Technical Service Site, Beijing Benz Automotive Co. Ltd., Beijing 100176, China.

Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re-examine the research of the equipment-state-prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.
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http://dx.doi.org/10.1155/2021/9441649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452418PMC
September 2021

The role of cystatin C in multiple myeloma.

Int J Lab Hematol 2021 Sep 22. Epub 2021 Sep 22.

Department of Hematology, Affiliated Hospital of Nantong University, Nantong, China.

Introduction: Renal insufficiency is one of the common complications in multiple myeloma (MM) and an independent factor indicating a poor prognosis. Cystatin C (Cys C) is considered to be expected to replace creatinine to calculate glomerular filtration rate due to its own characteristics. Gene expression analysis suggested that cystatin C is up-regulated nearly 50-fold in patients with multiple myeloma.

Methods: To further clarify the role of cystatin C in multiple myeloma, we retrospectively evaluated pretreatment cystatin C levels in 195 newly diagnosed patients through statistical analysis.

Results: The elevation of serum cystatin C was positively related to the elevation of serum creatinine (P < .001), LDH (P = .006), β2-microglobulin (P < .001), bone marrow plasma cell proportion (P = .005) and the reduction of hemoglobin levels (P < .001). Patients with serum cystatin C levels >1.6 mg/L had a significantly shorter progression-free survival (PFS) or overall survival (OS) than patients with serum cystatin C levels <1.6 mg/L (median PFS: median unreached vs 16.7 months, P < .001; median OS: 68 months vs 42 months, P = .014). Although serum cystatin C is not an independent prognostic factor of OS and PFS in patients with multiple myeloma, serum cystatin C can be considered as a sensitive indicator to differentiate well OS and PFS in the group of ISS II patients.

Conclusion: Serum cystatin C is associated with tumor burden of multiple myeloma and cystatin C can further differentiate the prognosis of ISS II patients. More prospective studies are required to explore the role of serum cystatin C in multiple myeloma.
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http://dx.doi.org/10.1111/ijlh.13695DOI Listing
September 2021

Suppression of 4.1R enhances the potency of NKG2D-CAR T cells against pancreatic carcinoma via activating ERK signaling pathway.

Oncogenesis 2021 Sep 21;10(9):62. Epub 2021 Sep 21.

Shanghai Key Laboratory of Regulatory Biology, School of Life Sciences, East China Normal University, 200241, Shanghai, China.

Pancreatic carcinoma (PC) is one of the most common malignancies. Chimeric antigen receptor (CAR)-modified T cells has achieved remarkable efficacy in the treatment of hematological malignancies. However, lack of tumor-specific targets and the existence of inhibitory factors limit the function of CAR T cells when treating solid tumors. 4.1R has been reported to suppress the anti-tumor activity of T cell responses. In this study, we investigated the anti-tumor activity of 4.1R deletion in natural killer group 2D (NKG2D)-CAR T cells against PC. The CAR T cells were obtained by transfecting T cells with lentiviral vector carrying NKG2D-CAR, NC-NKG2D-CAR, or KD2-NKG2D-CAR. In vitro, NKG2D-CAR T cells showed higher cytotoxicity than Mock T cells. However, compared to NKG2D-CAR T cells, furtherly higher cytotoxicity against PC cells in a dose-dependent manner was found in KD2-NKG2D-CAR T cells. In addition, the proliferation rate and cytotoxic activity of KD2-NKG2D-CAR T cells were significantly higher than those of NKG2D-CAR T cells. Besides, the inhibitory receptors PD-1 and TIM-3 were expressed in lower level on KD2-NKG2D-CAR T cells. In vivo, KD2-NKG2D-CAR T cells suppressed tumor growth more effectively in a xenograft model compared to NKG2D-CAR T cells. Mechanistically, 4.1R regulated CAR T cell function via activating ERK signaling pathway. Therefore, the study provides a new idea to enhance the anti-tumor efficiency of CAR T therapy.
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http://dx.doi.org/10.1038/s41389-021-00353-8DOI Listing
September 2021
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