Sichuan University of Science and Engineering

BACKGROUND/AIMS: To study the potential role of the 78kDa glucose regulated protein (GRP78) in the pathogenesis of acute pancreatitis (AP) in vitro. METHODOLOGY: AR42J cells were stimulated by cerulein or cerulein plus lipoplysaccharide (LPS). The severity of pancreatic inflammation was evaluated by amylase, lipase, TNF-a, and IL-6. Apoptosis was determined by flow cytometry; the expressions of apoptotic genes, GRP78 and the downstream molecules were determined by real-time quantitative PCR and Western blot. RESULTS: After cerulein stimulation, the levels of amylase, lipase, TNF-a and IL-6 were all increased, with a more pronounced increase after cerulein plus LPS stimulation. Apoptosis was different in two cell models, high apoptosis in cerulein group; whereas cerulein plus LPS induced relatively less apoptosis. Apoptotic gene expressions revealed more pronounced increase in the cerulein group than those in cerulein plus LPS group. The expressions of GRP78 and downstream molecules were different in two cell models. GRP78 expression was down-regulated in cerulein group and upregulated in cerulein plus LPS group. CONCLUSIONS: GRP78 expression was associated with apoptosis and the severity of cerulein-induced pancreatic inflammation, indicating that GRP78 might prevent apoptosis in pancreatic acinar cells thereby deteriorating the severity of AP.

The present study provides an overview of the coronavirus disease 2019 (COVID-19) outbreak which has rapidly extended globally within a short period. COVID-19 is a highly infectious respiratory disease caused by a new coronavirus known as SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2). SARS-CoV-2 is different from usual coronaviruses responsible for mild sickness such as common cold among human beings. It is crucial to understand the impact and outcome of this pandemic. We therefore overview the changes in the curves of COVID-19 confirmed cases and fatality rate in China and outside of China from 31st of December 2019 to 25th of March 2020. We also aimed to assess the temporal developments and death rate of COVID-19 in China and worldwide. More than 414,179 confirmed cases of COVID-19 have been reported in 197 countries, including 81,848 cases in China and 332,331 outside of China. Furthermore, 18,440 infected patients died from COVID-19 infection; 3,287 cases were from China and 15,153 fatalities were reported worldwide. Among the worldwide infected cases, 113,802 patients have been recovered and discharged from different hospitals. Effective prevention and control measures should be taken to control the disease. The presented Chinese model (protocol) of disease prevention and control could be utilized in order to curb the pandemic situation.

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

We report here a simple, high-yield yet low-cost approach to design single-layer MoS2 nanosheets with controllable size via an improved oleum treatment exfoliation process. By decorating MoS2 nanosheets with chitosan, these functionalized MoS2 nanosheets have been developed as a chemotherapeutic drug nanocarrier for near-infrared (NIR) photothermal-triggered drug delivery, facilitating the combination of chemotherapy and photothermal therapy into one system for cancer therapy. Loaded doxorubicin could be controllably released upon the photothermal effect induced by 808 nm NIR laser irradiation. In vitro and in vivo tumor ablation studies demonstrate a better synergistic therapeutic effect of the combined treatment, compared with either chemotherapy or photothermal therapy alone. Finally, MoS2 nanosheets can also be used as a promising contrast agent in X-ray computed tomography imaging due to the obvious X-ray absorption ability of Mo. As a result, the high-throughput oleum treatment exfoliation process could be extended for fabricating other 2D nanomaterials, and the NIR-triggered drug release strategy was encouraging for simultaneous imaging-guided cancer theranostic application.

Persulfates activation by carbon nanotubes (CNT) has been evidenced as nonradical systems for oxidation of organic pollutants. Peroxymonosulfate (PMS) and peroxydisulfate (PDS) possess discrepant atomic structures and redox potentials, while the nature of their distinct behaviors in carbocatalytic activation has not been investigated. Herein, we illustrated that the roles of nitrogen species in CNT-based persulfate systems are intrinsically different. In PMS activation mediated by nitrogen-doped CNT (N-CNT), surface chemical modification (N-doping) can profoundly promote the adsorption quantity of PMS, consequently elevate potential of derived nonradical N-CNT-PMS* complexes, and boost organic oxidation efficiency via an electron-transfer regime. In contrast, PDS adsorption was not enhanced upon incorporating N into CNT due to the limited equilibrium adsorption quantity of PDS, leading to a relatively lower oxidative potential of PDS/N-CNT system and a mediocre degradation rate. However, with equivalent persulfate adsorption on N-CNT at a low quantity, PDS/N-CNT exhibited a stronger oxidizing capacity than PMS/N-CNT because of the intrinsic higher redox potential of PDS than PMS. The oxidation rates of the two systems were in great linearity with the potentials of carbon-persulfate* complexes, suggesting N-CNT activation of PMS and PDS shared the similar electron-transfer oxidation mechanism. Therefore, this study provides new insights into the intrinsic roles of heteroatom doping in nanocarbons for persulfates activation and unveils the principles for a rational design of reaction-oriented carbocatalysts for persulfate-based advanced oxidation processes.

Photocatalytic hydrogen production from water offers an abundant, clean fuel source, but it is challenging to produce photocatalysts that use the solar spectrum effectively. Many hydrogen-evolving photocatalysts are active in the ultraviolet range, but ultraviolet light accounts for only 3% of the energy available in the solar spectrum at ground level. Solid-state crystalline photocatalysts have light absorption profiles that are a discrete function of their crystalline phase and that are not always tunable. Here, we prepare a series of amorphous, microporous organic polymers with exquisite synthetic control over the optical gap in the range 1.94-2.95 eV. Specific monomer compositions give polymers that are robust and effective photocatalysts for the evolution of hydrogen from water in the presence of a sacrificial electron donor, without the apparent need for an added metal cocatalyst. Remarkably, unlike other organic systems, the best performing polymer is only photoactive under visible rather than ultraviolet irradiation.

Background In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer‐assisted drug design to predict the binding affinity and analyze the interactive mode. Results This study introduces the key principles, procedures and the widely‐used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://github.com/XLearning-SCU/2022-CVPR-AirNet.

Nanodiamonds (NDs), as a new member of the carbon nanoparticles family, have attracted more and more attention in biomedicine recently due to their excellent physical and chemical properties. This paper summarizes the main results from the in vitro and in vivo safety assessments of NDs and reports the application of NDs in the development of drug delivery systems. In view of the NDs' characteristics of easy formation of a porous cluster structure in solution, an adsorption model for a variety of functional molecules on the ND clusters is proposed, which provides new ideas for developing a novel smart drug with various features such as sustained-release, targeting, and fluorescence imaging.

BACKGROUND/AIMS: To investigate the effectiveness and safety of central pancreatectomy. METHODOLOGY: We retrospectively studied 44 cases that underwent central pancreatectomy (CP), 55 patients who underwent distal pancreatectomy (DP), and 62 patients who underwent pancreatoduodenectomy (PD) for their benign or borderline pancreatic lesions; as well as the different management styles for pancreatic stumps in CP. RESULTS: The duration of surgery and length of hospital stay were shorter in the CP group than that of PD group, and blood loss was also less in CP group. There were no differences between the CP and DP groups in duration of surgery, length of hospital stay, and blood loss. The incidence of common surgical complications was higher in the PD group. There were more pancreatic fistulas (grade B/C) in CP and PD groups compared to that of the DP group. New onset or worsening of diabetes occurred only in the CP and PD groups at 4.8% and 10.9%, respectively. A pancreaticogastrostomy for distal pancreatic stumps reduced the incidence of pancreatic fistula (p=0.038). Duct-to-mucosa anastomosis had less pancreatic fistula than invagination anastomosis (p=0.017). There was no difference in incidence of pancreatic fistula between pancreaticojejunostomy and oversewing of proximal pancreatic stumps (p=0.601). CONCLUSIONS: CP is an available and safe operation for benign or borderline lesions located in the pancreatic neck. A pancreaticogastrostomy for distal pancreatic stumps or duct-to-mucosa anastomosis may reduce the risk of pancreatic fistula.

The orthogonal-signal-generator-based phase-locked loops (OSG-PLLs) are among the most popular single-phase PLLs within the areas of power electronics and power systems, mainly because they are often easy to be implemented and offer a robust performance against the grid disturbances. The main aim of this paper is to present a survey of the comparative performance evaluation among the state-of-the-art OSG-PLLs (include Delay-PLL, Deri-PLL, Park-PLL, SOGI-PLL, DOEC-PLL, VTD-PLL, CCF-PLL, and TPFA-PLL) under different grid disturbances such as voltage sags, phase, and frequency jumps, and in the presence of dc offset, harmonic components, and white noise in their input. This analysis provides a useful insight about the advantages and disadvantages of these PLLs. The performance enhancement of Delay-PLL, Deri-PLL, and CCF-PLL by including a moving average filter into their structure is another goal of this paper.

Urbanization is a continuous process for a city’s economic development. Though rapid urbanization provides a huge employment opportunity for people, urban threats also increase proportionately due to natural and man-made hazards. Understanding urban resilience and sustainability is an urgent matter to face hazards in the rapidly urbanized world. Therefore, this study aims to clarify the concept and develop key indications of urban resilience and sustainability from the existing literature. A systematic literature review guided by PRISMA has been conducted using literature from 1 January 2001 to 30 November 2021. It argues that sustainability and resilience are interrelated paradigms that emphasize a system’s capacity to move toward desirable development paths. Resilience and sustainability are fundamentally concerned with preserving societal health and well-being within the context of a broader framework of environmental change. There are significant differences in their emphasis and time scales, particularly in the context of urbanization. This study has identified key indicators of urban resilience under three major components like adaptive capacity (education, health, food, and water), absorptive capacity (community support, urban green space, protective infrastructure, access to transport), and transformative capacity (communication technology, collaboration of multi-stakeholders, emergency services of government, community-oriented urban planning). This study also identified several indicators under major dimensions (social, economic, and environmental) of urban sustainability. The findings will be fruitful in understanding the dynamics of urban vulnerability and resilience and its measurement and management strategy from developed indicators.

Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

This research aimed to investigate effect of transformational leadership (TL) in stimulating employee creativity (EC) leading to organizational innovation (OI) and assess the moderating role of Intrinsic Motivation (IM) between TL and EC. The data was collected from 164 supervisor-employee dyads of 31 Pakistani software firms. A three-step approach to SEM was applied to examine mediator and moderator variables. The findings indicate that the Idealized Influence, Intellectual Stimulation, and Inspirational Motivation have a great influence on OI and EC but Individual Consideration was not found to be significantly related to OI and EC. The findings further proved the positive and significant moderating effect of IM for TL and EC. Thus, through TL style, EC can be promoted with the interactive effect of IM by inspiring employees to think out of the box leading to innovation in the organization. This paper attempts to contribute to the current literature by developing moderating role of IM. Furthermore, we have also examined how each dimension of TL is affecting EC, and OI, further some interesting discoveries are also made which other studies have not done before.

Bioceramics, because of its excellent biocompatible and mechanical properties, has always been considered as the most promising materials for hard tissue repair. It is well know that an appropriate cellular response to bioceramics surfaces is essential for tissue regeneration and integration. As the in vivo implants, the implanted bioceramics are immediately coated with proteins from blood and body fluids, and it is through this coated layer that cells sense and respond to foreign implants. Hence, the adsorption of proteins is critical within the sequence of biological activities. However, the biological mechanisms of the interactions of bioceramics and proteins are still not well understood. In this review, we will recapitulate the recent studies on the bioceramic-protein interactions.

Virtually all organosulfur compounds react with Fe(0) carbonyls to give the title complexes. These reactions are reviewed in light of major advances over the past few decades, spurred by interest in Fe2(μ-SR)2(CO)x centers at the active sites of the [FeFe]-hydrogenase enzymes. The most useful synthetic route to Fe2(μ-SR)2(CO)6 involves the reaction of thiols with Fe2(CO)9 and Fe3(CO)12. Such reactions can proceed via mono-, di-, and triiron intermediates. The reactivity of Fe(0) carbonyls toward thiols is highly chemoselective, and the resulting dithiolato complexes are fairly rugged. Thus, many complexes tolerate further synthetic elaboration directed at the organic substituents. A second major route involves alkylation of Fe2(μ-S2)(CO)6, Fe2(μ-SH)2(CO)6, and Li2Fe2(μ-S)2(CO)6. This approach is especially useful for azadithiolates Fe2[(μ-SCH2)2NR](CO)6. Elaborate complexes arise via addition of the FeSH group to electrophilic alkenes, alkynes, and carbonyls. Although the first example of Fe2(μ-SR)2(CO)6 was prepared from ferrous reagents, ferrous compounds are infrequently used, although the Fe(II)(SR)2 + Fe(0) condensation reaction is promising. Almost invariably low-yielding, the reaction of Fe3(CO)12, S8, and a variety of unsaturated substrates results in C-H activation, affording otherwise inaccessible derivatives. Thiones and related C═S-containing reagents are highly reactive toward Fe(0), often giving complexes derived from substituted methanedithiolates and C-H activation.

Oncolytic viruses (OVs) are emerging as potentially useful platforms in treatment methods for patients with tumors. They preferentially target and kill tumor cells, leaving healthy cells unharmed. In addition to direct oncolysis, the essential and attractive aspect of oncolytic virotherapy is based on the intrinsic induction of both innate and adaptive immune responses. To further augment this efficacious response, OVs have been genetically engineered to express immune regulators that enhance or restore antitumor immunity. Recently, combinations of OVs with other immunotherapies, such as immune checkpoint inhibitors (ICIs), chimeric antigen receptors (CARs), antigen-specific T-cell receptors (TCRs) and autologous tumor-infiltrating lymphocytes (TILs), have led to promising progress in cancer treatment. This review summarizes the intrinsic mechanisms of OVs, describes the optimization strategies for using armed OVs to enhance the effects of antitumor immunity and highlights rational combinations of OVs with other immunotherapies in recent preclinical and clinical studies.

About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improve the diagnosis process. In this paper, we introduce an expert system that stacks two support vector machine (SVM) models for the effective prediction of HF. The first SVM model is linear and L1regularized. It has the capability to eliminate irrelevant features by shrinking their coefficients to zero. The second SVM model is L2regularized. It is used as a predictive model. To optimize the two models, we propose a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously. The effectiveness of the proposed method is evaluated using six different evaluation metrics: accuracy, sensitivity, specificity, the Matthews correlation coefficient (MCC), ROC charts, and area under the curve (AUC). The experimental results confirm that the proposed method improves the performance of a conventional SVM model by 3.3%. Moreover, the proposed method shows better performance compared to the ten previously proposed methods that achieved accuracies in the range of 57.85%-91.83%. In addition, the proposed method also shows better performance than the other state-of-the-art machine learning ensemble models.

Abstract Many reagents as electron sacrificers have been recently investigated to induce decomposition of permanganate (KMnO4) to produce highly reactive intermediate Mn species toward oxidation of organic contaminants; however, this strategy meanwhile causes low KMnO4 utilization efficiency. This study surprisingly found that graphite can mediate direct electron transfer from organics (e.g., sulfamethoxazole (SMX)) to KMnO4, resulting in high KMnO4 utilization efficiency, rather than reductive sites of graphite-induced conversion of KMnO4 to highly reactive intermediate Mn species. The galvanic oxidation process (GOP) and comparative experiments of different organic contaminants prove that the KMnO4/graphite system mainly extracts electrons from organic contaminants via a one-electron pathway instead of a two-electron pathway. More importantly, the KMnO4/graphite system has superior reusability, graphite can keep a long-lasting reactivity, and the KMnO4 utilization efficiency elevates significantly after each cycle of graphite. The transformation of SMX in the KMnO4/graphite system mainly includes self-coupling, hydroxylation, oxidation, and hydrolytic reaction. The work will improve insights into the electron-transfer mechanism and unveil the advantages of efficient KMnO4 utilization in the KMnO4-based technologies in environmental remediation.