Southern University of Science and Technology

ABSTRACT: The current International Association for the Study of Pain (IASP) definition of pain as "An unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage" was recommended by the Subcommittee on Taxonomy and adopted by the IASP Council in 1979. This definition has become accepted widely by health care professionals and researchers in the pain field and adopted by several professional, governmental, and nongovernmental organizations, including the World Health Organization. In recent years, some in the field have reasoned that advances in our understanding of pain warrant a reevaluation of the definition and have proposed modifications. Therefore, in 2018, the IASP formed a 14-member, multinational Presidential Task Force comprising individuals with broad expertise in clinical and basic science related to pain, to evaluate the current definition and accompanying note and recommend whether they should be retained or changed. This review provides a synopsis of the critical concepts, the analysis of comments from the IASP membership and public, and the committee's final recommendations for revisions to the definition and notes, which were discussed over a 2-year period. The task force ultimately recommended that the definition of pain be revised to "An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage," and that the accompanying notes be updated to a bulleted list that included the etymology. The revised definition and notes were unanimously accepted by the IASP Council early this year.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. OBJECTIVE: To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status. METHODS: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities. RESULTS: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (1.61-4.17) among patients with two or more comorbidities. CONCLUSION: Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks.

Electricity-driven water splitting can facilitate the storage of electrical energy in the form of hydrogen gas. As a half-reaction of electricity-driven water splitting, the oxygen evolution reaction (OER) is the major bottleneck due to the sluggish kinetics of this four-electron transfer reaction. Developing low-cost and robust OER catalysts is critical to solving this efficiency problem in water splitting. The catalyst design has to be built based on the fundamental understanding of the OER mechanism and the origin of the reaction overpotential. In this article, we summarize the recent progress in understanding OER mechanisms, which include the conventional adsorbate evolution mechanism (AEM) and lattice-oxygen-mediated mechanism (LOM) from both theoretical and experimental aspects. We start with the discussion on the AEM and its linked scaling relations among various reaction intermediates. The strategies to reduce overpotential based on the AEM and its derived descriptors are then introduced. To further reduce the OER overpotential, it is necessary to break the scaling relation of HOO* and HO* intermediates in conventional AEM to go beyond the activity limitation of the volcano relationship. Strategies such as stabilization of HOO*, proton acceptor functionality, and switching the OER pathway to LOM are discussed. The remaining questions on the OER and related perspectives are also presented at the end.

BACKGROUND: The novel coronavirus SARS-CoV-2 is a newly emerging virus. The antibody response in infected patients remains largely unknown, and the clinical value of antibody testing has not been fully demonstrated. METHODS: 173 patients with SARS-CoV-2 infection were enrolled. Their serial plasma samples (n = 535) collected during hospitalization were tested for total antibodies (Ab), IgM, and IgG against SARS-CoV-2. The dynamics of antibodies with disease progress were analyzed. RESULTS: Among 173 patients, the seroconversion rates for Ab, IgM, and IgG were 93.1%, 82.7%, and 64.7%, respectively. The reason for the negative antibody findings in 12 patients might be due to the lack of blood samples at the later stage of illness. The median seroconversion times for Ab, IgM, and then IgG were days 11, 12, and 4, respectively. The presence of antibodies was <40% among patients within 1 week of onset, and rapidly increased to 100.0% (Ab), 94.3% (IgM), and 79.8% (IgG) by day 15 after onset. In contrast, RNA detectability decreased from 66.7% (58/87) in samples collected before day 7 to 45.5% (25/55) during days 15-39. Combining RNA and antibody detection significantly improved the sensitivity of pathogenic diagnosis for COVID-19 (P < .001), even in the early phase of 1 week from onset (P = .007). Moreover, a higher titer of Ab was independently associated with a worse clinical classification (P = .006). CONCLUSIONS: Antibody detection offers vital clinical information during the course of SARS-CoV-2 infection. The findings provide strong empirical support for the routine application of serological testing in the diagnosis and management of COVID-19 patients.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Importance: Coronavirus disease 2019 (COVID-19) is a pandemic with no specific therapeutic agents and substantial mortality. It is critical to find new treatments. Objective: To determine whether convalescent plasma transfusion may be beneficial in the treatment of critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design, Setting, and Participants: Case series of 5 critically ill patients with laboratory-confirmed COVID-19 and acute respiratory distress syndrome (ARDS) who met the following criteria: severe pneumonia with rapid progression and continuously high viral load despite antiviral treatment; Pao2/Fio2 <300; and mechanical ventilation. All 5 were treated with convalescent plasma transfusion. The study was conducted at the infectious disease department, Shenzhen Third People's Hospital in Shenzhen, China, from January 20, 2020, to March 25, 2020; final date of follow-up was March 25, 2020. Clinical outcomes were compared before and after convalescent plasma transfusion. Exposures: Patients received transfusion with convalescent plasma with a SARS-CoV-2-specific antibody (IgG) binding titer greater than 1:1000 (end point dilution titer, by enzyme-linked immunosorbent assay [ELISA]) and a neutralization titer greater than 40 (end point dilution titer) that had been obtained from 5 patients who recovered from COVID-19. Convalescent plasma was administered between 10 and 22 days after admission. Main Outcomes and Measures: Changes of body temperature, Sequential Organ Failure Assessment (SOFA) score (range 0-24, with higher scores indicating more severe illness), Pao2/Fio2, viral load, serum antibody titer, routine blood biochemical index, ARDS, and ventilatory and extracorporeal membrane oxygenation (ECMO) supports before and after convalescent plasma transfusion. Results: All 5 patients (age range, 36-65 years; 2 women) were receiving mechanical ventilation at the time of treatment and all had received antiviral agents and methylprednisolone. Following plasma transfusion, body temperature normalized within 3 days in 4 of 5 patients, the SOFA score decreased, and Pao2/Fio2 increased within 12 days (range, 172-276 before and 284-366 after). Viral loads also decreased and became negative within 12 days after the transfusion, and SARS-CoV-2-specific ELISA and neutralizing antibody titers increased following the transfusion (range, 40-60 before and 80-320 on day 7). ARDS resolved in 4 patients at 12 days after transfusion, and 3 patients were weaned from mechanical ventilation within 2 weeks of treatment. Of the 5 patients, 3 have been discharged from the hospital (length of stay: 53, 51, and 55 days), and 2 are in stable condition at 37 days after transfusion. Conclusions and Relevance: In this preliminary uncontrolled case series of 5 critically ill patients with COVID-19 and ARDS, administration of convalescent plasma containing neutralizing antibody was followed by improvement in their clinical status. The limited sample size and study design preclude a definitive statement about the potential effectiveness of this treatment, and these observations require evaluation in clinical trials.

Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.

Thermoelectric technology, harvesting electric power directly from heat, is a promising environmentally friendly means of energy savings and power generation. The thermoelectric efficiency is determined by the device dimensionless figure of merit ZT(dev), and optimizing this efficiency requires maximizing ZT values over a broad temperature range. Here, we report a record high ZT(dev) ∼1.34, with ZT ranging from 0.7 to 2.0 at 300 to 773 kelvin, realized in hole-doped tin selenide (SnSe) crystals. The exceptional performance arises from the ultrahigh power factor, which comes from a high electrical conductivity and a strongly enhanced Seebeck coefficient enabled by the contribution of multiple electronic valence bands present in SnSe. SnSe is a robust thermoelectric candidate for energy conversion applications in the low and moderate temperature range.

Macrophages exist in various tissues, several body cavities, and around mucosal surfaces and are a vital part of the innate immune system for host defense against many pathogens and cancers. Macrophages possess binary M1/M2 macrophage polarization settings, which perform a central role in an array of immune tasks via intrinsic signal cascades and, therefore, must be precisely regulated. Many crucial questions about macrophage signaling and immune modulation are yet to be uncovered. In addition, the clinical importance of tumor-associated macrophages is becoming more widely recognized as significant progress has been made in understanding their biology. Moreover, they are an integral part of the tumor microenvironment, playing a part in the regulation of a wide variety of processes including angiogenesis, extracellular matrix transformation, cancer cell proliferation, metastasis, immunosuppression, and resistance to chemotherapeutic and checkpoint blockade immunotherapies. Herein, we discuss immune regulation in macrophage polarization and signaling, mechanical stresses and modulation, metabolic signaling pathways, mitochondrial and transcriptional, and epigenetic regulation. Furthermore, we have broadly extended the understanding of macrophages in extracellular traps and the essential roles of autophagy and aging in regulating macrophage functions. Moreover, we discussed recent advances in macrophages-mediated immune regulation of autoimmune diseases and tumorigenesis. Lastly, we discussed targeted macrophage therapy to portray prospective targets for therapeutic strategies in health and diseases.

Abstract Photocatalysis has been widely applied in various areas, such as solar cells, water splitting, and pollutant degradation. Therefore, the photochemical mechanisms and basic principles of photocatalysis, especially TiO 2 photocatalysis, have been extensively investigated by various surface science methods in the last decade, aiming to provide important information for TiO 2 photocatalysis under real environmental conditions. Recent progress that provides fundamental insights into TiO 2 photocatalysis at a molecular level is highlighted. Insights into the structures of TiO 2 and the basic principles of TiO 2 photocatalysis are discussed first, which provides the basic concepts of TiO 2 photocatalysis. Following this, details of the photochemistry of three important molecules (oxygen, water, methanol) on the model TiO 2 surfaces are presented, in an attempt to unravel the relationship between charge/energy transfer and bond breaking/forming in TiO 2 photocatalysis. Lastly, challenges and opportunities of the mechanistic studies of TiO 2 photocatalysis at the molecular level are discussed briefly, as well as possible photocatalysis models.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Colorectal cancer, a commonly diagnosed cancer in the elderly, often develops slowly from benign polyps called adenoma. The gut microbiota is believed to be directly involved in colorectal carcinogenesis. The identity and functional capacity of the adenoma- or carcinoma-related gut microbe(s), however, have not been surveyed in a comprehensive manner. Here we perform a metagenome-wide association study (MGWAS) on stools from advanced adenoma and carcinoma patients and from healthy subjects, revealing microbial genes, strains and functions enriched in each group. An analysis of potential risk factors indicates that high intake of red meat relative to fruits and vegetables appears to associate with outgrowth of bacteria that might contribute to a more hostile gut environment. These findings suggest that faecal microbiome-based strategies may be useful for early diagnosis and treatment of colorectal adenoma or carcinoma.

Abstract Background Since December 2019, acute respiratory disease (ARD) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. Methods We extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD from 552 hospitals in 31 provinces/provincial municipalities through January 29 th , 2020. Results The median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%). Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P &lt;0.001). Lymphopenia was observed in 82.1% of patients. 55 patients (5.00%) were admitted to intensive care unit and 15 (1.36%) succumbed. Severe pneumonia was independently associated with either the admission to intensive care unit, mechanical ventilation, or death in multivariate competing-risk model (sub-distribution hazards ratio, 9.80; 95% confidence interval, 4.06 to 23.67). Conclusions The 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes.

The Fenton-like process presents one of the most promising strategies to generate reactive oxygen-containing radicals to deal with the ever-growing environmental pollution. However, developing improved catalysts with adequate activity and stability is still a long-term goal for practical application. Herein, we demonstrate single cobalt atoms anchored on porous N-doped graphene with dual reaction sites as highly reactive and stable Fenton-like catalysts for efficient catalytic oxidation of recalcitrant organics via activation of peroxymonosulfate (PMS). Our experiments and density functional theory (DFT) calculations show that the CoN4 site with a single Co atom serves as the active site with optimal binding energy for PMS activation, while the adjacent pyrrolic N site adsorbs organic molecules. The dual reaction sites greatly reduce the migration distance of the active singlet oxygen produced from PMS activation and thus improve the Fenton-like catalytic performance.

There is currently an outbreak of respiratory disease caused by a novel coronavirus. The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it causes has been named coronavirus disease 2019 (COVID-19). More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was 1%–2%. No specific treatment has been reported. Herein, we examined the effects of favipiravir (FPV) versus lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million international unit (IU) twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million IU twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance median time was found for the FPV arm versus the control arm (4 d (interquartile range (IQR): 2.5–9) versus 11 d (IQR: 8–13), P < 0.001). The FPV arm also showed significant improvement in chest CT compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest CT. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.

The Society for Ecological Restoration (SER) is an international non-profit organization with members in 70 countries. SER advances the science, practice and policy of ecological restoration to sustain biodiversity, improve resilience in a changing climate, and re-establish an ecologically healthy relationship between nature and culture. SER is a dynamic global network, linking researchers, practitioners, land managers, community leaders and decision-makers to restore ecosystems and the human communities that depend on them. Via its members, publications, conferences, policy work, and outreach, SER defines and delivers excellence in the field of ecological restoration.

The fields of theoretical and computational chemistry have come a long way since their inception in the mid-20th century. Fifty years ago, only rudimentary approximations for very simple molecules could be performed. Thanks in part to the ongoing development of very fast computers, and the efforts of theoretical chemists in developing fast and accurate quantum mechanical (QM) methods for calculating electronic energies of atoms and molecules, theoretical and computational chemistry can now give reliable geometries, energies, reactivities, and electronic properties for molecules. Such information has become indispensable in understanding and explaining experimental results that would be otherwise difficult to interpret.

The extensive use of antibiotics leading to the rapid spread of antibiotic resistance poses high health risks to humans, but to date there is still lack of a quantitative model to properly assess the risks. Concerns over the health risk of antibiotic residues in the environment are mainly (1) the potential hazard of ingested antibiotic residues in the environment altering the human microbiome and promoting emergence and selection for bacteria resistance inhabiting the human body, and (2) the potential hazard of creating a selection pressure on environmental microbiome and leading to reservoirs of antibiotic resistance in the environment. We provide a holistic view of health risk assessment of antibiotic resistance associated with antibiotic residues in the environment in contrast with that of the antibiotic resistant bacteria and discuss the main knowledge gaps and the future research that should be prioritized to achieve the quantitative risk assessment. We examined and summarized the available data and information on the four core elements of antibiotic resistance associated with antibiotic residues in the environment: hazard identification, exposure assessment, dose-response assessment, and risk characterization. The data required to characterize the risks of antibiotic residues in the environment is severely limited. The main future research needs have been identified to enable better assessments of antibiotic resistance associated with antibiotic residues in the environment: (1) establishment of a standardized monitoring guide of antibiotic residues and antibiotic resistance in the environment, (2) derivation of the relationship between antibiotic levels and pathogenic antibiotic-resistance development in different settings, and (3) establishment of the dose-response relationship between pathogenic antibiotic resistant bacteria and various infection diseases. After identification of key risk determinant parameters, we propose a conceptual framework of human health risk assessments of antibiotic residues in the environment. CAPSULE: A holistic view of human health risk assessment of antibiotic residues in the environment was provided.