BPOZ-2 is a negative regulator of the NLPR3 inflammasome contributing to SARS-CoV-2-induced hyperinflammation

Introduction Inflammation play important roles in the initiation and progression of acute lung injury (ALI), acute respiratory distress syndrome (ARDS), septic shock, clotting dysfunction, or even death associated with SARS-CoV-2 infection. However, the pathogenic mechanisms underlying SARS-CoV-2-induced hyperinflammation are still largely unknown. Methods The animal model of septic shock and ALI was established after LPS intraperitoneal injection or intratracheal instillation. Bone marrow-derived macrophages (BMDMs) from WT and BPOZ-2 KO mouse strains were harvested from the femurs and tibias of mice. Immunohistology staining, ELISA assay, coimmunoprecipitation, and immunoblot analysis were used to detect the histopathological changes of lung tissues and the expression of inflammatory factors and protein interaction. Results and conclusions We show a distinct mechanism by which the SARS-CoV-2 N (SARS-2-N) protein targets Bood POZ-containing gene type 2 (BPOZ-2), a scaffold protein for the E3 ubiquitin ligase Cullin 3 that we identified as a negative regulator of inflammatory responses, to promote NLRP3 inflammasome activation. We first demonstrated that BPOZ-2 knockout (BPOZ-2 KO) mice were more susceptible to lipopolysaccharide (LPS)-induced septic shock and ALI and showed increased serum IL-1β levels. In addition, BMDMs isolated from BPOZ-2 KO mice showed increased IL-1β production in response to NLRP3 stimuli. Mechanistically, BPOZ-2 interacted with NLRP3 and mediated its degradation by recruiting Cullin 3. In particular, the expression of BPOZ-2 was significantly reduced in lung tissues from mice infected with SARS-CoV-2 and in cells overexpressing SARS-2-N. Importantly, proinflammatory responses triggered by the SARS-2-N were significantly blocked by BPOZ-2 reintroduction. Thus, we concluded that BPOZ-2 is a negative regulator of the NLPR3 inflammasome that likely contributes to SARS-CoV-2-induced hyperinflammation.

BPOZ-2 is a negative regulator of the NLPR3 inflammasome contributing to SARS-CoV-2-induced hyperinflammation.

Introduction: Inflammation play important roles in the initiation and progression of acute lung injury (ALI), acute respiratory distress syndrome (ARDS), septic shock, clotting dysfunction, or even death associated with SARS-CoV-2 infection. However, the pathogenic mechanisms underlying SARS-CoV-2-induced hyperinflammation are still largely unknown. Methods: The animal model of septic shock and ALI was established after LPS intraperitoneal injection or intratracheal instillation. Bone marrow-derived macrophages (BMDMs) from WT and BPOZ-2 KO mouse strains were harvested from the femurs and tibias of mice. Immunohistology staining, ELISA assay, coimmunoprecipitation, and immunoblot analysis were used to detect the histopathological changes of lung tissues and the expression of inflammatory factors and protein interaction. Results and conclusions: We show a distinct mechanism by which the SARS-CoV-2 N (SARS-2-N) protein targets Bood POZ-containing gene type 2 (BPOZ-2), a scaffold protein for the E3 ubiquitin ligase Cullin 3 that we identified as a negative regulator of inflammatory responses, to promote NLRP3 inflammasome activation. We first demonstrated that BPOZ-2 knockout (BPOZ-2 KO) mice were more susceptible to lipopolysaccharide (LPS)-induced septic shock and ALI and showed increased serum IL-1ß levels. In addition, BMDMs isolated from BPOZ-2 KO mice showed increased IL-1ß production in response to NLRP3 stimuli. Mechanistically, BPOZ-2 interacted with NLRP3 and mediated its degradation by recruiting Cullin 3. In particular, the expression of BPOZ-2 was significantly reduced in lung tissues from mice infected with SARS-CoV-2 and in cells overexpressing SARS-2-N. Importantly, proinflammatory responses triggered by the SARS-2-N were significantly blocked by BPOZ-2 reintroduction. Thus, we concluded that BPOZ-2 is a negative regulator of the NLPR3 inflammasome that likely contributes to SARS-CoV-2-induced hyperinflammation.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Application of artificial intelligence in cataract management: current and future directions.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

GP73 is a glucogenic hormone contributing to SARS-CoV-2-induced hyperglycemia.

Severe cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with elevated blood glucose levels and metabolic complications. However, the molecular mechanisms for how SARS-CoV-2 infection alters glycometabolic control are incompletely understood. Here, we connect the circulating protein GP73 with enhanced hepatic gluconeogenesis during SARS-CoV-2 infection. We first demonstrate that GP73 secretion is induced in multiple tissues upon fasting and that GP73 stimulates hepatic gluconeogenesis through the cAMP/PKA signaling pathway. We further show that GP73 secretion is increased in cultured cells infected with SARS-CoV-2, after overexpression of SARS-CoV-2 nucleocapsid and spike proteins and in lungs and livers of mice infected with a mouse-adapted SARS-CoV-2 strain. GP73 blockade with an antibody inhibits excessive glucogenesis stimulated by SARS-CoV-2 in vitro and lowers elevated fasting blood glucose levels in infected mice. In patients with COVID-19, plasma GP73 levels are elevated and positively correlate with blood glucose levels. Our data suggest that GP73 is a glucogenic hormone that likely contributes to SARS-CoV-2-induced abnormalities in systemic glucose metabolism.

HDL-scavenger receptor B type 1 facilitates SARS-CoV-2 entry.

Responsible for the ongoing coronavirus disease 19 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through binding of the viral spike protein (SARS-2-S) to the cell-surface receptor angiotensin-converting enzyme 2 (ACE2). Here we show that the high-density lipoprotein (HDL) scavenger receptor B type 1 (SR-B1) facilitates ACE2-dependent entry of SARS-CoV-2. We find that the S1 subunit of SARS-2-S binds to cholesterol and possibly to HDL components to enhance viral uptake in vitro. SR-B1 expression facilitates SARS-CoV-2 entry into ACE2-expressing cells by augmenting virus attachment. Blockade of the cholesterol-binding site on SARS-2-S1 with a monoclonal antibody, or treatment of cultured cells with pharmacological SR-B1 antagonists, inhibits HDL-enhanced SARS-CoV-2 infection. We further show that SR-B1 is coexpressed with ACE2 in human pulmonary tissue and in several extrapulmonary tissues. Our findings reveal that SR-B1 acts as a host factor that promotes SARS-CoV-2 entry and may help explain viral tropism, identify a possible molecular connection between COVID-19 and lipoprotein metabolism, and highlight SR-B1 as a potential therapeutic target to interfere with SARS-CoV-2 infection.

Impact of COVID-19 lockdown on ambient levels and sources of volatile organic compounds (VOCs) in Nanjing, China.

A lot of restrictive measures were implemented in China during January-February 2020 to control rapid spread of COVID-19. Many studies reported impact of COVID-19 lockdown on air quality, but little research focused on ambient volatile organic compounds (VOCs) till now, which play important roles in production of ozone and secondary organic aerosol. In this study, impact of COVID-19 lockdown on VOCs mixing ratios and sources were assessed based on online measurements of VOCs in Nanjing during December 20, 2019-Feburary 15, 2020 (P1-P2) and April 15-May 13, 2020 (P3). Average VOCs levels during COVID-19 lockdown period (P2) was 26.9 ppb, about half of value for pre-lockdown period (P1). Chemical composition of VOCs also showed significant changes. Aromatics contribution during decreased from 13% during P1 to 9% during P2, whereas alkanes contribution increased from 64% to 68%. Positive matrix factorization (PMF) was then applied for non-methane hydrocarbons (NMHCs) sources apportionment. Five sources were identified, including a source related to transport and background air masses, three sources related to petrochemical industry or chemical industry (petrochemical industry#1-propene/ethene, petrochemical industry#2-C7-C9 aromatics, and chemical industry-benzene), and a source attributed to gasoline evaporation and vehicular emission. During P2, NMHCs levels from petrochemical industry#2-C7-C9 aromatics showed the largest relative decline of 94%, followed by petrochemical industry#1-propene/ethene (67%), and gasoline evaporation and vehicular emission (67%). Furthermore, ratios of OH reactivity of NMHCs versus NO2 level (ROH,NMHCs/NO2) and total oxidant production rate (P (OX)) were calculated to assess potential influences of COVID-19 lockdown on O3 formation.

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.