Showing papers by "The Chinese University of Hong Kong published in 2021"

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary.

Abstract: The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide 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.

Genetic mechanisms of critical illness in Covid-19.

Abstract: Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10−8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10−8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 × 10−12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10−8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte–macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice. A genome-wide association study of critically ill patients with COVID-19 identifies genetic signals that relate to important host antiviral defence mechanisms and mediators of inflammatory organ damage that may be targeted by repurposing drug treatments.

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Abstract: In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19.

Abstract: Objective Although COVID-19 is primarily a respiratory illness, there is mounting evidence suggesting that the GI tract is involved in this disease. We investigated whether the gut microbiome is linked to disease severity in patients with COVID-19, and whether perturbations in microbiome composition, if any, resolve with clearance of the SARS-CoV-2 virus. Methods In this two-hospital cohort study, we obtained blood, stool and patient records from 100 patients with laboratory-confirmed SARS-CoV-2 infection. Serial stool samples were collected from 27 of the 100 patients up to 30 days after clearance of SARS-CoV-2. Gut microbiome compositions were characterised by shotgun sequencing total DNA extracted from stools. Concentrations of inflammatory cytokines and blood markers were measured from plasma. Results Gut microbiome composition was significantly altered in patients with COVID-19 compared with non-COVID-19 individuals irrespective of whether patients had received medication (p Conclusion Associations between gut microbiota composition, levels of cytokines and inflammatory markers in patients with COVID-19 suggest that the gut microbiome is involved in the magnitude of COVID-19 severity possibly via modulating host immune responses. Furthermore, the gut microbiota dysbiosis after disease resolution could contribute to persistent symptoms, highlighting a need to understand how gut microorganisms are involved in inflammation and COVID-19.

From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network

Abstract: 3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part- $A^2$ A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. First, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part- $A^2$ A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data.

Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

Layer-by-Layer Processed Ternary Organic Photovoltaics with Efficiency over 18.

Abstract: Obtaining a finely tuned morphology of the active layer to facilitate both charge generation and charge extraction has long been the goal in the field of organic photovoltaics (OPVs). Here, a solution to resolve the above challenge via synergistically combining the layer-by-layer (LbL) procedure and the ternary strategy is proposed and demonstrated. By adding an asymmetric electron acceptor, BTP-S2, with lower miscibility to the binary donor:acceptor host of PM6:BO-4Cl, vertical phase distribution can be formed with donor-enrichment at the anode and acceptor-enrichment at the cathode in OPV devices during the LbL processing. In contrast, LbL-type binary OPVs based on PM6:BO-4Cl still show bulk-heterojunction like morphology. The formation of the vertical phase distribution can not only reduce charge recombination but also promote charge collection, thus enhancing the photocurrent and fill factor in LbL-type ternary OPVs. Consequently, LbL-type ternary OPVs exhibit the best efficiency of 18.16% (certified: 17.8%), which is among the highest values reported to date for OPVs. The work provides a facile and effective approach for achieving high-efficiency OPVs with expected morphologies, and demonstrates the LbL-type ternary strategy as being a promising procedure in fabricating OPV devices from the present laboratory study to future industrial production.

Observation of Gravitational Waves from Two Neutron Star–Black Hole Coalescences

Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

Engineering exosomes for targeted drug delivery

Abstract: Exosomes are cell-derived nanovesicles that are involved in the intercellular transportation of materials. Therapeutics, such as small molecules or nucleic acid drugs, can be incorporated into exosomes and then delivered to specific types of cells or tissues to realize targeted drug delivery. Targeted delivery increases the local concentration of therapeutics and minimizes side effects. Here, we present a detailed review of exosomes engineering through genetic and chemical methods for targeted drug delivery. Although still in its infancy, exosome-mediated drug delivery boasts low toxicity, low immunogenicity, and high engineerability, and holds promise for cell-free therapies for a wide range of diseases.

Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans

Abstract: This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. To evaluate our approach, we create a multi-view dataset named ZJU-MoCap that captures performers with complex motions. Experiments on ZJU-MoCap show that our approach outperforms prior works by a large margin in terms of novel view synthesis quality. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

Metaverse for Social Good: A University Campus Prototype

Abstract: In recent years, the metaverse has attracted enormous attention from around the world with the development of related technologies. The expected metaverse should be a realistic society with more direct and physical interactions, while the concepts of race, gender, and even physical disability would be weakened, which would be highly beneficial for society. However, the development of metaverse is still in its infancy, with great potential for improvement. Regarding metaverse's huge potential, industry has already come forward with advance preparation, accompanied by feverish investment, but there are few discussions about metaverse in academia to scientifically guide its development. In this paper, we highlight the representative applications for social good. Then we propose a three-layer metaverse architecture from a macro perspective, containing infrastructure, interaction, and ecosystem. Moreover, we journey toward both a historical and novel metaverse with a detailed timeline and table of specific attributes. Lastly, we illustrate our implemented blockchain-driven metaverse prototype of a university campus and discuss the prototype design and insights.

Metaverse for Social Good: A University Campus Prototype

Abstract: In recent years, the metaverse has attracted enormous attention from around the world with the development of related technologies. The expected metaverse should be a realistic society with more direct and physical interactions, while the concepts of race, gender, and even physical disability would be weakened, which would be highly beneficial for society. However, the development of metaverse is still in its infancy, with great potential for improvement. Regarding metaverse's huge potential, industry has already come forward with advance preparation, accompanied by feverish investment, but there are few discussions about metaverse in academia to scientifically guide its development. In this paper, we highlight the representative applications for social good. Then we propose a three-layer metaverse architecture from a macro perspective, containing infrastructure, interaction, and ecosystem. Moreover, we journey toward both a historical and novel metaverse with a detailed timeline and table of specific attributes. Lastly, we illustrate our implemented blockchain-driven metaverse prototype of a university campus and discuss the prototype design and insights.

Esophageal motility disorders on high-resolution manometry: Chicago classification version 4.0©

Abstract: Chicago Classification v4.0 (CCv4.0) is the updated classification scheme for esophageal motility disorders using metrics from high-resolution manometry (HRM). Fifty-two diverse international experts separated into seven working subgroups utilized formal validated methodologies over two-years to develop CCv4.0. Key updates in CCv.4.0 consist of a more rigorous and expansive HRM protocol that incorporates supine and upright test positions as well as provocative testing, a refined definition of esophagogastric junction (EGJ) outflow obstruction (EGJOO), more stringent diagnostic criteria for ineffective esophageal motility and description of baseline EGJ metrics. Further, the CCv4.0 sought to define motility disorder diagnoses as conclusive and inconclusive based on associated symptoms, and findings on provocative testing as well as supportive testing with barium esophagram with tablet and/or functional lumen imaging probe. These changes attempt to minimize ambiguity in prior iterations of Chicago Classification and provide more standardized and rigorous criteria for patterns of disorders of peristalsis and obstruction at the EGJ.

A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health

Abstract: There are no literature reviews that have examined the impact of health-domain interventions, informed by self-determination theory (SDT), on SDT constructs and health indices. Our aim was to meta-analyse such interventions in the health promotion and disease management literatures. Studies were eligible if they used an experimental design, tested an intervention that was based on SDT, measured at least one SDT-based motivational construct, and at least one indicator of health behaviour, physical health, or psychological health. Seventy-three studies met these criteria and provided sufficient data for the purposes of the review. A random-effects meta-analytic model showed that SDT-based interventions produced small-to-medium changes in most SDT constructs at the end of the intervention period, and in health behaviours at the end of the intervention period and at the follow-up. Small positive changes in physical and psychological health outcomes were also observed at the end of the interventions. Increases in need support and autonomous motivation (but not controlled motivation or amotivation) were associated with positive changes in health behaviour. In conclusion, SDT-informed interventions positively affect indices of health; these effects are modest, heterogeneous, and partly due to increases in self-determined motivation and support from social agents.

Dietary cholesterol drives fatty liver-associated liver cancer by modulating gut microbiota and metabolites

Abstract: Objective Non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is an increasing healthcare burden worldwide. We examined the role of dietary cholesterol in driving NAFLD–HCC through modulating gut microbiota and its metabolites. Design High-fat/high-cholesterol (HFHC), high-fat/low-cholesterol or normal chow diet was fed to C57BL/6 male littermates for 14 months. Cholesterol-lowering drug atorvastatin was administered to HFHC-fed mice. Germ-free mice were transplanted with stools from mice fed different diets to determine the direct role of cholesterol modulated-microbiota in NAFLD–HCC. Gut microbiota was analysed by 16S rRNA sequencing and serum metabolites by liquid chromatography–mass spectrometry (LC–MS) metabolomic analysis. Faecal microbial compositions were examined in 59 hypercholesterolemia patients and 39 healthy controls. Results High dietary cholesterol led to the sequential progression of steatosis, steatohepatitis, fibrosis and eventually HCC in mice, concomitant with insulin resistance. Cholesterol-induced NAFLD–HCC formation was associated with gut microbiota dysbiosis. The microbiota composition clustered distinctly along stages of steatosis, steatohepatitis and HCC. Mucispirillum, Desulfovibrio, Anaerotruncus and Desulfovibrionaceae increased sequentially; while Bifidobacterium and Bacteroides were depleted in HFHC-fed mice, which was corroborated in human hypercholesteremia patients. Dietary cholesterol induced gut bacterial metabolites alteration including increased taurocholic acid and decreased 3-indolepropionic acid. Germ-free mice gavaged with stools from mice fed HFHC manifested hepatic lipid accumulation, inflammation and cell proliferation. Moreover, atorvastatin restored cholesterol-induced gut microbiota dysbiosis and completely prevented NAFLD–HCC development. Conclusions Dietary cholesterol drives NAFLD–HCC formation by inducing alteration of gut microbiota and metabolites in mice. Cholesterol inhibitory therapy and gut microbiota manipulation may be effective strategies for NAFLD–HCC prevention.

BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation

Abstract: Low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, leading to a considerable decrease in accuracy. We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for real-time semantic segmentation. For this purpose, we propose an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2). This architecture involves the following: (i) A detail branch, with wide channels and shallow layers to capture low-level details and generate high-resolution feature representation; (ii) A semantics branch, with narrow channels and deep layers to obtain high-level semantic context. The detail branch has wide channel dimensions and shallow layers, while the semantics branch has narrow channel dimensions and deep layers. Due to the reduction in the channel capacity and the use of a fast-downsampling strategy, the semantics branch is lightweight and can be implemented by any efficient model. We design a guided aggregation layer to enhance mutual connections and fuse both types of feature representation. Moreover, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture shows favorable performance compared to several state-of-the-art real-time semantic segmentation approaches. Specifically, for a $$2048\times 1024$$ input, we achieve 72.6% Mean IoU on the Cityscapes test set with a speed of 156 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods, yet we achieve better segmentation accuracy. The code and trained models are available online at https://git.io/BiSeNet .

FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection

Abstract: Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost. It is much more challenging than conventional 2D cases due to its inherent ill-posed property, which is mainly reflected in the lack of depth information. Recent progress on 2D detection offers opportunities to better solving this problem. However, it is non-trivial to make a general adapted 2D detector work in this 3D task. In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D. Specifically, we first transform the commonly defined 7-DoF 3D targets to the image domain and decouple them as 2D and 3D attributes. Then the objects are distributed to different feature levels with consideration of their 2D scales and assigned only according to the projected 3D-center for the training procedure. Furthermore, the center-ness is redefined with a 2D Gaussian distribution based on the 3D-center to fit the 3D target formulation. All of these make this framework simple yet effective, getting rid of any 2D detection or 2D-3D correspondence priors. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020. Code and models are released at this https URL.

Neutralizing antibody titres in SARS-CoV-2 infections.

Abstract: The SARS-CoV-2 pandemic poses the greatest global public health challenge in a century. Neutralizing antibody is a correlate of protection and data on kinetics of virus neutralizing antibody responses are needed. We tested 293 sera from an observational cohort of 195 reverse transcription polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 infections collected from 0 to 209 days after onset of symptoms. Of 115 sera collected ≥61 days after onset of illness tested using plaque reduction neutralization (PRNT) assays, 99.1% remained seropositive for both 90% (PRNT90) and 50% (PRNT50) neutralization endpoints. We estimate that it takes at least 372, 416 and 133 days for PRNT50 titres to drop to the detection limit of a titre of 1:10 for severe, mild and asymptomatic patients, respectively. At day 90 after onset of symptoms (or initial RT-PCR detection in asymptomatic infections), it took 69, 87 and 31 days for PRNT50 antibody titres to decrease by half (T1/2) in severe, mild and asymptomatic infections, respectively. Patients with severe disease had higher peak PRNT90 and PRNT50 antibody titres than patients with mild or asymptomatic infections. Age did not appear to compromise antibody responses, even after accounting for severity. We conclude that SARS-CoV-2 infection elicits robust neutralizing antibody titres in most individuals.

Updated Overall Survival and PD-L1 Subgroup Analysis of Patients With Extensive-Stage Small-Cell Lung Cancer Treated With Atezolizumab, Carboplatin, and Etoposide (IMpower133)

Abstract: PURPOSE:IMpower133 (ClinicalTrials.gov identifier: NCT02763579), a randomized, double-blind, phase I/III study, demonstrated that adding atezolizumab (anti-programmed death-ligand 1 [PD-L1]) to car...

Effect of IBD medications on COVID-19 outcomes: results from an international registry.

Abstract: Objective We sought to evaluate COVID-19 clinical course in patients with IBD treated with different medication classes and combinations. Design Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) is a large, international registry created to monitor outcomes of IBD patients with confirmed COVID-19. We used multivariable regression with a generalised estimating equation accounting for country as a random effect to analyse the association of different medication classes with severe COVID-19, defined as intensive care unit admission, ventilator use and/or death. Results 1439 cases from 47 countries were included (mean age 44.1 years, 51.4% men) of whom 112 patients (7.8%) had severe COVID-19. Compared with tumour necrosis factor (TNF) antagonist monotherapy, thiopurine monotherapy (adjusted OR (aOR) 4.08, 95% CI 1.73 to 9.61) and combination therapy with TNF antagonist and thiopurine (aOR 4.01, 95% CI 1.65 to 9.78) were associated with an increased risk of severe COVID-19. Any mesalamine/sulfasalazine compared with no mesalamine/sulfasalazine use was associated with an increased risk (aOR 1.70, 95% CI 1.26 to 2.29). This risk estimate increased when using TNF antagonist monotherapy as a reference group (aOR 3.52, 95% CI 1.93 to 6.45). Interleukin-12/23 and integrin antagonists were not associated with significantly different risk than TNF antagonist monotherapy (aOR 0.98, 95% CI 0.12 to 8.06 and aOR 2.42, 95% CI 0.59 to 9.96, respectively). Conclusion Combination therapy and thiopurines may be associated with an increased risk of severe COVID-19. No significant differences were observed when comparing classes of biologicals. These findings warrant confirmation in large population-based cohorts. MKH should be changed to MDK for co-last author line