Poor COVID-19 outcomes occur with higher frequency in people with rheumatic and musculoskeletal diseases (RMD). Better understanding of the factors involved is crucial to informing patients and clinicians regarding risk mitigation.To describe COVID-19 outcomes for people with RMD in Ireland over the first 2 years of the pandemic.st March 2022 were analysed. Differences in the likelihood of hospitalisation and mortality according to demographic and clinical variables were investigated.Data entered into the C19-GRA provider registry from Ireland between 24th March 2020 and 31U-shaped relationship with disease activity, being more common in both high disease activity and remission. Mortality was more common with increasing age, gout, smoking, long-term glucocorticoid use, comorbidities, and specific comorbidities of cardiovascular disease, pulmonary disease, and obesity. Inflammatory arthritis was less frequent in those who died.Of 237 cases included, 59.9% were female, 95 (41.3%) were hospitalised, and 22 (9.3%) died. Hospitalisation was more common with increasing age, gout, smoking, long-term glucocorticoid use, comorbidities, and specific comorbidities of cardiovascular and pulmonary disease, and cancer. Hospitalisation was less frequent in people with inflammatory arthritis and conventional synthetic or biologic disease-modifying antirheumatic drug use. Hospitalisation had a Hospitalisation or death were more frequently experienced by RMD patients with increasing age, certain comorbidities including potentially modifiable ones, and certain medications and diagnoses amongst other factors. These are important ‘indicators’ that can help risk-stratify and inform the management of RMD patients. Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and consequent Coronavirus disease 2019 (COVID-19) is an ongoing global health challenge. People with rheumatic and musculoskeletal disease (RMD) who develop COVID-19 may have worse prognosis compared to the general population . VaccinaAs public health restrictions lessen and increased interpersonal interactions occur in work and social settings, it becomes of increased importance to report data that people with RMD can use to understand the potential health implications. Exploration of the outcomes of COVID-19 in people with RMD and the associations with disease and individual specific factors may facilitate more precise/accurate risk assessments.In this study, we report COVID-19 outcomes in people with RMD in Ireland over the first 2 years of the pandemic.Data regarding individuals with RMD with COVID-19 are entered into one of two parallel international data portals hosted in the USA and UK. Details of the C19-GRA registries have been published previously , 7.st March 2022 and included baseline RMD status including demographic and clinical variables such as age, sex, smoking status, RMD diagnosis, disease activity ), and comorbidities. All diagnoses were physician-reported. Medications were categorised as previously described [Data input into the C19-GRA provider registry took place between 24th March 2020 and 31escribed . Diseaseescribed . RMDs weDemographic and clinical continuous variables were reported as median (Interquartile range (IQR)) and categorical variables as number and percentage (%). Hospitalisation and mortality probability were calculated for each category of the key demographic and clinical variables. Differences in the likelihood of hospitalisation and mortality according to demographic and clinical variables were investigated using Chi-squared test or Fisher’s exact test, as appropriate.All statistical analyses were performed and graphs were prepared using STATA IC15 .This study was approved by the Irish National Research Ethics Committee for COVID-19 (20-NREC-COV-010). The committee waived the need for written informed consent as the data were anonymised.Over the 2-year period, 237 cases of COVID-19 were reported in people with RMD  being more likely to have disease activity labelled as “remission” between gout flares. However, the The RMD-specific factors we have identified as being more frequent among RMD patients in Ireland with poor outcomes are broadly in keeping with previous studies in this area. Glucocorticoid use prior to the development of COVID-19, particularly in doses ≥ 10 mg/day have been associated with COVID-19-related hospitalisation and mortality , 9. IncrOur study has several limitations. While this is the largest study of people with RMD and COVID-19 from Ireland, the statistical power of our study is limited by the relatively low number of cases reported, prohibiting the undertaking of multivariable analyses. The findings of our study must be interpreted with due cognisance of the limitations of the C19-GRA registry, including selection bias, unmeasured confounders, and possible artefact related to the identification of milder cases over time as testing capacity expanded . The C19In conclusion, we have identified both shared features with the general population and RMD-specific factors which are over-represented among patients with severe COVID-19 outcomes. These factors may aid early identification of patients with RMDs with poor prognosis when prioritising booster vaccination and when prescribing COVID-19 antiviral therapy.


While cannabis is known to have immunomodulatory properties, the clinical consequences of its use on outcomes in COVID-19 have not been extensively evaluated. We aimed to assess whether cannabis users hospitalized for COVID-19 had improved outcomes compared to non-users.We conducted a retrospective analysis of 1831 patients admitted to two medical centers in Southern California with a diagnosis of COVID-19. We evaluated outcomes including NIH COVID-19 Severity Score, need for supplemental oxygen, ICU (intensive care unit) admission, mechanical ventilation, length of hospitalization, and in-hospital death for cannabis users and non-users. Cannabis use was reported in the patient’s social history. Propensity matching was used to account for differences in age, body-mass index, sex, race, tobacco smoking history, and comorbidities known to be risk factors for COVID-19 mortality between cannabis users and non-users.p < 0.001), less often diabetic , and more frequently active tobacco smokers  compared to non-users. Notably, active users had lower levels of inflammatory markers upon admission than non-users—CRP (C-reactive protein) , ferritin , D-dimer , and procalcitonin . Based on univariate analysis, cannabis users had significantly better outcomes compared to non-users as reflected in lower NIH scores , shorter hospitalization , lower ICU admission rates , and less need for mechanical ventilation . Using propensity matching, differences in overall survival were not statistically significant between cannabis users and non-users, nevertheless ICU admission was 12 percentage points lower (p = 0.018) and intubation rates were 6 percentage points lower (p = 0.017) in cannabis users.Of 1831 patients admitted with COVID-19, 69 patients reported active cannabis use (4% of the cohort). Active users were younger (44 years vs. 62 years, This retrospective cohort study suggests that active cannabis users hospitalized with COVID-19 had better clinical outcomes compared with non-users, including decreased need for ICU admission or mechanical ventilation. However, our results need to be interpreted with caution given the limitations of a retrospective analysis. Prospective and observational studies will better elucidate the effects cannabis use in COVID-19 patients.The online version contains supplementary material available at 10.1186/s42238-022-00152-x. The COVID-19 (Coronavirus Disease 2019) Pandemic caused by SARS-CoV2 (severe acute respiratory syndrome coronavirus 2) has upended the global community, infecting over 300 million people and claiming over 5 million lives in its wake (Coronavirus Disease (COVID-19) Situation Reports The immunomodulatory effects of cannabis may be especially important to understand in hospitalized patients with COVID-19, where progression to more severe forms of COVID may be due to manifestations of hyperinflammatory states  led to 100% survival, decreased lung inflammation, and suppressed cytokine storm  found in cannabis are capable of binding to endogenous cannabinoid receptors CBWhile some authors recommend caution in encouraging cannabis use for prevention or treatment of COVID-19, as has been eschewed in the media, there has been growing interest in this area  approval was obtained prior to data collection (IRB 20-000473) with informed consent waived by the IRB given the retrospective nature of the study utilizing existing clinical data. Cannabis use was assessed by patient self-report as part of the patient’s social history obtained at the time of admission. The electronic health records of documented cannabis users were manually abstracted to confirm active use, defined as any use of inhaled (both vaporized and combusted) or edible cannabis within 1 month prior to admission. Patients with missing social history regarding cannabis use were presumed to be non-users. Data was abstracted and stored in a REDCap database   which classifies disease severity from 1  to 8 (death)  for continuous variables and Fisher’s exact tests for categorical variables. Because of the observational nature of the study and potential bias due to self-selection into the cannabis user groups, we utilized inverse-probability-weighted regression adjustment (IPW-RA) based on the aforementioned covariates  as a means to estimate the effect of cannabis use on the study outcomes. The covariates were used in both the creation of the propensity weight and in the regression adjustment portion of the analysis. Both age and BMI were modeled using quadratic terms in order to capture potential non-linear associations with the outcomes and propensity for cannabis use. Certain outcomes ) were log-transformed in these models. Because unknown tobacco smoking status was collinear with cannabis use , the IPW-RA models were implemented excluding the 72 non-users with unknown tobacco smoking status. For some outcomes , these IPW-RA models failed to converge and a 1-to-1 Propensity Score Matching (PSM) method with caliper distance restricted to within 0.20 was implemented instead. The standardized mean difference between current marijuana users and non-users on the covariates used in the IPW-RA and PSM models were additionally examined  were assessed using Welch’s p < 0.001). Active users also had lower rates of diabetes mellitus . There were no significant differences among the other comorbid conditions. Subjects that consumed cannabis also had nearly 5 times higher prevalence of current tobacco smoking (p < 0.001).A total of 1831 patients with COVID-19 were included in this study. Of those, 69  were active users, defined as use within the past 1 month ; an NIH score of 5 includes patients requiring supplemental oxygen while a score of 6 differentiates those that require either non-invasive ventilation or high flow oxygen devices. Cannabis users also had shorter hospitalizations  and appreciably lower ICU admission rates . In addition, cannabis users had less need for mechanical ventilation  compared to non-users. Using propensity matching, differences in overall survival were not statistically significant between cannabis users and non-users, nevertheless ICU admission was 12 percentage points lower (p = 0.018) and intubation rates were 6 percentage points lower (p = 0.017) in cannabis users after adjusting for covariates including age, sex, BMI, tobacco smoking history, and COVID-specific treatment , Ferritin , and D-dimer  compared to non-users. This effect was sustained during their hospital course, with cannabis users continuing to have lower inflammatory markers compared to non-users. Cannabis users were more likely to have higher lymphocyte counts on admission and during their hospital course. Peak neutrophil counts were also lower in cannabis users, while admission neutrophil values were the same. No statistical difference was detected in total white blood cell (WBC) count between cannabis users and non-users.We evaluated the admission lab values (drawn within 48 h of admission) and the highest value during the patient’s hospital course (Table p = 0.001), remdesivir  and antibiotics  compared with users. Thus, cannabis users received less COVID-19 adjunctive therapies but had better outcomes compared to non-users. These findings, along with the aforementioned outcomes investigated in this study, were consistent at all points throughout the pandemic as shown in Fig. Given changing recommendations during the time of this patient sample, we also compared the pharmacologic management between groups. We found that non-users were more likely to receive systemic steroids  used cannabis during the past year  Releases | CBHSQ Data In our population of cannabis users, there was a paucity of data regarding route of administration. This is important, as the route of cannabis use can influence health outcomes. While cannabis smoking is associated with respiratory issues including bronchitis and arguably impaired lung function (Tashkin and Roth Previous studies have found that patients with cannabis use disorder, while younger and less comorbid, had higher risk for breakthrough infections of COVID-19 despite vaccination. Wang et al. posit that behavioral factors or adverse pulmonary and immunologic effects of cannabis may contribute to this breakthrough risk (Wang et al. To our knowledge, this study is one of the first evaluations of the effect of cannabis use on outcomes in patients hospitalized with COVID-19. While previous data have determined the detrimental relationship of tobacco smoking with COVID-19, this study suggests that cannabis may actually lead to reduced disease severity and better outcomes despite a five-fold greater concomitant use of tobacco amongst cannabis users compared to non-users in our study population.Inaccurate or incomplete documentation in the medical record may bias our findings, as we were beholden to the data contained within the clinical chart, which this retrospective analysis was based on. While the majority of data abstraction was blinded to our study purpose, our authors did evaluate each chart of reported cannabis users to ensure current use. Additionally, cannabis use is self-reported without specific focus on it during routine admission data collection. Thus, we may not have captured all current cannabis users, potentially introducing further selection bias. Additionally, we do not have complete data on the route of cannabis use, frequency or duration, and we were therefore unable to comment on dose response or durability of the potential effects of cannabis consumption. Cannabis users were defined after both automatic and manual data processing and both populations were very well characterized. Given legalized recreational cannabis use in California, our retrospective analysis is less prone to the selection bias and underreporting of cannabis use in comparison to other centers where its use is illegal. Because our focus was also on cannabis use, we were also unable to ascertain alcohol use history and usage of other substances. Therefore, we were unable to factor in substance use disorder into our inverse probability weighting process.In this retrospective review of 1831 COVID-19 patients requiring hospital admission, current cannabis use was associated with decreased disease severity. This was demonstrated in lower NIH severity scores as well as less need for oxygen supplementation, ICU admission and mechanical ventilation. While there was a trend toward improved survival in cannabis users, this was not statistically significant. To our knowledge, this is the first study looking at clinical outcomes of cannabis users hospitalized with COVID-19. Further studies, including prospective analyses, will help to better understand the relationship between cannabis and COVID-19 outcomes.Additional file 1: Supplemental Table 1. NIH Severity Score and Definitions (Beigel et al. Supplemental Table 2. Covariate balance after propensity weighting or matching.


The base dataset for the extraction is CORD-19, a dataset of COVID-19-related publications, which is updated regularly. Consequently, CovidPubGraph is updated biweekly. Our generation pipeline applies named entity recognition, entity linking and link discovery approaches to the original data. The current version of CovidPubGraph contains 268,108,670 triples and is linked to 9 other datasets by over 1 million links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications. CovidPubGraph is publicly available under the Creative Commons Attribution 4.0 International license.The rapid generation of large amounts of information about the coronavirus SARS-CoV-2 and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview of current insights related to the disease. With this work, we aim to support the rapid access to a comprehensive data source on COVID-19 targeted especially at researchers. Our knowledge graph, C  While the CORD-19 dataset provides the extracted full texts and corresponding licenses, it is still difficult to consume for end users and applications. For example, the data is available as one download (see https://www.semanticscholar.org/cord19/download). Hence, users first need to download the dataset and carry out some processing  to get the information they desire. The integration of insights from different sources, which is of central importance in scientific research, cannot be carried out on the dataset directly. Moreover, the data being available in textual form makes it difficult to query using a structured query language such as SQL or SPARQL.The number of papers pertaining to SARS-CoV-2 and COVID-19 has surged over the last few months, making it hard to keep track of the latest research findings on the subject matter. Hence, the Allen Institute initiated a growing corpus of publications about COVID-19 called CORD-19ikidata Scholia only contain a small subset of the publications available as CORD-19. Other knowledge graphs about COVID-19 focus exclusively on case statistics instead of scientific publications  or text mining on the CORD-19 dataset without providing much information about the content present in the publications . Our goal differs from that of other COVID-19-related datasets: We aim to provide a comprehensive RDF representation of the CORD-19 data and include Natural Language Processing (NLP) results on the data to facilitate the development of intelligent search engines, domain-specific conversational AIs and structured machine learning solutions for COVID-19.A growing number of research labs are hence building upon CORD-19 to make the data more amenable to automated processing. Table ovidPubGraph, a comprehensive RDF knowledge graph of COVID-19 based on CORD-19. Our dataset follows the Linked Data lifecycle2. We provide a detailed representation of the COVID-19 publications in RDF including properties like publication title, authors names and their institutions, paper sections  and annotated references . Resources such as authors and named entities augment the original data and make it easier to process for the sake of question answering and machine learning. All resources in the dataset are dereferenceable HTTP IRIs, which can be accessed via LodView (https://lodview.it/) or via the dataset’s SPARQL endpoint (https://covid-19ds.data.dice-research.org/sparql/). In addition, we link our dataset to the biomedical entities in other relevant datasets .In this paper, we present C3: It is findable by virtue of being annotated with rich metadata and indexable by search engines. We make it accessible by providing our data via an RDF dump download (https://hobbitdata.informatik.uni-leipzig.de/COVID19DS/archive/), a SPARQL endpoint as well as dereferenceable individual resources. For example, see https://covid-19ds.data.dice-research.org/resource/4bf4b71883a26d15dcc13b2800ec470b99764956. We make it interoperable by employing standard vocabularies, e.g., for authors, papers, and sections within papers, as well as through the aforementioned links to 9 knowledge graphs including Cord19-NEKG, Cord-19-on-FHIR as well as Covid-19-Literature . The class expressions are comprehensible by domain experts.Training explainable machine learning models by running structured machine learning on selected named entities  to find similar drugs for clinical trials. The models can be trained with DL-Learner7 and peer review trends8, which would be informative for policy-makers and the scientific community.Supporting scientometric research on various aspects related to COVID-19 publications, such as international collaboration trendsPotential use cases of our knowledge graph include:Knowledge graphs on the field of COVID-19 can be divided by their topics covered: publications, biomedical entities, and case statistics.1. The CORD-19 dataset is based on papers and preprints from Semantic Scholar. Papers in CORD-19 are sourced from PubMedCentral (PMC), PubMed, the World Health Organization’s Covid-19 Database, and preprint servers bioRxiv, medRxiv, and arXiv1. While CORD-19 contains the full texts of scientific publications, it does not adhere to FAIR principles3, e.g., it is only available via download and does not use common vocabularies. The two knowledge graphs most closely related to ours are Cord19-NEKG9 and Covid-19-Literature10. However, neither of them provides comprehensive metadata about the publications, and neither provides fine-granular information pertaining to the publications . An alternative to CORD-19 is the Lens dataset on COVID-1911. Lens contains metadata about scientific publications on COVID-19. However, it is only available as one big download (in JSON format). The Covidgraph project (https://covidgraph.org/) aims to utilize the dataset. However, at the time of writing, the proposed CovidPubGraph has not been released yet, making it hard to compare it to other knowledge graphs. To enable interoperability, we link our dataset to other datasets such as the Cord19-NEKG.Most knowledge graphs of COVID-19 publications are based on the COVID-19 Open Research Dataset (CORD-19) by the Allen Institute13 such as genes, drugs, and proteins and linking them to existing knowledge bases such as DBpedia. For doing so, established tools such as DBpedia Spotlight14 and Entity Fishing (Wikidata) (https://github.com/kermitt2/entity-fishing/) are used. Alternatively, novel tools for recognizing biomedical entities on CORD-19 are also developed16. Noteworthy is also the work by Zhou, Y. et al.17, in which a network of genes, proteins, and viruses are proposed. The network is based on pre-existing biomedical databases  and does not cover the latest research findings. Still, such biomedical knowledge graphs might be employed to identify promising treatment options such as repurposing existing drugs or developing novel drugs regardless of the underlying construction methodology. We perform named entity recognition on CORD-19 and link the discovered entities to other biomedical RDF databases such as DrugBank18 (drugs), Sider19 (side effects), and Kegg20 (genes), thus making our dataset more amenable to tasks such as machine learning based on entities.Most works utilizing CORD-19 focus on extracting named entities21, e.g., subdivided by region and based on the Dashboard data by the John Hopkins University.Another class of knowledge graphs focuses on the case statistics of novel COVID-19 virushttps://www.semanticscholar.org/cord19/download). Our conversion process is implemented in Python 3.6 with RDFLib 5.0.0 (https://github.com/RDFLib/rdflib). We make our source code publicly available (https://github.com/dice-group/COVID19DS) to ensure the reproducibility of our results and the rapid conversion of novel CORD-19 versions. One version of the generated RDF dataset can be found at Zenodo22.The ontology behind our knowledge graph was derived from the source from which it was extracted, i.e., the full-texts of publications provided as part of the CORD-19 dataset. The ontology was designed to enable search, question answering and machine learning. At the time of writing, our dataset is based on CORD-19 version 2021-11-08 . Important attributes include the title, PMID, DOI, publication date, publisher, publisher URI, license and authors. For each paper, we store provenance information. In particular, our code allows the reference to the original CORD-19 raw files as well as the time when we generate the resource. The URIs of our generated Paper resources follow the format http://xmlns.com/foaf/spec/). Important attributes include the first, middle, and last names as well as mail addresses and institutions.Authors are represented in FOAF , its source string in the text (nif:referenceContext) along with its position in the text  as well as the referenced object (its:taIdentRef) which might be a paper (BibEntry), a figure , or a table (Table).Example paper resource.https://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html). Further details of our entity linking process are described in Linking Section.As machine learning and question answering often rely on named entities and their locations in texts, we annotate CORD-19 papers accordingly and represent this information with the NIF 2.0 Core Ontology , , Figure 1A,Fig. 1, etc.), we represent a reference in RDF as follows: We represent the anchor of the reference with nif:anchorOf , the start position of the reference with nif:beginIndex, the end position of the reference with nif:endIndex, the source section of the reference with nif:referenceContext, and the referenced target with its:taIdentRef . An example is shown in Listing 4. Listing 5 shows an example of provenance information.We link our dataset to other data sources to ensure its reusability and integrability as well as to improve its use for search, question answering and structured machine learning. We generate links from our paper and author resources to publicly available related knowledge bases. Moreover, we extract named entities related to diseases, genes, and cells from all converted papers and link them to three external knowledge bases.publications in our knowledge graph to six other datasets using the owl:sameAs and rdfs:seeAlso predicates  that is provided as part of CORD-19. For Covid-19-Literature and Cord-19-on-FHIR, we employ sha hash values from CORD-19. Moreover, we link our dataset to the publications’ JSON files in Cord-19-on-FHIR with the predicate rdfs:seeAlso. Listing 6 shows an example of linked publications from our dataset CovidPubGraph to Cord19-NEKG and LitCovid.Cauthors and institutes to the Microsoft Academic Knowledge Graph (MAKG)25 using the latest version of our link discovery framework LIMES26. For linking the authors, LIMES is configured to discover owl:sameAs links between our instances of foaf:Person and Microsoft’s makg:Author. For linking the institutes, we look for links between instances of type dbo:EducationalInstitution from our knowledge graph and MAKG’s resources of type makg:Affiliation. LIMES configuration files for linking authors and institutes are available as part of our source code (https://github.com/dice-group/COVID19DS).We link our resources of both our cispacy27 in version 0.2.4 in conjunction with the model en_ner_bionlp13cg_md  which allows the extraction of biomedical entities such as diseases, genes and cells. Scispacy is a specialized NLP library based on the spaCy library (https://spacy.io/). The NER model in spaCy is a transition-based chunking model that represents tokens as hashed embedded representations of the prefix, suffix, shape and lemmatized features of individual words27.We apply entity linking to connect entities derived from the sections of papers to other knowledge bases. This process comprises two steps: (1) entity extraction and (2) entity linking. For the extraction step, we use SProvenance information for the non-commercial dataset.An example of a linked publication.Entity linking example.28 to link our extracted resources to the three knowledge bases Sider19, Kegg20 and DrugBank18—using their RDF versions provided by the Bio2RDF project (https://bio2rdf.org/). We adapt MAG by creating a search index for each of the external knowledge bases and running MAG once per knowledge base. The output is a set of entities in the NLP Interchange Format (NIF) (https://persistence.uni-leipzig.org/nlp2rdf/). In Listing 7, we provide an example for the named entity “folic acid”.For the linking step, we adapt the entity linking framework MAGCrawling. We start by crawling the most recent version as a zip file from the CORD-19 website, which includes a CSV metadata file and JSON parsed full texts of scientific papers about the coronavirus.RDF conversion. Then, we convert the CORD-19 data into an RDF knowledge graph with a Python script using the RDFLib library (https://github.com/RDFLib/rdflib).Linking. We integrate the AGDISTIS library (https://github.com/dice-group/AGDISTIS) into the generation process to extract and link the named entities from abstracts of the scholarly articles. Moreover, we carry out the entity linking tasks  by making use of the link discovery framework LIMES (https://github.com/dice-group/LIMES).KG Update. We upload the new version of CovidPubGraph dumps into the HOBBIT server (https://hobbitdata.informatik.uni-leipzig.de/COVID19DS/archive/) as well as to the Virtuoso triple store (https://hub.docker.com/r/openlink/virtuoso-opensource-7).CORD-19 uploaded new data almost every day for the second half of 2020. Due to this fact, we have to automate the process of updating our knowledge graph. To this end, we developed a pipeline to automate the entire process, which can be found in Fig. Starting from 2021, CORD-19 publishes new data only every two weeks. Therefore, we keep our KG up-to-date by crawling the new version of the CORD-19 dataset biweekly. Then, we follow the KG creation procedure presented in Fig. Representing COVID-19-related publications as RDF promises to facilitate many applications and use cases—some of which we outline in this section.An example of how the data are constantly updated is provided in Table List the top 10 papers-URIs with the most number of authors.List all paper URIs written by the author “Ian Mackay.”While our base dataset CORD-19 contains a significant number of publications, they are not represented in a format optimized for retrieval.ovidPubGraph in RDF with a well-defined ontology, we enable the easy retrieval of data with structured query languages such as SPARQL. For example, Listing 9 shows a query to retrieve all papers written by the author “Ian Mackay.” Another query to retrieve the top 10 papers in terms of their number of authors is provided inBy providing CUsing SPARQL queries, we carried out some random checks of the duplicate articles and authors, which resulted in no duplicates. This could be a direct consequence of the high quality of the original CORD dataset. Still, doing a full KG deduplication task is part of our future work.Using the interoperability capabilities provided by NIF, it is easy to query all occurrences of a certain text segment within the whole dataset and still know exactly where each mention occurs. For example, in Listing 10, we provide a SPARQL query to list all papers where “folic acid” is mentioned with their respective sections.http://ma-graph.org/) and our interlinking enables the retrieval of an author’s citation count (Listing 11).Linking our dataset to other RDF datasets adds a considerable amount of value. For example, Microsoft Academic Knowledge Graph (MAKG) covers more than 209 million publications (Table https://dice-research.org) as part of the lab’s HOBBIT dataset efforts29. A 100TB-Server maintained by the Paderborn university’s computing centre will host the datasets.All our resources are served from one of our servers via persistent URIs. The resource will be maintained by the DICE research team (https://lodview.it/) for dereferencing our dataset URIs and allowing users to conveniently browse HTML pages. Figure odView.We employ LodView  and archived on Zenodo (https://zenodo.org/record/4650261).We provide dump files of our dataset for download. The generated RDF datasets are located on our HOBBIT storage (ovidPubGraph via a SPARQL endpoint (https://covid-19ds.data.dice-research.org/sparql).We publicly serve C


N = 366), 55% indicated worsening of their mental health. In logistic regression, compared to participants who did not experience any loss of income, those who experienced loss of income due to the COVID-19 pandemic were significantly more likely to report worsening mental health . Compared to participants who self-identified as gay, bisexual-identifying participants were less likely to report worsening mental health . Finally, as compared to participants who were married or partnered, participants who were dating , single , and separated/divorced/widowed  were all significantly more likely to report experiencing a worsening of mental health due to the COVID-19 pandemic. This study highlights the need to develop robust public strategies for sub-populations of 2SGBQ+ men . Specific targeted and tailored public health interventions designed with the unique needs of 2SGBQ+ men in Manitoba may be required to increase their access to socio-economic and mental health supports.This study examined the relationship between loss of income due to the COVID-19 pandemic and worsening mental health among a sample of 366 Two-Spirit, gay, bisexual, queer (2SGBQ+) men in Manitoba. Data were drawn from a cross-sectional online survey among 2SGBQ+ men in Manitoba. Logistic regression assessed the relationship between sociodemographics, loss of income due to COVID-19 (independent variable) and worsening of mental health . Among all respondents in the sample ( The COVID-19 pandemic is reinforcing and exacerbating mental health inequities among vulnerable populations, such as two-spirit, gay, bisexual, queer (2SGBQ+), cisgender and transgender men and other men who have sex with men. In Canada, 2SGBQ+ men are already at a disproportionately high risk for a variety of negative mental health outcomes, such as depression, anxiety, loneliness, and social isolation [n = 410) reported that they needed help with problems like anxiety, depression, and suicidal thoughts [According to the results of the Manitoba Two-Spirit, Gay, Bisexual, and Queer Men’s Health Study, 52% of participants n = 4 reportedResearch suggests that mental health stressors may be greater for vulnerable populations, including Two-Spirit, lesbian, gay, bisexual, transgender, queer, and intersex (2SLGBTQIA+) individuals, who report worse mental health outcomes as a result of discrimination, stigma, and experiences of rejection based on their sexual orientation or gender identity –13.n = 2,450) completed between 2017–2019 found that one in four 2SGBQ+ men rated their mental health as poor in the last six months [Research also suggests that 2SGBQ+ men are 3 times more likely to report depression relative to individuals in the general population with comparable levels of mental health challenges and of similar socio-economic status . Gay menx months . Moreovex months .Research conducted in the United States shows that social isolation, limited access to friends and partners and sheltering in hostile (homophobic and transphobic) home environments during COVID-19 is associated with poorer mental health outcomes among 2SGBQ+ men –19. AnotA global study of cisgender sexual minority men showed that men who experienced employment loss due to COVID-19 also reported higher rates of depression and anxiety relative to those without employment loss . ResearcThe impacts of COVID-19 on 2SGBQ+ men, in particular the negative impacts of income loss on 2SGBQ+ men’s mental health, are still unknown. There is a dearth of public health research on 2SGBQ+ men’s communities in Manitoba. The goal of this analysis was to examine the impacts of the COVID-19 pandemic on 2SGBQ+ men’s mental health in Manitoba, including the relationship between socio-demographics and economic impacts of COVID-19 on their mental health. We hypothesize that sociodemographic factors  and loss of income will be associated with worsening mental health due to the COVID-19 pandemic among 2SGBQ+ men in Manitoba.The COVID-19 & Manitoba Two-Spirit, Gay, Bisexual, and Queer Men’s Health Study. The study was a 2-part community-based research study designed to examine the impacts of COVID-19 on the health and well-being of 2SGBQ+ men in Manitoba. The study was conducted in collaboration with a community advisory committee (CAC) and the research team. The research team consisted of members connected to health and 2SLGBTQI+ community-based organizations (CBOs). These organizations worked hand-in-hand with our team throughout this research project. The CAC consisted of ten 2SGBQ+ men, representing diverse 2SGBQ+ communities and assisted the research team with the development of the survey, recruitment, and data analysis.The data used in this paper were collected through a cross-sectional online survey method as part of the quantitative phase of This study approaches the worsening of mental health of 2SGBQ+ men in Manitoba during the COVID-19 pandemic as a multi-level challenge, requiring a theory-driven approach, and an understanding of a variety of socio-ecological factors at individual and socio-structural levels , 26. SocParticipants (n = 366) for the online survey were recruited across Manitoba, using printed flyers  at CBOs,CBOs also helped in recruiting 2SGBQ+ men with diverse characteristics and from harder to reach populations, such as Two-Spirit and Indigenous GBQ+ men, racialized 2SGBQ+ men, young adult 2SGBQ+ men, 2SGBQ+ men living in rural settings, and those living with HIV. Participants who did not have access to the internet  were given an option to complete this survey by phone. Eligibility included: 1) Identify as a man (cisgender or transgender); 2) Report any sex with another man in the previous 12 months or identify as two-spirit, gay, bisexual, or queer; 3) Be 18 years of age or older; 4) Live in Manitoba. Survey participants were compensated $35 (CAD).All procedures were approved by the University of Manitoba Research Ethics Board. Informed written consent was obtained from all individual participants included in the study. All data were kept confidential. All procedures performed in studies involving human participants were in accordance with the ethical standard of the institutional and national research committee  and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.The online survey included questions on sociodemographics, and impact of COVID-19 on financial wellbeing and mental health. The analytic outcome in this study was worsening of mental health due to COVID-19.Sociodemographic measures included: 1) age; 2) race/ethnicity ; 3) sexual orientation identity ; 4) relationship status .To understand the experience of any loss of income, we asked participants: “Have you lost any income because of the COVID-19 pandemic?” Answer choices included ‘yes’ and ‘no.’We asked participants if the COVID-19 pandemic has affected their mental health. Individuals who reported that the COVID-19 pandemic has affected their mental health were asked if their mental health was made worse due to the COVID-19 pandemic (with responses ‘yes’ and ‘no’).All data analyses were conducted using SPSS 27 (IBM Corp. 2020). First, descriptive analyses and tests were conducted. Second, bivariate analyses were conducted using Chi-Square tests for categorical variables, as well as t-tests for continuous variables. After significant associations were identified in bivariate tests, multivariable analyses were conducted using binary logistic regression with reported adjusted odds ratios (AOR), and 95% confidence intervals (CI). A logistic regression analysis was used to examine the relationship between worsening of mental health due to the COVID-19 pandemic (outcome/dependent variable), sociodemographic , and loss of income. In the logistic regression model variables were entered using two blocks. In block one, the sociodemographic variables were entered. In block two, the theoretical variable of interest (loss of income) was entered.SD = 6.0).The final sample included 366 2SGBQ+ men. Demographics (participant characteristics) and descriptive data are presented in χ2 = 18.07, df = 6, p = .006), such that White Western European participants were more likely to report worsening mental health. There was also a significant association between the worsening of mental health due to the COVID-19 pandemic and the relationship status of the participants , with participants who were married or partnered less likely to report worsening mental health. Finally, a significant relationship was found between worsening mental health and loss of income due to the COVID-19 pandemic .In the bivariate analysis, there was a significant association between worsening of mental health due to COVID-19 pandemic and ethnicity of the participants , where White Western European men were more likely to report loss of income. A significant association was also found between loss of income and relationship status , so that those who were married or partnered were more likely to experience loss of income.Furthermore, in the bivariate analysis, there was a significant association between loss of income due to the COVID-19 pandemic and ethnicity of the participants . This model had a very good fit with the sample data . The model successfully predicted 72.3% of the cases.The results of the logistic analysis revealed a significant logistic regression model for worsening of mental health due to the COVID-19 pandemic for this sample of 2SGBQ+ men in Manitoba . Furthermore, as compared to participants who were married or partnered, participants who were dating , single , and separated/divorced/widowed  were all significantly more likely to report experiencing worsening of mental health due to the COVID-19 pandemic. No other demographics emerged as significant. Finally, compared to participants who did not experience any loss of income, those who experienced loss of income due to the COVID-19 pandemic were significantly more likely to report experiencing worsening of mental health .With regards to sexual orientation, compared to participants who self-identified as gay, bisexual-identifying participants were less likely to report experiencing a worsening of mental health due to the COVID-19 pandemic  of respondents indicated loss of income during this pandemic, and 55% of the sample indicated worsening of their mental health. The findings from this study revealed a significant association between participants’ relationship status, sexual orientation identity, loss of income and worsening of mental health due to the COVID-19 pandemic. Our findings contribute to the body of research that shows the adverse impact of the COVID-19 pandemic on the mental health of sexual and gender minorities , 19.This study adds nuance to this line of research by elucidating differences among different sexual minority identities. Previous research on the mental health impacts of COVID-19 did not differentiate between various sexual minority groups and identities. Research often generalized 2SGBQ+ mens’ mental health outcomes which, in turn, have overlooked the unique mental health needs of bisexual-identifying men . The finOur findings also showed how relationship status may have implications for individuals’ mental health. Being in a relationship  has significant implications for how people may have experienced social isolation during this pandemic. The influence of public health restrictions on social and physical connections and social isolation has negative implications for mental health among marginalized groups , 35. MenOur study found an association between loss of income and mental health. Our findings are consistent with other research which showed that men in a sexual minority who experienced employment loss due to COVID-19 also reported higher rates of depression and anxiety relative to those without employment loss . HoweverImportantly, the study employed a socio-ecological framework to examine the effects of sociodemographics, socio-structural factors (loss of income due to COVID-19 pandemic) and the ways in which these negatively affect the mental health of cisgender and transgender 2SGBQ+ men, emphasizing the multifaceted nature of person-to-environment influences in the socio-ecological framework. This study’s findings therefore contribute to a socio-ecological understanding of worsening mental health among 2SGBQ+ men during the COVID-19 pandemic.The study results point to several important implications for public health practice, policy, and research. Health professionals who work with 2SGBQ+ men should pay attention to the differences in mental health profiles of bisexual-identifying men and ethno-racialized men in order to better engage these men in public health promotion activities or when designing services for post-pandemic times. Public health intervention efforts may seek to focus on increasing culturally-sensitive and affirming mental health supports, programs, and services for these men.Our findings also underscore the need to develop more robust strategies for sub-populations of 2SGBQ+ men . Specific targeted and tailored public health interventions designed with the unique needs of these sub-populations may be required to increase their access to economic, social, and mental health supports. Future research needs to continue collecting essential demographic data to allow for meaningful comparative data analysis along lines of diversity and intersectionality, including relationship status, income, and sexual identity. Finally, structural and policy changes that prioritize economic and social equity, public health and those that address systemic barriers that maintain racism, biphobia, and social exclusion affecting 2SGBQ+ men are needed to alleviate the observed disparities.Limitations of this study include its nonprobability sampling, selection bias, small sample size, and limited generalizability. Our study may be missing 2SGBQ+ men who lack internet access or are not comfortable responding to a 2SGBQ+-focused online survey in their homes. In addition, since we leveraged the networks of CBOs that serve 2SLGBTQIA+ people to facilitate data collection, our findings may best reflect the experiences of 2SGBQ+ men who are affiliated with or use the services of such organizations. Another limitation in our study was not having standardized mental health measures or precise measures for various mental health concerns. Future research needs to examine the impacts of COVID-19 on specific mental health concerns  for this population using standardized mental health measures. Finally, while an A priori sample size calculation was not conducted, we relied on a theory-driven data-informed approach, which included an A priori theory-driven analysis plan.This study highlighted how COVID-19 may function to deepen existing mental health and socio-economic inequities of 2SGBQ+ men. As the COVID-19 pandemic continues, it is imperative to expand efforts to mitigate the reductions in access to mental health services among 2SGBQ+ men in Manitoba. Continued monitoring of the COVID-19 pandemic’s impacts is of great importance to better understand the evolving economic, social, and mental health needs of this population.


To identify a computed tomography (CT) derived radiomic signature for the options of concurrent chemo-radiotherapy (CCR) in patients with non-small cell lung cancer (NSCLC).A total of 226 patients with NSCLC receiving CCR were enrolled from public dataset, and allocated to discovery and validation sets based on patient identification number. Using CT images of 153 patients in the discovery dataset, we pre-selected a list of radiomic features significantly associated with 5-year survival rate and adopted the least absolute shrinkage and selection operator regression to establish a predictive radiomic signature for CCR treatment. We performed transcriptomic analyzes of the signature, and evaluated its association with molecular lesions and immune landscapes in a dataset with matched CT images and transcriptome data. Furthermore, we identified CCR resistant genes positively correlated with resistant scores of radiomic signature and screened essential resistant genes for NSCLC using genome-scale CRIPSR data. Finally, we combined DrugBank and Genomics of Drug Sensitivity in Cancer databases to excavate candidate therapeutic agents for patients with CCR resistance, and validated them using the Connectivity Map dataset.The radiomic signature consisting of nine features was established, and then validated in the dataset of 73 patients receiving CCR log-rank P = 0.0005, which could distinguish patients into resistance and sensitivity groups, respectively, with significantly different 5-year survival rate. Furthermore, the novel proposed radiomic nomogram significantly improved the predictive performance (concordance indexes) of clinicopathological factors. Transcriptomic analyzes linked our signature with important tumor biological processes . Then, we identified 36 essential resistant genes, and constructed a gene-agent network including 10 essential resistant genes and 35 candidate therapeutic agents, and excavated AT-7519 as the therapeutic agent for patients with CCR resistance. The therapeutic efficacy of AT-7519 was validated that significantly more resistant genes were down-regulated induced by AT-7519, and the degree gradually increased with the enhanced doses.This study illustrated that radiomic signature could non-invasively predict therapeutic efficacy of patients with NSCLC receiving CCR, and indicated that patients with CCR resistance might benefit from AT-7519 or CCR treatment combined with AT-7519. Lung cancer was the first most commonly diagnosed cancer , with noCurrently, most predictive signatures for CCR therapeutic efficacy were constructed based on molecular characterization using genomic and proteomic technologies –9. HowevRadiomics converts medical images into high-throughput quantitative features; this is a new field that could be the vanguard of precision medicine , 13, whiIn this study, using CT images of patients, we develop a non-invasive radiomic signature for patients with locally advanced stage NSCLC receiving CCR, which might help to accurately predict therapeutic efficacy for CCR treatment with improved 5-year survival rate. Subsequently, based on the dataset with matched CT images and gene expression profiles, we characterized the underlying functional pathways reflected by the radiomic features in the signature and tentatively captured the potentially beneficial agents required for the treatment of patients with CCR resistance based on cancer cell lines dataset.https://www.cancerimagingarchive.net/, 2020), including 422 patients previously treated with CCR or radiotherapy. The inclusion criteria of the samples for CCR treatment planning were as follows: 1) available treatment-naive CT scans; 2) confirmed NSCLC; 3) patients treated with CCR; 4) available survival information. Finally, 226 patients with locally advanced stage NSCLC receiving CCR were preselected and divided into discovery (n = 153) and validation (n = 73) datasets based on patient identification number (pid), that is, the 153 patients whose pid wasn’t divisible by 3 were used as a discovery dataset to develop a radiomic signature for CCR treatment, and the remaining 73 patients were assigned to the validation dataset. These details and applications of the analyzed datasets are displayed in In this study, the NSCLC-Radiomics (NR) dataset  with DICNSCLC-Radiogenomics (NRG) dataset  with DIChttps://depmap.org/portal/download/; 2019). Dependency scores for around 17,000 candidate genes were calculated using the CERES algorithm (https://go.drugbank.com/) was used to identify therapeutic agents targeting essential genes.Genome-wide CRISPR screening of NSCLC cells (n = 87) was downloaded from the DepMap portal , which contains responses to 345 anticancer agents across 917 cancer cell lines, with gene expression profiles and half-maximal inhibitory concentrations (IC50) values of cell lines were used to identify potential therapeutic agents.Genomics of Drug Sensitivity in Cancer dataset  , including 12,328 genes. These samples were derived from A549 and HCC515 NSCLC cell lines. The samples treated with AT-7519 were divided into three dose groups: Low (dose < 1 µM), Middle (1 < dose < 10 µM) and High (dose = 10 µM), which was used to validate therapeutic efficacy of AT-7519.Gene expression profiles of 27 samples treated with different concentrations (0.01 - 10 µM) of AT-7519 for 24 hours and the corresponding 124 untreated control samples were downloaded from Connectivity Map dataset   by the Imaging Biomarker Standardization Initiative (The regions of interest (ROI) of CT scans in the NR and NRG datasets were publicly available. In general, the three-dimensional radiomic features that enabled quantification of the tumor characteristics were divided into ten groups according to the following: I) Tumor intensity, II) Shape, III) Texture, IV) wavelet filters, V) Laplacian of Gaussian filters, VI) Logarithm filters, VII) Square filters, VIII) Exponential filters, IX) Gradient filters and X) Squareroot filters features. For the NR and NRG datasets, radiomic feature extraction was performed for each CT scan with ROIs using free and open-source PyRadiomics (v2.2.0) libraries. An extraction intensity bin width was set at 25 HU and the slice thicknesses of all scans were interpolated to a voxel size of 1×1×1 mmitiative . via a linear combination of features in the signature that were weighted by their respective coefficients as follows:In the discovery dataset, radiomic features whose quantitative values were significantly associated with the 5-year survival rate were identified as CCR-associated features. Based on the CCR-associated features, we adopted the least absolute shrinkage and selection operator (LASSO) regression  using “gi represents the i-th feature in the signature; iw represents the weight of the i-th feature derived from LASSO model; iFeatureValue represents the quantitative value of the i-th feature; and n represents the number of features contained in the signature.where The median value of resistant scores of the radiomic signature in the discovery dataset was used as the cut-off value for dividing patients into the resistance (≧ Median) and sensitivity (< Median) groups.The 5-year survival rate of the patients was used as the end point of interest. Patients with more than 5 years follow-up were censored at 5 years because deaths occurring past five years were not likely to be related to CCR treatment. Survival curves were estimated using the Kaplan-Meier method and statistically compared using the log-rank test . To analTo assess the complementary effect of the radiomic signature on the clinical model in predicting the therapeutic efficacy in patients with NSCLC receiving CCR treatment, a radiomic nomogram was constructed using multivariate linear regression analysis (“rms” R package). Additionally, the predictive performance of the radiomic nomogram was evaluated based on C-index, calibration curve, and the decision curve analysis. The net reclassification improvement (NRI)  index waSpearman’s rank correlation was applied to investigate the association between the radiomic signature and clinical parameters. The “clusterProfiler” R package was used to conduct the functional enrichment analysis of the genes that were correlated with the radiomic features based on the current Kyoto Encyclopaedia of Genes and Genomes (KEGG) database, wherein a hypergeometric test was employed.ESTIMATE  was intrt-test was used to examine the intergroup difference by comparing samples treated with potential therapeutic agents and the corresponding untreated control samples. Binomial distribution was used to examine the difference in the distribution of the down-regulated and up-regulated resistance genes induced by the potential therapeutic agents.Student’s P values were adjusted using the Benjamini-Hochberg procedure for multiple tests to control the false discovery rate (FDR). Statistical significance was defined as two-sided P<0.05 or FDR<0.05 for multiple tests.Statistical analyzes were performed using R, version 3.5.3; P < 0.05). The CCR-associated features were selected as inputs for LASSO regression to generate a radiomic signature consisting of nine weighted features  of CCR-9RS, the 35 patients were classified into the resistance group, and exhibited significantly shorter 5-year survival rate than the 38 patients classified into the sensitivity group  and performed functional enrichment analysis for these correlated genes. It was observed that 6 of the 9 features were significantly enriched in 20 functional pathways  with matched CT images and gene expression profiles. Using Spearman’s rank correlation analysis, we identified the significantly correlated genes of each feature in CCR-9RS (P = 0.3685), KRAS mutation (P = 0.8272) and ALK translocation (P = 0.6256). The result indicated that patients with CCR resistance might not benefit from the currently molecular-targeted therapies. Furthermore, we observed that patients with CCR resistance exhibited marginally significantly negatively correlated with immune scores , and significantly negatively correlated with some immune cells , Activated B cells  and Central memory CD4 T cell . The result suggested that patients with CCR resistance had lower infiltration levels predicted by CCR-9RS, who might also not benefit from immunotherapy.We also investigated the association of CCR-9RS with the molecular lesions  and immune landscapes based on Spearman’s rank correlation analysis . Using Spearman’s rank correlation analysis, we found that only the IC50 value of AT-7519 therapeutic agent was significantly negatively associated with the mRNA expression of the target gene (CDK1) in the GDSC dataset . Herein, 124 of the 183 resistance genes were significantly down-regulated induced by AT-7519, including the targeted CDK1 gene of AT-7519 , Middle (n = 8) and High (n = 4), and found that significantly more resistant genes , which is the predictor of the therapeutic efficacy in patients with locally advanced stage NSCLC receiving CCR treatment. The radiomic signature successfully stratified NSCLC patients into the resistance and sensitivity groups with significantly different 5-year survival rate when they receiving CCR treatment. The combination of clinical factors with the radiomic signature in a radiomic nomogram could significantly improve the predictive performance of the clinical evaluation system in the discovery and validation datasets. These results indicated that CCR-9RS could provide additional predictive information for patients within the same clinical factors, and it will be worthwhile to develop this signature as a non-invasive predictive tool for clinical application.P = 0.0138, HR = 1.61, 95% CI: 1.10-2.35, C-index = 0.57, Additionally, we tentatively estimated the predictive performance of CCR-9RS in early stage (stage I-II) NSCLC patients receiving radiotherapy, which is a guideline-recommended treatment for some early stage patients, based on the hypothesis that patients who were resistant to CCR should be resistant to both chemotherapy and radiotherapy. Here, we collected 93 stage I and 40 stage II NSCLC patients receiving radiotherapy in the NR dataset, and found that the resistance patients predicted by CCR-9RS also had significantly shorter 5-year survival rate than the predicted sensitivity patients , providing evidence that patients sensitive to CCR treatment with higher infiltration levels might benefit from CCR treatment in the molecular mechanism. In contrast, patients with CCR resistance might not benefit from targeted therapy or immunotherapy, which requires further analysis of the benefit from other therapeutic agents.via the leveraging the genome-scale CRIPSR data, we pre-selected a set of essential resistant genes, which could be the potential targets of the patients with CCR resistance. Then, we extracted candidate therapeutic agents targeting essential resistant genes and constructed a gene-agent network using the DrugBank database. Finally, we identified AT-7519 as a therapeutic agent for samples with CCR resistance in GDSC dataset. Furthermore, we tentatively validated the therapeutic efficacy of AT-7519 for samples with CCR resistance using the CMap dataset that significantly more resistant genes, positively correlated with the resistant scores of CCR-9RS, were down-regulated induced by AT-7519, and the degree was gradually increased with the enhanced doses of AT-7519. The results suggest that the patients with CCR resistance might benefit from AT-7519 or CCR treatment combined with AT-7519. We additionally explored the underlying correlation of AT-7519 and immunetherapy, and found that AT-7519 induced significant increases in the expression levels of 17 immune inhibitor/checkpoint genes , PDCD1 (P = 0.0298) and IDO1 (P = 6.61E-06). The correlation between AT-7519 and immunotherapy has not been mentioned yet, which merits further exploration. AT-7519 is an ATP competitive CDK inhibitor with effective anti-proliferative activity and has been undertaken or are undergoing the phase I and II clinical trials in a variety of solid tumors, including colorectal cancer . Thereafter, nt genes  in CMap l cancer , cervical cancer  and ovarl cancer . Therefol cancer .This study still had some limitations. First, as a single-center retrospective study, the predictive estimation of CCR-9RS still need to be further validated in multicenter clinical trial studies. Second, our study indicated that the NSCLC patients with CCR resistance might benefit from AT-7519 or CCR treatment combined with AT-7519, which should be further validated in the phase of clinical development for cancer treatment.In conclusion, the radiomic signature developed in this study could be applied to identify patients with NSCLC, who might benefit from CCR treatment prior to treatment, thus allowing clinicians to monitor the progress of patients. Furthermore, AT-7519 was captured as a potentially therapeutic agent for NSCLC patients with CCR resistance, which is worth exploring in future studies.https://www.cancerimagingarchive.net/, 2020), the Genome-wide CRISPR , the GDSC dataset  and the CMap dataset (https://clue.io/data/CMap2020#LINCS2020).The datasets for this study can be found in the NSCLC-Radiomics and NSCLC-Radiogenomics datasets  and the National Undergraduate Innovation and Entrepreneurship Training Program (no. 202110226018).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Obesity-related disease (ORD) places substantial burden on health systems. This review summarises the cost-effectiveness evidence for non-surgical weight management programmes (WMPs) for adults with severe obesity.Severe obesity (BMI ≥ 35 kg/mWhilst evidence shows bariatric surgery is often cost-effective, there is no clear consensus on the cost-effectiveness of non-surgical WMPs.Thirty-two studies were included. Most were short-term evaluations that did not capture the long-term costs and consequences of ORD. Decision models often included only a subset of relevant ORDs, and made varying assumptions about the rate of weight regain over time. A lack of sensitivity analyses limited interpretation of results. Heterogeneity in the definition of WMPs and usual care prevents formal evidence synthesis. We were unable to establish the most cost-effective WMPs. Addressing these limitations may help future studies provide more robust cost-effectiveness evidence for decision makers.The online version contains supplementary material available at 10.1007/s13679-022-00483-z. In 20172 and BMI of 35–40 kg/m2 for people with comorbidities.Economic evaluations are comparative analyses of the costs and benefits of different health care interventions and provide information to help decision makers reach evidence-based decisions on the efficient allocation of scarce health care funding resources. International decision makers, such as the National Institute for Health and Care Excellence (NICE) in the UK and Canadian Agency for Drugs and Technologies in Health (CADTH) in Canada provide funding recommendations on the use of health technologies using economic evidence as an integral part of their decision-making processes. For example, in the UK, NICE published obesity guidance in 2014  that recDespite the substantial health, social and economic burden, there remains a lack of evidence synthesis that clarifies the most effective and cost-effective management strategies for people with severe obesity (and their comorbidities). The aim of this paper is twofold. First, we report the findings of existing cost-effectiveness studies evaluating non-surgical WMPs for people with severe obesity. Secondly, we identify common evaluation challenges, with a view to providing recommendations for the conduct of future obesity economic evaluations.We searched MEDLINE and EMBASE databases from 1980; NHS Economic Evaluation Database (NHS EED), Health Technology Assessment (HTA) database, Cost-effectiveness Analysis Registry, and Research Papers in Economics (RePEc) from inception. Original searches by us up to May 2017 were conducted as part of the REview of Behaviour And Lifestyle interventions for severe obesity: AN evidenCE synthesis (REBALANCE) study ••. Updat2) based on mean or median BMI in source clinical effectiveness studies (or a modelled cohort with (BMI ≥ 35 kg/m2)). Interventions were eligible for inclusion so long as they were a WMP, where the key target of the intervention was weight loss or weight loss maintenance. This also included VLCDs, defined here as ≤ 800 ± 10% kcal/day. Partial economic evaluations such as evaluations of costs alone or outcomes alone, cost-consequence analyses (costs and consequences not compared but reported separately) and methodological studies were all excluded. The only pharmacotherapy included was Orlistat because, at the time of writing, it was the only drug prescribed for weight loss in the UK.English language studies, reporting full economic evaluations, defined as a comparative assessment of two or more non-surgical WMPs , cost-effectiveness analysis (CEA), cost–benefit analysis (CBA) or cost-minimisation analysis (CMA) frameworks) were deemed eligible for inclusion. Eligible populations were adults aged 18 and over, with severe obesity , evaluation approach , scope of evaluation (narrowly defined such as diabetes vs broadly defined multiple ORDs), differences across health care systems, definitions of interventions and comparators. Methodological limitations of the studies were identified and catalogued, with a view to providing guidance for future research.Included studies (in our REBALANCE report ••) were Studies identified in this updated review were assessed against the methodological issues identified in the REBALANCE review to identify whether the quality of studies has improved over time.N = 352 full texts were retrieved and assessed against the inclusion/exclusion criteria. N = 32 studies were finally included in the review (reported in 36 papers). Further details are provided in the PRISMA flow chart  and pharmacotherapies (n = 5). Two studies evaluated both WMPs and pharmacotherapies [n = 13), others (neither RCT-based nor model-based) (n = 4) and decision models (n = 15). The majority of studies were published within the past 10 years (n = 29), and the remainder were published in 2005 (n = 3). The WMPs are further categorised as lifestyle WMPs (n = 25) [n = 4) [n = 2) [n = 1) [n = 6) [n = 5) [Economic evaluations included evaluations of WMPs  –32, 40,  [n = 4) , 29, mea [n = 1) , and rem [n = 6) , 34–36.  [n = 5) –39. SomeThe cost-effectiveness results are presented in Figs. Lifestyle WMPs  included diet and physical activity advice , 31, 40,Four studies •, 27, 29Two meal replacement studies , 11 wereA group intervention  included counselling through a conference call, instead of individually (control group) . The ICEThe interventions that were delivered remotely  were Internet or telephone-based. Other evaluations were for interventions delivered remotely rather than in-person , 35, 36.Five studies  evaluated the cost-effectiveness of Orlistat and low-fat diet and showed mixed results , 37–39. Some interventions were evaluated in multiple studies. Counterweight was deemed cost-effective when compared to no treatment . Howevern = 17). The WMPs considered cost-effective in the longer term (in terms of cost per QALY) in the USA were OPTIFAST (a VLCD) [n = 12) in the longer term were the WMP delivered in a football club [n = 1), Ireland (n = 1) and Australia (n = 1) was Orlistat, with ICERs ranging from €13,125 per QALY (vs placebo plus a low-fat diet) [The majority of studies were conducted in the USA ((a VLCD)  (but wit(a VLCD)    to dominat diet) , 11.Note that all the cost-effectiveness results here are compared against different thresholds, with differing health care systems and methodological quality. Therefore, in the following section, we will assess the methodological quality of the studies.About half of the economic evaluations were trial-based. The follow-up period for most studies ranged between 1 and 2 years. Studies with longer (than 2 years) follow-up periods were 3.5 years •, 5 yearThe following sections reflect the key methodological issues identified in the quality assessment of the included modelling studies. The most common model types were a Markov model and individual level simulation/microsimulation model. The most common framework for analysis was CUA, and the most common benefit measurement was the quality adjusted life year (QALY). The incremental cost effectiveness ratio (ICER) was therefore compared to a commonly used country-specific threshold.Decision model time horizons ranged from 3 years to a lifetime horizon across the studies. 8/15 (53%) of decision models were built on a life-time horizon, which is likely required to capture all the costs and consequences of ORD such as stroke, cancer, diabetes and myocardial infarction. The varying time horizons further limit the comparability between the studies. Short-term decision models, such as those conducted over only 3 years are insufficient for decision making as they fail to capture the long-term benefits of weight loss interventions on ORD and may generate cost-effectiveness conclusions biased against WMPs. However, a counterargument is that longer term extrapolations require assumptions about the impact of transient weight loss on ORD, and assumptions about the long-term rate of weight regain over time (Weight Regain Assumptions). Longer term extrapolations, based on short-term data, add uncertainty to results, with a risk of drawing cost-effectiveness conclusions that are biased towards WMPs. To determine the most likely cost-effectiveness conclusions from a decision model, it is critical that models include a comprehensive range of sensitivity analyses to ascertain the impact of important assumptions such as transient effects and weight regain rates on results.Furthermore, many of the obesity models did not include many of the relevant disease health states such as T2DM, stroke, cardiovascular disease, and obesity-related cancers. Some obesity models •, 31  assumed a constant weight regain rate to baseline (often at 1-kg regain per year or a 5-year regain to baseline weight) or a linear projection of the BMI based on trial data. For the remainder of the studies, it was either unclear, not reported or done differently .The weight regain rate has important implications for cost-effectiveness, particularly in models where the risk of ORD is directly linked to time-specific weight/BMI. Long-term follow-up data on WMPs is frequently lacking and therefore exploring the impact that the weight regain assumption has on results is crucially important. The longest follow-up for WMPs identified in the REBALANCE clinical effectiveness review •• was frThe comparisons identified in this review varied widely. The interventions and comparators differed both between WMP categories and within categories. Lifestyle interventions varied widely and were compared to no active treatment (e.g. country-specific population BMI trajectory) or some form of usual care. VLCDs were compared to WMPs with varying intensity. The meal replacement (Jenny Craig) was compared to different WMPs. The group and remote interventions were compared to in-person lifestyle interventions. Because of the variation in the intervention and comparators, it is difficult to compare across the studies.Sensitivity analyses are key to unravelling the uncertainty in the cost-effectiveness results. Four studies varied the discount rate •, 28, 36The weight regain rate was varied in 4 studies •, 28. InIn the younger age group (aged 20–34), a total diet replacement programme •  • and morOnly three studies •, 25, 362). The cost-effectiveness findings from the WMP and pharmacotherapy studies were mixed. Half of the WMP studies were economic evaluations alongside RCTs, not extrapolating costs and benefits over a longer time horizon, failing to capture the long-term impact of an intervention on obesity, a chronic disease. Furthermore, studies were subject to heterogeneity with regard to the chosen comparators, study populations, settings, decision model structure, costing methodology, weight regain assumptions and time horizons. To our knowledge, this (both our REBALANCE review and updated review) is the first systematic review of economic evaluations of different WMPs for severe obesity (BMI ≥ 35 kg/m2).We identified 32 studies (across 36 papers) evaluating the cost-effectiveness of non-surgical interventions for severe obesity (BMI ≥ 35 kg/m2) rather than severe obesity (BMI ≥ 35 kg/m2), included partial economic evaluations  in addition to full economic evaluations. As in the REBALANCE study, they also found surgery to be cost-effective. One of their included studies [Two reviews have recently been conducted on the cost-effectiveness of interventions for people with obesity , 43. How studies  applied  studies , 46. MorThe quality of the included studies varied. However, as we have learnt from the REBALANCE study, many of these quality assessment items were not captured in the quality assessment checklists. These additional items for the quality assessment checklists would improve the quality assessment of obesity models . FirstlyTwo studies in the review (UK studies) evaluated multiple WMPs and bariatric surgery, however, one with only a 10-year time horizon for costs and outcomes  and the Key strengths of this study are the systematic approach to the literature review in identifying the cost-effectiveness evidence on interventions for severe obesity and the methodological quality assessment of the included studies. Furthermore, this review brings focus to the population with severe obesity, identifying value for money interventions for treating severe obesity.Due to study heterogeneity, no quantitative synthesis of the study results by meta-analysis was attempted, a common issue with systematic reviews of economic evaluations. This is because studies were conducted in different countries with different health care systems, different definitions of comparator groups, model structures, costing methods and modelling assumptions. A detailed quality assessment was not conducted for all included studies, only for those identified through the REBALANCE review, but this informed our subsequent assessment of studies.Most WMPs were cost-effective and pharmacotherapies showed mixed results. However, the cost-effectiveness evidence should be read with caution due to the varying methodological issues and study heterogeneity across the studies. About half of the WMPs were economic evaluations alongside RCTs, not accounting for the difference in long-term costs and outcomes between the considered interventions, crucial for a chronic disease such as obesity. WMPs tended to have short-term follow-up, rendering it even more important to make use of decision models. Decision models did not include most relevant health states and had varying assumptions around weight regain which was rarely explored in sensitivity analysis.Although there exists a decision model assessing different types of interventions ••, thereSupplementary file1 (DOCX 25 KB)Below is the link to the electronic supplementary material.


The study aimed to determine the seroprevalence, the fraction of asymptomatic infections, and risk factors of SARS-CoV-2 infections among the Forcibly Displaced Myanmar Nationals (FDMNs).It was a population-based two-stage cross-sectional study at the level of households.The study was conducted in December 2020 among household members of the FDMN population living in the 34 camps of Ukhia and Teknaf Upazila of Cox’s Bazar district in Bangladesh.Among 860 697 FDMNs residing in 187 517 households, 3446 were recruited for the study. One individual aged 1 year or older was randomly selected from each targeted household.Blood samples from respondents were tested for total antibodies for SARS-CoV-2 using Wantai ELISA kits, and later positive samples were validated by Kantaro kits.More than half (55.3%) of the respondents were females, aged 23 median (IQR 14–35) years and more than half (58.4%) had no formal education. Overall, 2090 of 3446 study participants tested positive for SARS-CoV-2 antibody. The weighted and test adjusted seroprevalence (95% CI) was 48.3% (45.3% to 51.4%), which did not differ by the sexes. Children (aged 1–17 years) had a significantly lower seroprevalence 38.6% (95% CI 33.8% to 43.4%) compared with adults . Almost half  of seropositive individuals reported no relevant symptoms since March 2020. Antibody seroprevalence was higher in those with any comorbidity  than those without . Multivariate logistic regression analysis of all subjects identified increasing age and education as risk factors for seropositivity. In children (≤17 years), only age was significantly associated with the infection.In December 2020, about half of the FDMNs had antibodies against SARS-CoV-2, including those who reported no history of symptoms. Periodic serosurveys are necessary to recommend appropriate public health measures to limit transmission. This study is one of the largest serosurveys in COVID-19 pandemic among displaced/refugee population.Two-stage population-based design of the study with households identified randomly from the Family Counting Number using probability proportionate to size of the camps and participants recruited randomly from each selected households with a balanced number of males and females, ensured generalisability and gender equity in data collection.Seroprevalence estimates were adjusted to account for test sensitivity and specificity of Wantai test kit validated by Kantaro to address issue of cross-reactivity.The household roster could not be completed for all of targeted subjects because the listing of households was done several months before the survey. Non-response at the household level was higher than expected. This might have caused overestimation of the seroprevalence, which was addressed by population and household level weighting.The study outcome relied on testing of antibody response, estimates of which might be affected by the waning antibody levels over time and the Wantai ELISA might have cross-reactivity with antibodies to other common human coronaviruses, leading to a false-positive result in some instances.A novel pathogen’s epidemiological and serological characteristics at the beginning of a pandemic remain uncertain. After the initial detection of SARS-CoV-2 in Wuhan of China in December 2019,4The global and regional data on the seroprevalence of antibodies against SARS-CoV-2 ranged during 2020 from 1.4% to 26.0% in countries, such as the USA,16As of 31 March 2022, 5902 COVID-19 confirmed cases were reported from the FDMN camps with 42 deaths.The aim of this survey, which has been adapted from the WHO Unity Study Protocol,2 on average with some places reaching 70 000.21We conducted this population-based two-stage cross-sectional survey in December 2020. The study was done on household members of the FDMN population using an adapted version of the WHO Unity Study Protocol.10.1136/bmjopen-2022-066653.supp1Supplementary dataThe sample size was calculated using an online tool for calculating proportion (OpenEpi).In each camp, households were identified randomly by the Family Counting Number (FCN) using probability proportionate to size. Each identified household was assigned either as male or as female to ensure a balanced sample of males and females. One individual aged 1 year or more was selected from each identified household using a random number generator. Informed written consent was collected from the recruited respondents or their parents or legal guardians in case of children. Written assent was obtained from children aged 12–17 years. People who had any contraindication to venepuncture, such as bruising, swelling, infection or broken skin, were excluded from the study .The questionnaire was based on the WHO Unity ProtocolThe field teams consisted of doctors (team lead), nurses, laboratory technologists, paramedics and enumerators who were proficient in the local dialect. Forty-nine teams were deployed to collect data. Each field team comprised of two data collectors and one enumerator of the same sex. Female teams collected samples from households assigned as female and male teams collected samples from households assigned as male. In addition, community health workers/community leaders (Majhis) provided support to the teams to locate the selected households. The field teams’ training included interactive discussions on all questions, mock interviews and blood sample collection exercises.Sensitisation meetings in all 34 FDMN camps were arranged before initiating data collection by the Camp In-charge assigned by the Government. Support from local religious leaders , Majhis, site management staff and community health workers of relevant partner organisations were sought to conduct the study. A flipchart developed by WHO was used to aid these sensitisation meetings.A 5 mL (3 mL for young children) of venous blood was collected with aseptic precautions from each of the respondents. The samples were transported to specimen collection hubs maintaining proper labelling and temperature (2–8°C). At the hubs (one for each Upazila), the samples were checked for haemolysis, lipaemia and inadequate quantity and sorted and sent to the IEDCR field laboratory at Cox’s Bazar Medical College, Cox’s Bazar for centrifugation at 3000 rpm for 20 min and labelled appropriately in aliquots  for preservation at −80°C.Samples with laboratory identification numbers were screened for the presence of SARS-CoV-2 virus-specific antibodies using Wantai SARS-CoV-2 Antibody ELISA kitThe blood samples were tested for the qualitative detection of total antibodies (IgG and IgM) to SARS-CoV-2 using a two-step incubation antigen ‘sandwich’ enzyme immunoassay where the receptor-binding domain of SARS-CoV-2 spike protein was present. The patient’s serum specimen was added during the first incubation, and a second recombinant SARS-CoV-2 antigen was added during the second incubation, respectively. The microwells were then washed to remove unbound conjugate, and chromogen solutions were added to identify the amount of antibody captured inside the well. Wells containing specimens negative for SARS-CoV-2 antibodies remained colourless.The positive test results were validated by Kantaro test kitsThe data collection procedure and teams were supervised by trained physicians. Independent monitoring visits were made by the investigators and other officials familiar with the study protocol. For external validation and quality control, 5% of the blood samples (positive and negative) were retested using Wantai kits at the IEDCR laboratory in Dhaka, the reference laboratory for SARS-CoV-2 in Bangladesh. Again, positive test results for antibodies to SARS-CoV-2 by Wantai test kits were validated by Kantaro test kits at the IEDCR reference laboratory.et al.Data were downloaded from the cloud and merged with laboratory test results with unique identification numbers of respondents. After cleaning the dataset, weighting for FDMN population2 was used to test the independence between seropositive and categorical factors.Categorical variables were reported as numbers and percentages, and continuous variables by median (IQR) or mean (SD) as appropriate. χOur estimation of the seroprevalence of SARS-CoV-2 proceeded in two steps. First, we estimated the weighted seroprevalence of SARS-CoV-2 to represent the FDMN population. Later we used the Rogan-Gladen formula to adjust seroprevalence estimates to account for test sensitivity and specificity of Wantai validated by Kantaro.ORs and their 95% CIs were obtained using logistic regression analysis to identify the factors associated with weighted test adjusted seroprevalence. Multiple logistic regression models were applied on weighted data for children (1–17 years), adults (18 years and above) and all subjects combined. All variables were entered simultaneously into the multiple regression models. However, smoking could be used for adults only because smoking data were not collected for children. Alpha was set at 5% for considering statistical significance. All analyses were conducted using IBM SPSS V.26.0 and R V.4.1.1.Sensitisation meetings were conducted in all FDMN camps before initiating data collection to ensure public involvement. Local religious leaders  and Majhis were involved to implement the study, especially for locating the households.Patients or the public were not involved in the study design, or reporting, or dissemination plans.Among the estimated sample size of 6202, the study teams were successful in locating 4398 households yielding a response rate of 86.4%. Among the selected households, 3678 respondents  completed the individual interview and provided blood samples. Among the collected blood samples, 232 were rejected in the laboratory. Therefore, blood samples from 3446 respondents completed the laboratory test leading to a response rate of 93.7%. Data from these 3446 respondents with completed laboratory tests were used for the final analysis. Based on these three response rates, the overall response rate was 70.6% . All resMore than half of the respondents (55.3%) were female. More than two-thirds (69.4%) of them were aged 18 years or above. The median age (IQR) of all participants was 23 (14–35) years, which was not different by the sexes. More than half of the school eligible (age≥6 years) population reported no formal schooling (58.4%). Most of the respondents reported receiving BCG vaccination (86.3%). Nearly half (42.8%) of adult males (18 years and above) reported smoking tobacco, compared with 7.2% of females. About one in eight (12.5%) reported at least one comorbid condition. More than one-third (37.0%) of the respondents reported having at least one relevant symptom of COVID-19 disease since March 2020. The symptoms included fever, cough, dyspnoea, anosmia, chills, fatigue, sore throat, headache, ageusia, nausea, vomiting, diarrhoea, wheezing, chest pain, rhinorrhoea, conjunctivitis, other respiratory symptoms, muscle aches, myalgia, rash, loss of appetite, nosebleed and seizures .Overall, 2090  of 3446 study participants tested positive for SARS-CoV-2 antibody with the Wantai kit . The oveThe prevalence of SARS- CoV-2 antibody was higher in adults than in children, irrespective of their self-report of symptoms . HoweverUnivariate logistic regression analysis identified increasing age , increasing education level , history of relevant symptoms  and presence of co-morbid conditions  as the factors associated with higher risk of seropositivity, whereas smoking  was associated with lower risk of seropositivity. In multiple logistic regression for all subjects adjusted for age, sex, camp locations, BCG vaccination, history of symptoms and comorbid conditions, the association for higher risk of seropositivity persisted for increasing age  and higher education  only. However, after adding smoking to the model for adults (aged 18–94 years because smoking data were not collected for children), respondents who smoked had a lower risk of infection with SARS-CoV-2 . In addition, respondents who had a history of one or more symptoms had higher odds  of having the infection .We report a high seroprevalence of antibodies  to SARS-CoV-2 virus (48.3%) using a cross-sectional survey conducted in December 2020 among FDMNs living in Cox’s Bazar district of Bangladesh. This high seroprevalence contrasts with a very low number of infections detected by RT-PCR before the study period. This probably is not surprising as other studies had similar observations even with low seroprevalence, like nationally representative study in Sierra Leone in March 2021 reported weighted seroprevalence of only 2.6%, but it was 43 times higher than the reported number of cases.31et al.33Although we found a higher seroprevalence (58.1%) in adults than children aged 1–17 years (38.6%), there was no difference in seroprevalence for older adults (aged 60 and above years) compared with middle-aged adults (aged 40–59 years), which supports the findings of the systematic review by Levin Nearly half 45.7% of the people who did not report any symptom since March 2020 also had evidence of SARS-CoV-2 virus infection. They might have had very mild symptoms to recall correctly or were in fact asymptomatic. The younger age profile of the FDMN population also might explain the higher proportion of mild or asymptomatic infection.This study examines SARS-CoV-2 infections among those temporarily or chronically experiencing homelessness (or otherwise living in precarious housing) during the COVID-19 pandemic. The FDMN community has intrinsic differences from the host community, especially regarding awareness of COVID-19 issues and access to related information. Although their access to healthcare was beyond the scope of this survey, understanding the risk and needs of this group is crucial to containing the spread of the pandemic.Our study findings indicated that by December 2020, almost half of the FDMN people were exposed to the SARS-CoV-2 infections, when population vaccination for COVID-19 yet to be started in the country. Another study in Sitakunda subdistrict in the Chattogram division  of Bangladesh conducted in April 2021 has found 63.1% seroprevalence, which was conducted later than our study so expected to have more cumulative seropositive population and sample size, geographical context and type of antibody assessment were different than the current study.Like others,Our study among FDMN population had several limitations. The studies relying on antibody response have their inherent limitations. The estimates reported here might be affected by the waning antibody levels over time. However, it should be noted that vaccination for SARS-CoV-2 had not been initiated in Bangladesh (and hence among the study population) at the time of the study, and therefore was not likely to influence the seroprevalence estimates. The household roster could be completed for about 71% of targeted subjects  because 51The findings of this study imply that the prevalence of seropositivity is likely to be much higher than the presumed RT-PCR confirmed rates in FDMN population. A high seroprevalence across age and sex groups, irrespective of a history of symptoms, may indicate an inadequacy of infection control measures. Because of a very high population density in the camps, social distancing may not work. Other well-known public health measures, such as face masks and mass vaccination, are warranted.