Impact of tapering targeted therapies (bDMARDs or JAKis) on the risk of serious infections and adverse events of special interest in patients with rheumatoid arthritis or spondyloarthritis : a systematic analysis of the literature and meta-analysis

To systematically review the impact of tapering targeted therapies (bDMARDs or JAKis) on the risk of serious infections and severe adverse events (SAEs) in patients with rheumatoid arthritis (RA) or axial spondyloarthritis (axSpA) in remission or low disease activity (LDA) state. A meta-analysis based on a systematic review of PubMed, Embase, Cochrane, until August 2019, as well as relevant databases of international conferences, was used to evaluate the risk difference (RD) at 95% confidence interval (95% CI) of incidence density of serious infections, SAEs, malignancies, cardiovascular adverse events (CV AEs), or deaths after tapering (dose reduction or spacing) compared to continuation of targeted therapies. Of the 1957 studies initially identified, 13 controlled trials (9 RA and 4 SpA trials) were included in the meta-analysis. 1174 patient-years were studied in the tapering group (TG) versus 1086 in the usual care group (UC). There were 1.7/100 patient-year (p-y) serious infections in TG versus 2.6/100 p-y in UC (RD (95% CI) 0.01 (0.00 to 0.02), p = 0.13) and 7.4/100 p-y SAEs in TG versus 6.7/100 p-y in UC (RD 0.00 (− 0.02 to 0.02), p = 0.82). The risk of malignancies, CV AEs, or deaths did not differ between the tapering and the usual care groups. Subgroup analysis (RA and SpA) detected no significant differences between the two groups. We could not show significant impact of tapering bDMARD or JAKi over continuation concerning the risk of serious infections, SAEs, malignancies, CV AEs, or deaths in RA and SpA patients in remission or LDA state

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression

Drug-microbiota interactions and treatment response: Relevance to rheumatoid arthritis

Knowledge about associations between changes in the structure and/or function of intestinal microbes (the microbiota) and the pathogenesis of various diseases is expanding. However, interactions between the intestinal microbiota and different pharmaceuticals and the impact of these on responses to treatment are less well studied. Several mechanisms are known by which drug-microbiota interactions can influence drug bioavailability, efficacy, and/or toxicity. This includes direct activation or inactivation of drugs by microbial enzymes which can enhance or reduce drug effectiveness. The extensive metabolic capabilities of the intestinal microbiota make it a hotspot for drug modification. However, drugs can also influence the microbiota profoundly and change the outcome of interactions with the host. Additionally, individual microbiota signatures are unique, leading to substantial variation in host responses to particular drugs. In this review, we describe several known and emerging examples of how drug-microbiota interactions influence the responses of patients to treatment for various diseases, including inflammatory bowel disease, type 2 diabetes and cancer. Focussing on rheumatoid arthritis (RA), a chronic inflammatory disease of the joints which has been linked with microbial dysbiosis, we propose mechanisms by which the intestinal microbiota may affect responses to treatment with methotrexate which are highly variable. Furthering our knowledge of this subject will eventually lead to the adoption of new treatment strategies incorporating microbiota signatures to predict or improve treatment outcomes

Results From the Global Rheumatology Alliance Registry

Funding Information: We acknowledge financial support from the ACR and EULAR. The ACR and EULAR were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.Objective: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.publishersversionepub_ahead_of_prin

De Novo assembly and transcriptome analysis of the mediterranean fruit fly ceratitis capitata early embryos

The agricultural pest Ceratitis capitata, also known as the Mediterranean fruit fly or Medfly, belongs to the Tephritidae family, which includes a large number of other damaging pest species. The Medfly has been the first non-drosophilid fly species which has been genetically transformed paving the way for designing geneticbased pest control strategies. Furthermore, it is an experimentally tractable model, in which transient and transgene-mediated RNAi have been successfully used. We applied Illumina sequencing to total RNA preparations of 8-10 hours old embryos of C. capitata, This developmental window corresponds to the blastoderm cellularization stage. In summary, we assembled 42,614 transcripts which cluster in 26,319 unique transcripts of which 11,045 correspond to protein coding genes; we identified several hundreds of long ncRNAs; we found an enrichment of transcripts encoding RNA binding proteins among the highly expressed transcripts, such as CcTRA-2, known to be necessary to establish and, most likely, to maintain female sex of C. capitata. Our study is the first de novo assembly performed for Ceratitis capitata based on Illumina NGS technology during embryogenesis and it adds novel data to the previously published C. capitata EST databases. We expect that it will be useful for a variety of applications such as gene cloning and phylogenetic analyses, as well as to advance genetic research and biotechnological applications in the Medfly and other related Tephritidae

High risk of osteoporotic fracture in Parkinson's disease: Meta-analysis, pathophysiology and management

International audienc

Subcutaneous Tocilizumab in Monotherapy or in Combination with DMARD in Patients with Moderate to Severe Active Rheumatoid Arthritis: Observational Study to Describe Real-World Drug Retention Rate at 12 Months, Interim Analysis

International audienc