| 1. |
- Bursill, D., et al.
(författare)
-
Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) consensus statement regarding labels and definitions of disease states of gout
- 2019
-
Ingår i: Annals of the Rheumatic Diseases. - : BMJ. - 0003-4967 .- 1468-2060. ; 78:11, s. 1592-1600
-
Tidskriftsartikel (refereegranskat)abstract
- Objective There is a lack of standardisation in the terminology used to describe gout. The aim of this project was to develop a consensus statement describing the recommended nomenclature for disease states of gout. Methods A content analysis of gout-related articles from rheumatology and general internal medicine journals published over a 5-year period identified potential disease states and the labels commonly assigned to them. Based on these findings, experts in gout were invited to participate in a Delphi exercise and face-to-face consensus meeting to reach agreement on disease state labels and definitions. Results The content analysis identified 13 unique disease states and a total of 63 unique labels. The Delphi exercise (n=76 respondents) and face-to-face meeting (n=35 attendees) established consensus agreement for eight disease state labels and definitions. The agreed labels were as follows: 'asymptomatic hyperuricaemia', 'asymptomatic monosodium urate crystal deposition', 'asymptomatic hyperuricaemia with monosodium urate crystal deposition', 'gout', 'tophaceous gout', 'erosive gout', 'first gout flare' and 'recurrent gout flares'. There was consensus agreement that the label 'gout' should be restricted to current or prior clinically evident disease caused by monosodium urate crystal deposition (gout flare, chronic gouty arthritis or subcutaneous tophus). Conclusion Consensus agreement has been established for the labels and definitions of eight gout disease states, including 'gout' itself. The Gout, Hyperuricaemia and Crystal-Associated Disease Network recommends the use of these labels when describing disease states of gout in research and clinical practice.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Vazquez, SE, et al.
(författare)
-
Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq
- 2022
-
Ingår i: bioRxiv : the preprint server for biology. - : Cold Spring Harbor Laboratory.
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.
|
|
| 10. |
|
|
