Targeted genotyping of COVID-19 patients reveals a signature of complement C3 and factor B coding SNPs associated with severe infection.

We have attempted to explore further the involvement of complement components in the host COVID-19 (Coronavirus disease-19) immune responses by targeted genotyping of COVID-19 adult patients and analysis for missense coding Single Nucleotide Polymorphisms (coding SNPs) of genes encoding Alternative pathway (AP) components. We have identified a small group of common coding SNPs in Survivors and Deceased individuals, present in either relatively similar frequencies (CFH and CFI SNPs) or with stark differences in their relative abundance (C3 and CFB SNPs). In addition, we have identified several sporadic, potentially protective, coding SNPs of C3, CFB, CFD, CFH, CFHR1 and CFI in Survivors. No coding SNPs were detected for CD46 and CD55. Our demographic analysis indicated that the C3 rs1047286 or rs2230199 coding SNPs were present in 60 % of all the Deceased patients (n = 25) (the rs2230199 in 67 % of all Deceased Males) and in 31 % of all the Survivors (n = 105, p = 0.012) (the rs2230199 in 25 % of all Survivor Males). When we analysed these two major study groups using the presence of the C3 rs1047286 or rs2230199 SNPs as potential biomarkers, we noticed the complete absence of the protective CFB rs12614 and rs641153 coding SNPs from Deceased Males compared to Females (p = 0.0023). We propose that in these individuals, C3 carrying the R102G and CFB lacking the R32W or the R32Q amino acid substitutions, may contribute to enhanced association dynamics of the C3bBb AP pre-convertase complex assembly, thus enabling the exploitation of the activation of the Complement Alternative pathway (AP) by SARS-CoV-2.

Unmet need in rheumatology: reports from the Advances in Targeted Therapies meeting, 2022.

To detail the unmet clinical and scientific needs in the field of rheumatology. After a 2-year hiatus due to the SARS-CoV-2 pandemic, the 22nd annual international Advances in Targeted Therapies meeting brought together more than 100 leading basic scientists and clinical researchers in rheumatology, immunology, epidemiology, molecular biology and other specialties. Breakout sessions were convened with experts in five rheumatological disease-specific groups including: rheumatoid arthritis (RA), psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus and connective tissue diseases (CTDs). In each group, experts were asked to identify and prioritise current unmet needs in clinical and translational research, as well as highlight recent progress in meeting formerly identified unmet needs. Clinical trial design innovation was emphasised across all disease states. Within RA, developing therapies and trials for refractory disease patients remained among the most important identified unmet needs and within lupus and spondyloarthritis the need to account for disease endotypes was highlighted. The RA group also identified the need to better understand the natural history of RA, pre-RA states and the need ultimately for precision medicine. In CTD generally, experts focused on the need to better identify molecular, cellular and clinical signals of early and undifferentiated disease in order to identify novel drug targets. There remains a strong need to develop therapies and therapeutic strategies for those with treatment-refractory disease. Increasingly it is clear that we need to better understand the natural history of these diseases, including their 'predisease' states, and identify molecular signatures, including at a tissue level, which can facilitate disease diagnosis and treatment. As these unmet needs in the field of rheumatic diseases have been identified based on consensus of expert clinicians and scientists in the field, this document may serve individual researchers, institutions and industry to help prioritise their scientific activities.

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices.

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Real-Life Effectiveness and Safety of Baricitinib as Adjunctive to Standard-of-Care Treatment in Hospitalized Patients With Severe Coronavirus Disease 2019.

BACKGROUND: Therapeutic options for hospitalized patients with severe coronavirus disease 2019 (sCOVID-19) are limited. Preliminary data have shown promising results with baricitinib, but real-life experience is lacking. We assessed the safety and effectiveness of add-on baricitinib to standard-of-care (SOC) including dexamethasone in hospitalized patients with sCOVID-19. METHODS: This study is a 2-center, observational, retrospective cohort study of patients with sCOVID-19, comparing outcomes and serious events between patients treated with SOC versus those treated with SOC and baricitinib combination. RESULTS: We included 369 patients with sCOVID-19 (males 66.1%; mean age 65.2 years; median symptom duration 6 days). The SOC was administered in 47.7% and combination in 52.3%. Patients treated with the combination reached the composite outcome (intensive care unit [ICU] admission or death) less frequently compared with SOC (22.3% vs 36.9%, P = .002). Mortality rate was lower with the combination in the total cohort (14.7% vs 26.6%, P = .005), and ICU admission was lower in patients with severe acute respiratory distress syndrome (29.7% vs 44.8%, P = .03). By multivariable analysis, age (odds ratio [OR] = 1.82, 95% confidence interval [CI] = 1.36-2.44, per 10-year increase), partial pressure of oxygen/fraction of inspired oxygen ratio (OR = 0.60, 95% CI = .52-0.68, per 10 units increase), and use of high-flow nasal cannula (OR = 0.34; 95% CI, .16-0.74) were associated with the composite outcome, whereas baricitinib use was marginally not associated with the composite outcome (OR = 0.52; 95% CI, .26-1.03). However, baricitinib use was found to be significant after inverse-probability weighted regression (OR = 0.93; 95% CI, .87-0.99). No difference in serious events was noted between treatment groups. CONCLUSIONS: In real-life settings, addition of baricitinib to SOC in patients hospitalized with sCOVID-19 is associated with decreased mortality without concerning safety signals.

The Activin/Follistatin Axis Is Severely Deregulated in COVID-19 and Independently Associated With In-Hospital Mortality.

BACKGROUND: Activins are members of the transforming growth factor-ß superfamily implicated in the pathogenesis of several immunoinflammatory disorders. Based on our previous studies demonstrating that overexpression of activin-A in murine lung causes pathology sharing key features of coronavirus disease 2019 (COVID-19), we hypothesized that activins and their natural inhibitor follistatin might be particularly relevant to COVID-19 pathophysiology. METHODS: Activin-A, activin-B, and follistatin were retrospectively analyzed in 574 serum samples from 263 COVID-19 patients hospitalized in 3 independent centers, and compared with demographic, clinical, and laboratory parameters. Optimal scaling with ridge regression was used to screen variables and establish a prediction model. RESULT: The activin/follistatin axis was significantly deregulated during the course of COVID-19, correlated with severity and independently associated with mortality. FACT-CLINYCoD, a scoring system incorporating follistatin, activin-A, activin-B, C-reactive protein, lactate dehydrogenase, intensive care unit admission, neutrophil/lymphocyte ratio, age, comorbidities, and D-dimers, efficiently predicted fatal outcome (area under the curve [AUC], 0.951; 95% confidence interval, .919-.983; P <10-6). Two validation cohorts indicated similar AUC values. CONCLUSIONS: This study demonstrates a link between activin/follistatin axis and COVID-19 mortality and introduces FACT-CLINYCoD, a novel pathophysiology-based tool that allows dynamic prediction of disease outcome, supporting clinical decision making.