Genalyte (United States)

Abstract Background and Aims Currently, risk assessment in chronic kidney disease (CKD) primarily relies on urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR), as integrated by the kidney disease: improving global outcomes (KDIGO) risk classification. However, these measures are subject to biological and analytical variability, hampering risk stratification. KidneyintelX.dkd, a composite risk score combining clinical variables and biomarkers at baseline, aims to refine risk stratification in diabetic kidney disease (DKD). This study investigates the correspondence between KDIGO and kidneyintelX.dkd risk assessment, focusing on their predictive power and clinical utility in a large cohort of patients with type 2 diabetes (T2D) and a broad range of CKD. Method We measured tumor necrosis factor receptor (TNFR)-1), TNFR-2, and kidney injury molecule (KIM-1) on banked plasma samples from CANVAS and CREDENCE participants, and executed kidneyintelX.dkd at baseline and year 1. Participants with a KDIGO low-risk classification (eGFR ≥60 mL/min/1.73 m² with UACR <30 mg/g) were excluded, as kidneyintelX.dkd is not intended for this group. We analyzed event rates across risk categories for a composite kidney outcome of 40% decline in eGFR or kidney failure. Results We included 2954 participants (mean eGFR 61.8 mL/min/1.73 m2; median UACR 518.0 mg/g) with available plasma samples and a moderate to very high KDIGO risk. At baseline, kidneyintelX.dkd scored 73.0% of participants with a moderate KDIGO risk as low risk and 27.0% as moderate. Among high KDIGO risk, 12.8% were classified as low risk, 58.8% as moderate and 28.5% as high by kidneyintelX.dkd. In the highest KDIGO risk group, 3.9% were classified as low, 44.3% as moderate and 51.8% as high by kidneyintelX.dkd. Amongst each KDIGO risk stratum, kidneyintelX.dkd further stratified participants for the risk of the composite outcome, with hazard ratios of 10.2 and 16.5 for high vs. low kidneyintelX.dkd in the high and very high risk KDIGO strata, respectively (Fig. 1). The number needed to treat (NNT) for canagliflozin was lower for kidneyintelX.dkd high risk (NNT 18) compared to KDIGO very high risk (NNT 29). Moreover, changes in kidneyintelX.dkd risk category from baseline to year 1 were more strongly associated with the outcome compared to changes in KDIGO risk categories (Fig. 2). Conclusion KidneyintelX.dkd aligns well with KDIGO but provides more nuanced risk categorization within each KDIGO risk stratum. KidneyintelX.dkd showed improved identification of patients at high risk, reflecting a potential for higher absolute benefits of treatment with SGLT2i. Changes in kidneyintelX.dkd risk levels from baseline to 1 year also associated with subsequent risk more effectively than changes in KDIGO risk categories. In conclusion, kidneyintelX.dkd may further refine risk stratification and inform more efficient treatment allocation compared to kidney clinical metrics alone.

ObjectivesOur aim was to evaluate systemic lupus erythematosus (SLE) disease activity and SARS-CoV-2-specific immune responses after BNT162b2 vaccination.MethodsIn this prospective study, disease activity and clinical assessments were recorded from the first dose of vaccine until day 15 after the second dose in 126 patients with SLE. SARS-CoV-2 antibody responses were measured against wild-type spike antigen, while serum-neutralising activity was assessed against the SARS-CoV-2 historical strain and variants of concerns (VOCs). Vaccine-specific T cell responses were quantified by interferon-γ release assay after the second dose.ResultsBNT162b2 was well tolerated and no statistically significant variations of BILAG (British Isles Lupus Assessment Group) and SLEDAI (SLE Disease Activity Index) scores were observed throughout the study in patients with SLE with active and inactive disease at baseline. Mycophenolate mofetil (MMF) and methotrexate (MTX) treatments were associated with drastically reduced BNT162b2 antibody response (β=−78, p=0.007; β=−122, p<0.001, respectively). Anti-spike antibody response was positively associated with baseline total immunoglobulin G serum levels, naïve B cell frequencies (β=2, p=0.018; β=2.5, p=0.003) and SARS-CoV-2-specific T cell response (r=0.462, p=0.003). In responders, serum neutralisation activity decreased against VOCs bearing the E484K mutation but remained detectable in a majority of patients.ConclusionMMF, MTX and poor baseline humoral immune status, particularly low naïve B cell frequencies, are independently associated with impaired BNT162b2 mRNA antibody response, delineating patients with SLE who might need adapted vaccine regimens and follow-up.

Aims: Improvements in information technology have granted the recent development of rapid, cloud-enabled, onsite laboratory testing for rheumatoid arthritis (RA). This study aims to quantify the value to payers of such technologies.Materials and methods: To calculate the value of rapid, cloud-enabled, onsite laboratory testing to diagnose RA relative to traditional, centralized laboratory testing, an Excel-based decision tree model was created that simulated potential cost-savings to payers who cover routine evaluations of RA patients in the US. First, a conceptual framework was created to identify the value components of rapid, cloud-enabled onsite testing. Second, value associated with patient time savings, savings on visit fees, change in treatment costs, and QALY improvements was measured, leveraging existing literature and information from an observational study. Lastly, these value components were combined to estimate the total incremental value accruing to payers per patient-year relative to centralized laboratory testing.Results: Rapid, cloud-enabled, onsite testing is estimated to save one office and 1.81 laboratory visits during the evaluation period for the average patient. Results from an observational study found that rapid, cloud-enabled testing increased the likelihood of completing diagnostic orders from 84.5% to 97%, resulting in an increased probability of early treatment (3.5 percentage points) with disease-modifying anti-rheumatic drugs among patients eligible for treatment. The combined total value was $5,648 per evaluated patient-year. This value is primarily attributed to health benefits of early treatment ($5,048), fewer visit payments ($459), and patient time savings due to fewer office ($216) and laboratory visits ($255).Limitations and conclusions: Data on the impact of rapid, cloud-enabled, onsite testing on patient health, care delivery, and clinical decision-making is scarce. More robust real-world data would confirm the validity of our model. Rapid, cloud-enabled, onsite testing has the potential to generate significant value to payers.

This study presents the first direct comparison of the influence of liquid-crystal order during synthesis on the thermo-mechanical behaviors of main-chain liquid-crystal elastomers (LCEs) in thiol-acrylate networks. Six polydomain nematic elastomer (PNE) chemistries were compared directly by synthesizing with the mesogens in either an isotropic state (i-PNE) or a nematic state (n-PNE). The i-PNE networks were created in the presence of solvent, which disrupted any liquid-crystal order during network formation. Conversely, the n-PNE networks were created without the presence of solvent below the isotropic transition (TNI). Differential scanning calorimetry (DSC) was first performed, and it showed that i-PNE networks experienced a clearly defined nematic-to-isotropic transition upon heating, whereas the transition in n-PNE networks was unable to be identified, which may be the result of a nematic-to-paranematic phase transition. Dynamic mechanical analysis (DMA) tests revealed that while both networks maintained elevated loss tangent in the nematic region, only i-PNE networks prominently displayed dynamic soft elasticity behavior. The two-way shape switching behaviors of LCE networks were examined using actuation tests under a 100 kPa bias stress. It showed that the strain amplitude strongly depends on synthesis history; it ranges from 66% to 126% in i-PNE samples and 3% to 61% in n-PNE samples. To help interpret the different actuation strain behaviors between i-PNEs and n-PNEs, wide-angle X-ray scattering (WAXS) was then performed where the LCE samples were strained to 40%. The results showed that order parameter (S) in n-PNE samples (ranging from 0.37 to 0.50) is lower than that in i-PNE samples (0.54 for all cases), and the parameter decreased as the cross-linking density increased. The stress-strain behaviors of the LCE networks measured from uniaxial tension tests revealed that all i-PNE samples had a lower soft-elasticity plateau during loading compared to the n-PNE samples. Finally, free-standing strain recovery of LCE samples after being strained to 100% was investigated. Immediately after removing stress on the samples, i-PNE and n-PNE samples recovered 14% to 38% and 27% to 73% of strain, respectively. We discuss the advantages and disadvantages of the different synthetic histories on LCE design.