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Immune-mediated inflammatory diseases (IMIDs) are typically characterised by relapsing and remitting flares of inflammation. However, the unpredictability of disease flares impedes their study. Addressing this critical knowledge gap, we use the experimental medicine approach of immunomodulatory drug withdrawal in rheumatoid arthritis (RA) remission to synchronise flare processes allowing detailed characterisation. Exploratory mass cytometry analyses reveal three circulating cellular subsets heralding the onset of arthritis flare – CD45RO + PD1 hi CD4 + and CD8 + T cells, and CD27 + CD86 + CD21 - B cells – further characterised by single-cell sequencing. Distinct lymphocyte subsets including cytotoxic and exhausted CD4 + memory T cells, memory CD8 + CXCR5 + T cells, and IGHA1 + plasma cells are primed for activation in flare patients. Regulatory memory CD4 + T cells (Treg cells) increase at flare onset, but with dysfunctional regulatory marker expression compared to drug-free remission. Significant clonal expansion is observed in T cells, but not B cells, after drug cessation; this is widespread throughout memory CD8 + T cell subsets but limited to the granzyme-expressing cytotoxic subset within CD4 + memory T cells. Based on our observations, we suggest a model of immune dysregulation for understanding RA flare, with potential for further translational research towards novel avenues for its treatment and prevention. Immune-mediated inflammatory diseases such as rheumatoid arthritis (RA) are characterised by relapsing-remitting flares, which are difficult to study due to their unpredictable nature. Here the authors use an experimental model of immunomodulatory drug cessation in RA patients combined with multi-omic analysis of circulating leukocytes to characterise the immune response for those with arthritis flare versus drug-free remission.

The past three decades have witnessed remarkable advances in our ability to target specific elements of the immune and inflammatory response, fuelled by advances in both biotechnology and disease knowledge. As well as providing superior treatments for immune-mediated inflammatory diseases (IMIDs), such therapies also offer unrivalled opportunities to study the underlying immunopathological basis of these conditions.In this review, we explore recent approaches to the treatment of IMIDs and the insights to pathobiology that they provide. We review novel biologic agents targeting the T-helper 17 axis, including therapies directed towards interleukin (IL)-17 (secukinumab, ixekizumab, bimekizumab), IL-17R (brodalumab), IL-12/23p40 (ustekinumab, briakinumab) and IL-23p19 (guselkumab, tildrakizumab, brazikumab, risankizumab, mirikizumab). We also present an overview of biologics active against type I and II interferons, including sifalumumab, rontalizumab, anifrolumab and fontolizumab. Emerging strategies to interfere with cellular adhesion processes involved in lymphocyte recruitment are discussed, including both integrin blockade (natalizumab, vedolizumab, etrolizumab) and sphingosine-1-phosphate receptor inhibition (fingolimod, ozanimod). We summarise the development and recent application of Janus kinase (JAK) inhibitors in the treatment of IMIDs, including first-generation pan-JAK inhibitors (tofacitinib, baricitinib, ruxolitinib, peficitinib) and second-generation selective JAK inhibitors (decernotinib, filgotinib, upadacitinib). New biologics targeting B-cells (including ocrelizumab, veltuzumab, tabalumab and atacicept) and the development of novel strategies for regulatory T-cell modulation (including low-dose IL-2 therapy and Tregitopes) are also discussed. Finally, we explore recent biotechnological advances such as the development of bispecific antibodies (ABT-122, COVA322), and their application to the treatment of IMIDs.

Modern treat-to-target management of rheumatoid arthritis (RA) involves titration of drug therapy to achieve remission, requiring close monitoring of disease activity through frequent clinical assessments. Accelerometry offers a novel method for continuous remote monitoring of RA activity by capturing fluctuations in mobility, sedentary behaviours, physical activity and sleep patterns over prolonged periods without the expense, inconvenience and environmental impact of extra hospital visits. We aimed to (a) assess the feasibility, usability and acceptability of wearable devices in patients with active RA; (b) investigate the multivariate relationships within the dataset; and (c) explore the robustness of accelerometry outcomes to downsampling to facilitate future prolonged monitoring. Eleven people with active RA newly starting an arthritis drug completed clinical assessments at 4-week intervals for 12 weeks. Participants wore an Axivity AX6 wrist device (sampling frequency 100 Hz) for 7 days after each clinical assessment. Measures of macro gait (volume, pattern and variability), micro gait (pace, rhythm, variability, asymmetry and postural control of walking), sedentary behaviour (standing, sitting and lying) and physical activity (moderate to vigorous physical activity [MVPA], sustained inactive bouts [SIBs]) and sleep outcomes (sleep duration, wake up after sleep onset, number of awakenings) were recorded. Feasibility, usability and acceptability of wearable devices were assessed using Rabinovich’s questionnaire, principal component (PC) analysis was used to investigate the multivariate relationships within the dataset, and Bland–Altman plots (bias and Limits of Agreement) and Intraclass Correlation Coefficient (ICC) were used to test the robustness of outcomes sampled at 100 Hz versus downsampled at 50 Hz and 25 Hz. Wearable devices obtained high feasibility, usability and acceptability scores among participants. Macro gait outcomes and MVPA (first PC) and micro gait outcomes and number of SIBs (second PC) exhibited the strongest loadings, with these first two PCs accounting for 40% of the variance of the dataset. Furthermore, these device metrics were robust to downsampling, showing good to excellent agreements (ICC ≥ 0.75). We identified two main domains of mobility, physical activity and sleep outcomes of people with RA: micro gait outcomes plus MVPA and micro gait outcomes plus number of SIBs. Combined with the high usability and acceptability of wearable devices and the robustness of outcomes to downsampling, our real-world data supports the feasibility of accelerometry for prolonged remote monitoring of RA disease activity.

Post-COVID fatigue (pCF) represents a significant burden for many individuals following SARS-CoV-2 infection. The unpredictable nature of fatigue fluctuations impairs daily functioning and quality of life, creating challenges for effective symptom management. This study investigated the feasibility of developing predictive models to forecast next-day fatigue levels in individuals with pCF, utilizing objective physiological and behavioral features derived from wearable device data. We analyzed data from 68 participants with pCF who wore an Axivity AX6 device on their non-dominant wrist and a VitalPatch electrocardiogram (ECG) sensor on their chest for up to 21 days while completing fatigue questionnaires every other day. HRV features were extracted from the VitalPatch single-lead ECG signal using the NeuroKit Python package, while activity and sleep features were derived from the Axivity wrist-worn device using the GGIR package. Using a 5-fold cross-validation approach, we trained and evaluated the performances of two machine learning models to predict next-day fatigue levels using Visual Analogue Scale (VAS) fatigue scores: Random Forest and XGBoost. Using five-fold cross-validation, XGBoost outperformed Random Forest in predicting next-day fatigue levels (mean R² = 0.79 ± 0.04 vs. 0.69 ± 0.02; MAE = 3.18 ± 0.63 vs. 6.14 ± 0.96). Predicted and observed fatigue scores were strongly correlated for both models (XGBoost: r = 0.89 ± 0.02; Random Forest: r = 0.86 ± 0.01). Key predictors included heart rate variability features-sample entropy, low-frequency power, and approximate entropy-along with demographic (age, sex) and activity-related (moderate and vigorous duration) factors. These findings underscore the importance of integrating physiological, demographic, and activity data for accurate fatigue prediction. This study demonstrates the feasibility of combining heart rate variability with activity and sleep features to predict next-day fatigue levels in individuals with pCF. Integrating physiological and behavioral data show promising predictive accuracy and provides insights that could inform future personalized fatigue management strategies.

This systematic review explores the relationship between digital biomarkers, measured using wearable devices, and fatigue in patients with chronic diseases. Studies included in this review focused on individuals with diseases or conditions in 13 broad categories: multiple sclerosis (MS); rheumatoid arthritis (RA); chronic obstructive pulmonary disease (COPD); long COVID; cancer; chronic fatigue syndrome (CFS); pulmonary sarcoidosis; Parkinson’s disease; chronic stroke; chronic inflammatory rheumatic disease (CIRD); Inflammatory Bowel Diseases (IBD), Primary Sjogren’s Syndrome (PSS), and Systemic Lupus Erythematosus (SLE). The review synthesizes findings on the correlation between objective digital biomarkers and self-reported fatigue, highlighting the potential for disease-specific digital biomarkers to inform personalized fatigue management. The results suggest that reduced physical activity, increased sedentary behavior and autonomic dysfunction are associated with fatigue levels across multiple disease conditions included in this review, though the strength of this association and the specific biomarkers involved vary across diseases.

ObjectivesHuge advances in rheumatoid arthritis (RA) treatment mean an increasing number of patients now achieve disease remission. However, long-term treatments can carry side effects and associated financial costs. In addition, some patients still experience painful and debilitating disease flares, the mechanisms of which are poorly understood. High rates of flare and a lack of effective prediction tools can limit attempts at treatment withdrawal. The BIOlogical Factors that Limit sustAined Remission in rhEumatoid arthritis (BIO-FLARE) experimental medicine study was designed to study flare and remission immunobiology. Here, we present the clinical outcomes and predictors of drug-free remission and flare, and develop a prediction model to estimate flare risk.Design, setting and participantsBIO-FLARE was a multicentre, prospective, single-arm, open-label experimental medicine study conducted across seven National Health Service Trusts in the UK. Participants had established RA in clinical remission (disease activity score in 28 joints with C reactive protein (DAS28-CRP)<2.4) and were receiving methotrexate, sulfasalazine or hydroxychloroquine (monotherapy or combination).InterventionsThe intervention was disease-modifying anti-rheumatic drug cessation, followed by observation for 24 weeks or until flare, with clinical and immune monitoring.Outcome measuresThe primary outcome measure was the proportion of participants experiencing a confirmed flare, defined as DAS28-CRP≥3.2 or DAS28-CRP≥2.4 twice within 2 weeks, and time to flare. Exploratory predictive modelling was also performed using multivariable Cox regression to understand risk factors for flare.Results121 participants were recruited between September 2018 and December 2020. Flare rate by week 24 was 52.3% (95% CI 43.0 to 61.7), with a median (IQR) time to flare of 63 (41–96) days. Female sex, baseline methotrexate use, anti-citrullinated peptide antibody level and rheumatoid factor level were associated with flare. An exploratory prediction model incorporating these variables allowed estimation of flare risk, with acceptable classification (C index 0.709) and good calibration performance.ConclusionThe rate of flare was approximately 50%. Several baseline clinical parameters were associated with flare. The BIO-FLARE study design provides a robust experimental medicine model for studying flare and remission immunobiology.Trial registration numberISRCTN registry 16371380

Background Coronavirus disease 2019 (COVID‐19) is associated with excess mortality after hospital discharge. Identification of patients at increased risk of death following hospital discharge is needed to guide clinical monitoring and early intervention. Herein, we aimed to identify predictors of early vs. late mortality in COVID‐19 patients. Methods A total of 471 patients with polymerase chain reaction‐confirmed COVID‐19 were followed up for 9 months [median (inter‐quartile range) of follow‐up time: 271 (14) days] after hospital admission. COVID‐19‐related signs and symptoms, laboratory features, co‐morbidities, Coronavirus Clinical Characterisation Consortium (4C) mortality and Clinical Frailty Scale (CFS) scores were analysed by logistic regression for association with early (28 day) vs. late mortality. Receiver operating characteristic (ROC) analysis was used to determine the discriminative value of 4C and CFS scores for early vs. late mortality. Results A total of 120 patients died within 28 days from hospital admission. Of the remaining 351 patients, 41 died within the next 8 months. Respiratory failure, systemic inflammation, and renal impairment were associated with early mortality, while active cancer and dementia were associated with late mortality, after adjustment for age and sex. 4C mortality score and CFS were associated with both early [odds ratio (OR) (95% confidence interval—CI): 4C: 1.34 (1.25–1.45); CFS: 1.49 (1.33–1.66)] and late [OR (95% CI): 4C: 1.23 (1.12–1.36); CFS: 2.04 (1.62–2.56)] mortality. After adjustment for CFS, the association between 4C and late mortality was lost. By ROC analysis, 4C mortality score was superior to CFS for 28 day mortality [area under the curve (AUC) (95% CI): 0.779 (0.732–0.825) vs. 0.723 (0.673–0.773), respectively; P = 0.039]. In contrast, CFS had higher predictive value for late mortality compared with 4C mortality score [AUC (95% CI): 0.830 (0.776–0.883) vs. 0.724 (0.650–0.798), respectively; P = 0.007]. Conclusions In our cohort, late mortality in COVID‐19 patients is more strongly associated with premorbid clinical frailty than with severity of the acute infection phase.

Since March 10, 2020, the Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust has been screening symptomatic health-care workers for severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2). An initial symptom screen is done, and staff with compatible symptoms (ie, new continuous cough or fever) are appointed to testing in a designated screening pod, staffed by trained nurses, within 24 h. Combined nose and throat swabs are taken for SARS-CoV-2 RT-PCR (RdRp assay; Public Health England), and written advice about self-isolation is provided. [...]we acknowledge possible insensitivity of the SARS-CoV-2 RdRp assay,3 which might provide unwarranted reassurance in some cases.

Background Withdrawal of disease-modifying anti-rheumatic drugs (DMARDs) once disease remission is achieved is endorsed by current international rheumatoid arthritis (RA) management guidelines. However, very little data exists concerning patients’ views of this practice. In this qualitative study, we aimed to explore patients’ perspectives on DMARD withdrawal in the setting of established RA. Methods In this qualitative interview study, patients with stable established RA were recruited from rheumatology outpatient clinics at a large UK teaching hospital. The perceived advantages and disadvantages of DMARDs and views on DMARD withdrawal were explored in semi-structured interviews. Interview transcripts were analysed using standard qualitative techniques to construct an analytical framework. Results Thirteen participants (8 female, median [IQR] age 65 [61–73]) expressed their views of DMARD treatment in the context of their “normal lives”. For some patients, disadvantages such as medication side-effects and the inconvenience of safety monitoring were sufficient hindrances to their lifestyle to justify DMARD withdrawal. However, patients who were vulnerable to loss of physical function, or who had prior experience of severe rheumatoid arthritis, expressed a strong preference against DMARD withdrawal, viewing the potential for increased pain and future disability as unacceptable risks. Conclusions Patients view DMARD withdrawal in the context of either restoring or threatening their “normal lives”. In this model, social and personal factors play a crucial role in influencing patients’ opinions of DMARD therapy beyond a simple consideration of medication side-effects alone. A formulaic approach to DMARD withdrawal determined and imposed by clinicians would not be successful. Instead, the discussion of DMARD withdrawal should take place with the identification of patients’ priorities and in the context of their personal disease experiences. Trial registration clinicaltrials.gov (NCT02064400), retrospectively registered 17 February 2014.