We examined the association of COVID time periods and equity-related variables with pharmacotherapy in a large jurisdiction fracture liaison service (FLS). We did not observe a significant association between COVID time periods and medication prescription after adjusting for all covariates. This highlights another potential success of the FLS model.

Our objective was to examine the impact of COVID on bone-active medication prescription in a fracture liaison service (FLS), after adjusting for fracture risk status and equity-related variables.

We conducted a logistic regression analysis with medication prescription (prescription by a bone health specialist or primary care provider) as the outcome. The model included covariates COVID time periods (T1: "pre-COVID" (n = 2796); T2: "during COVID" (n = 1575); T3: "COVID recovery" (n = 2208)), fracture risk status (high risk/not high risk) and equity-related variables (sex, age, marital status, living arrangement, education status, geographic location, and presence of comorbidities). Goodness of fit was assessed with the area under the receiver operating characteristic curve (AUC) and the Hosmer and Lemeshow test.

Fracture risk status was the primary driver of treatment with high-risk patients 7.8 times more likely to receive a medication prescription compared to patients who were not high risk, after adjusting for all covariates (OR = 7.80 [95% CI 6.91, 8.79]). COVID time period was not statistically significant. Female patients, those married or in a common-law relationship, living alone, or residing in urban areas were more likely to be prescribed medication. The model had good prediction power and fit the data well (AUC: 0.77; Hosmer-Lemeshow test p-value: 0.83).

Among patients reached by the FLS, COVID time period was not significantly associated with medication prescription, although program reach decreased at T2 and T3. Fracture risk status, sex, marital status, living arrangement, and geographic location were significantly associated with medication prescription.

Eosinophils amplify type-2 (Th2) inflammation and tissue injury in allergic rhinitis (AR), and eosinophilic burden correlates with disease severity and future asthma risk. Current AR therapies have limitations, motivating interest in non-pharmacologic neuromodulatory approaches. Here, we tested whether electroacupuncture (EA) attenuates eosinophilic inflammation in AR and probed a candidate neuroimmune mechanism.

Using an ovalbumin (OVA)-induced AR mouse model, we compared EA with the antihistamine chlorpheniramine (CLP). We assessed nasal behaviors, inflammatory biomarkers, and histological changes. Mechanistic exploration involved administering β₂-adrenergic (butoxamine) or dopamine D1 (butaclamol) antagonists before EA, followed by plasma catecholamine measurements and intranasal epinephrine rescue.

In OVA-challenged mice, EA significantly alleviated nasal rubbing, redness, and olfactory dysfunction, showing comparable efficacy to CLP. While OVA induction increased IL-5, IL-13, and serum OVA-specific IgE, both treatments significantly reduced these markers. Crucially, only EA reversed OVA-induced nasal eosinophil infiltration and suppressed RNASE2A expression; CLP primarily suppressed mast cell degranulation and MCPT1 expression. Mechanistically, pre-treatment with butoxamine-but not butaclamol-abolished the EA-mediated reduction of OVA-induced IL-5, IL-13, RNASE2A, and CCR4. Furthermore, EA was associated with elevated plasma norepinephrine and epinephrine levels. While butoxamine blocked EA-induced symptom relief and eosinophil reduction, intranasal epinephrine mimicked EA's beneficial effects on these parameters.

Our findings demonstrate that EA reduces eosinophilic inflammation and AR behaviors associated with the activation of β₂-adrenergic receptors. Unlike standard antihistamines that target mast cells, EA engages a sympathetic neuroimmune axis, representing a promising complementary intervention for eosinophil-driven AR.

Pregnancy induces significant immunological adaptation, including shifts in the balance between B effector and B regulatory cells. However, the impact of SARS-CoV-2 infection or vaccination on B cell populations during pregnancy remains largely unexplored.

Blood samples were collected from 139 women prior delivery and grouped according to the questionnaire responses and serology: controls (uninfected/unvaccinated); previously infected only; vaccinated only; both vaccinated and infected; and acutely SARS-CoV-2 infected (unvaccinated or vaccinated). Maternal serum cytokine levels were determined, and B cell populations were analyzed by flow cytometry following short- and long-term stimulation with CpG ± CD40L and PMA/ionomycin or.

Serum levels of APRIL, IL-4, IL-6, TNF-α and sCD40L varied according to SARS-CoV-2 vaccination and infection status. Vaccination against SARS-CoV-2, and to a lesser extent infection with the virus, altered the frequency of various B cell populations, including plasma blasts, plasma cells and B memory cells. Patients infected with the virus exhibited increased levels of IL-10+ B cells, and decreased levels of IL-6+ B cells, in comparison to vaccinated women. In addition, the expression of CD40 was induced in B cells in response to infection. Conversely, the expression of PD-1, FasL and CD86 was enhanced by vaccination.

SARS-CoV-2 infection and vaccination during pregnancy considerably shift the balance between pro- and anti-inflammatory B cell populations, and modified expression of costimulatory molecules. This highlights the need for further investigation into the long-term consequences of maternal SARS-CoV-2 immunity for both mothers and offspring.

Interstitial lung disease (ILD) comprises diverse chronic inflammatory and fibrotic disorders with poorly understood mechanisms and limited diagnostic biomarkers. Growing evidence implicates lipid metabolic reprogramming in ILD pathogenesis, yet integrated metabolomic-transcriptomic analyses remain scarce. We performed combined metabolomic and transcriptomic analysis using publicly available datasets. Plasma metabolite profiles of ILD and lobar pneumonia (LOB) patients were analyzed by NMR-based metabolomics. Multivariate analyses (PCA, PLS-DA, OPLS-DA) identified discriminatory metabolites. KEGG enrichment revealed associated pathways. Transcriptomic data from lung tissue were analyzed for differentially expressed genes, identifying 345 differentially expressed genes (|log2FC| > 0.585, adjusted p < 0.05), integrated with metabolomic data to identify shared pathways. ROC curves evaluated diagnostic performance of key metabolites. To validate the bioinformatic findings, we established a bleomycin (BLM)-induced ILD mouse model with or without high-cholesterol diet (HCD) intervention. RT-qPCR, Western blotting, H&E staining, and Masson's trichrome staining were performed to assess gene expression and histopathological changes in lung tissue. Metabolomic profiling showed clear separation between ILD and LOB samples, driven by alterations in triglyceride-rich lipoproteins, phospholipids, and cholesterol fractions. Seven metabolites were significantly increased in ILD (p < 0.05). Integrated multi-omics identified "lipid and atherosclerosis" as a key shared pathway, encompassing six differential genes (CD36, NFKBIA, PIK3R1, SELP, CCL2, VCAM1) and one differential metabolite (Cholesterol [HDL4]). ROC analysis showed a combined metabolite model achieved AUC of 0.810. Experimental validation confirmed SELP, CCL2, and VCAM1 were upregulated while NFKBIA was downregulated in BLM-treated mice. HCD further aggravated BLM-induced pulmonary fibrosis and markedly elevated the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β) as well as key pathway proteins (SELP, CCL2, VCAM1). This integrated multi-omics analysis reveals a strong link between lipid dysregulation and ILD pathogenesis. Cholesterol fractions, triglycerides, and phospholipids may serve as potential non-invasive biomarkers for ILD, while the lipid and atherosclerosis pathway represents a promising target for therapeutic intervention. Animal experiments further validated that HCD exacerbates ILD via the lipid and atherosclerosis pathway, reinforcing the clinical relevance of cholesterol dysregulation in ILD progression. Our findings provide new insights into the metabolic mechanisms of ILD and establish a foundation for future diagnostic and therapeutic development.

Umbrella protocols have recently come to be widely used in clinical trial designs. However, the value of this approach in public health is less well recognized. The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, reliable and scalable evaluation of diagnostic technologies. In the United Kingdom of Great Britain and Northern Ireland, this need prompted the development of an umbrella research protocol enabling multiple clinical evaluation studies of similar designs to be undertaken under a single overarching preapproved ethics and governance framework. We describe the development, implementation and evolution of this protocol, which was designed to support timely assessment of the performance of in vitro diagnostic devices and associated testing approaches in various settings. The umbrella protocol allowed studies to be started quickly during the pandemic, reduced administrative burdens, supported regulatory submissions and enabled prospective collection of samples for surveillance. While the system described reflects British governance structures, the principles underpinning this approach, including proportionality (ensuring oversight requirements are appropriate to the risk level), standardization and preapproved flexibility, are applicable to many settings. The protocol now forms part of the United Kingdom's wider pandemic preparedness structure and illustrates how preapproved, adaptable research frameworks can accelerate evidence generation during outbreaks. The world is now assessing lessons from the COVID-19 pandemic and it is timely to consider how research systems can support innovative designs such as umbrella protocols. We therefore summarize lessons learnt and practical considerations to support other countries seeking to adopt similar approaches within their own ethical and regulatory systems.

Glucose variability (GV) reflects fluctuations in blood glucose and may better capture metabolic instability than static glycemic measures in patients with Coronavirus disease 2019 (COVID-19). However, its association with mortality remains uncertain due to heterogeneous study designs and inconsistent findings. This meta-analysis was performed to evaluate the association between GV and short-term all-cause mortality in adult patients hospitalized with COVID-19.

PubMed, Embase, and Web of Science were systematically searched for longitudinal observational studies reporting the association between GV and mortality in patients hospitalized with COVID-19. Risk ratios (RRs) with 95% confidence intervals (CIs) were pooled using a random-effects model accounting for the possible influence of heterogeneity.

Ten cohort studies comprising 11 datasets and 77,395 patients were included, with 8,189 deaths. High GV was associated with a significantly increased risk of all-cause mortality (RR = 2.10, 95% CI: 1.69-2.59; I² = 45%). The association was stronger in studies with a mean age ≥ 62 years (RR = 2.79) compared with < 62 years (RR = 1.78; p for subgroup difference = 0.03). Results were consistent across GV metrics, analytic models, adjustment for diabetes status, and study quality (all p > 0.05). Meta-regression analysis showed that mean age demonstrated a borderline association with the effect estimate (coefficient = 0.020, p = 0.07), explaining a moderate proportion of heterogeneity (adjusted R² = 49.2%).

Higher GV is associated with increased short-term mortality in hospitalized patients with COVID-19, supporting its role as a potential prognostic marker.

https://www.crd.york.ac.uk/prospero/, identifier CRD420261358246.

Undernutrition is a major driver of common infectious morbidity among children under five; however, the relationship between different forms of undernutrition and childhood infectious morbidity remains poorly understood. This study examined variations in the association between different forms of undernutrition measured according to the Composite Index of Anthropometric Failure (CIAF) and common infectious morbidity among children under the age of five in sub-Saharan Africa (SSA).

We performed a multilevel binary logistic regression analysis using country and community clusters as random effects. Our study utilised demographic and health survey (DHS) data collected between 2016 and 2024 in 27 SSA countries. A total weighted sample of 157, 800 under-five children whose nutritional status was assessed based on the World Health Organization (WHO) anthropometric techniques and data on Acute Respiratory tract Infection (ARI) and diarrhea recorded were included. An adjusted odds ratio (AOR) with a 95% Confidence Interval (CI) was reported, and variables' effects with a p-value less than 0.05 were declared significant determinants of common infectious morbidity.

The prevalence of common infectious morbidity among children under five in SSA was 30.20% (95% CI: 27.34, 33.06). The lowest and highest prevalences were reported in Mozambique (16.96%; 95% CI: 16.94, 16.98) and Uganda (53.26%; 95% CI: 53.24, 53.28), respectively. The odds of infectious morbidity significantly differs between children with standalone, double and triple forms of undernutrition. Children with double (AOR: 1.25; 95% CI: 1.16, 1.34 for stunting-underweight; AOR: 1.36; 95% CI: 1.22, 1.51 for wasting-underweight) and triple undernutrition (AOR: 1.51; 95% CI: 1.36, 1.68) were more susceptible to common infectious morbidity.

Children with coexisting undernutrition were more likely to experience common infectious morbidity, and those affected by the coexistence of stunting-wasting-underweight experienced the highest odds of infectious morbidity. Among the standalone forms, only underweight children were more likely to experience common infectious morbidity. Therefore, to mitigate the burden of childhood infectious morbidity, it is crucial for policymakers to implement targeted nutritional interventions for children experiencing coexisting undernutrition.

Inflammatory rheumatic diseases (IRDs) represent a significant risk factor for cerebrovascular events, independent of traditional cardiovascular risk factors. The elevated risk of stroke in individuals with IRDs arises from chronic systemic inflammation, endothelial dysfunction, accelerated atherosclerosis, prothrombotic effects of antiphospholipid antibodies, and cumulative corticosteroid exposure. Post-stroke rehabilitation in patients with IRDs involves distinct clinical challenges that extend beyond standard neurorehabilitation, including joint involvement, chronic pain, fatigue, and treatment-related limitations. Assessment tools that focus exclusively on neurological recovery are inadequate for this population. Dual-layered protocols that integrate both neurological and rheumatological criteria are required. Although wearable sensors, telemonitoring, and electronic patient-reported outcome measures provide a foundation for continuous monitoring, barriers such as limited digital literacy, insufficient infrastructure, and the absence of IRD-specific content addressing symptom overlap persist. Post-stroke cognitive impairment is frequent and multifactorial in this group, necessitating cognitive assessment approaches tailored to IRD subgroups, as disease-specific manifestations may confound the interpretation of standard screening tools. Physiotherapy, occupational therapy, telerehabilitation, and virtual reality-based interventions may be beneficial when specifically adapted to accommodate joint limitations and pain. While artificial intelligence-based tools hold promise, there remains a need for clinically validated, domain-specific expert platforms. Nurses are integral to this process, providing patient education, monitoring medication adherence, offering psychosocial support, and coordinating remote care. Optimal management of stroke associated with IRDs requires multidisciplinary, technology-enabled, and patient-centered care models that integrate neurology, rheumatology, rehabilitation medicine, and digital health. Given the predominantly indirect nature of current evidence, prospective studies employing validated assessment tools are urgently needed.

Dizziness and vertigo are common emergency department (ED) presentations, but only 2%-5% receive a serious diagnosis, such as stroke or transient ischemic attack (TIA). Due to the lack of reliable validated prediction tools, many undergo unnecessary imaging and consultations, highlighting the need for improved risk stratification.

To develop machine learning (ML) models that predict serious diagnoses in ED patients presenting with dizziness or vertigo.

This multicenter cohort study included 6637 ED patients with dizziness, vertigo, or imbalance from September 2014-December 2022. The primary outcome was a serious diagnosis-stroke, TIA, vertebral artery dissection, or brain tumor-within 30 days, adjudicated by a blinded committee. Data were split 80/20 into training and test sets. Four ML models (decision tree, LASSO logistic regression, random forest, XGBoost) were trained on 17 variables using 5-fold cross-validation and evaluated alongside the Sudbury Vertigo Risk score. Performance was assessed using area under the curve (AUC) and diagnostic accuracy measures. Computed tomography (CT) and referral rates were hypothetically compared pre- and post-model application.

Among 6637 patients (mean age 78.1; 57.8% female), 3.3% had a serious diagnosis. All ML models demonstrated strong discrimination, with AUCs ranging from 0.92 to 0.97. At a 5% predicted probability threshold, sensitivities ranged from 53%-97% and specificities from 84% to 96%. Logistic regression with LASSO demonstrated a favorable balance between discrimination (AUC: 0.97, sensitivity: 97% and specificity: 91%), although confidence intervals overlapped substantially across models. In a hypothetical model-based analysis, ML-guided classification corresponded to projected reductions in CT utilization and referrals ranging from 53%-85% and 11%-73%, respectively.

Select ML models demonstrated discrimination comparable to the Sudbury Vertigo Risk Score while potentially improving specificity and reducing projected resource utilization. These tools show promise, but external validation is needed.

Atrial fibrillation patients often experience issues such as recurrence and reduced exercise tolerance following radiofrequency catheter ablation, which adversely affect postoperative recovery and long-term prognosis. Although current guidelines recommend exercise-based rehabilitation for these patients, its implementation in clinical practice remains suboptimal.

This study aims to summarize the best evidence regarding post-ablation exercise rehabilitation for atrial fibrillation, establish structured quality evaluation metrics, and identify barriers in clinical practice, thereby informing standardized management of exercise rehabilitation.

This study included evidence synthesis, baseline audit, barrier analysis, and strategy development. Guided by the Joanna Briggs Institute (JBI) evidence-based healthcare model, formulated a clear evidence question and established an evidence-based team responsible for literature retrieval and quality assessment, established quality indicators and review methods, analyzed the barriers and facilitators factors, and formed evidence-based practice strategies.

A total of sixteen evidence points across four areas, including exercise timing, assessment, prescription, monitoring, and follow-up. Twenty quality indicators were subsequently developed. Clinical quality evaluation revealed several implementation barriers, such as insufficient localization, inadequate knowledge and skills among healthcare personnel, patient misconceptions, and an incomplete organizational support system. These represent multi-level obstacles.

A significant evidence-practice gap exists in post-ablation exercise rehabilitation. The effective translation of evidence into practice entails a thorough analysis of implementation barriers and the subsequent formulation of tailored, evidence-based strategies to facilitate this process.