While short-acting beta-agonists have been a mainstay of therapy during acute asthma exacerbations and for patients with intermittent asthma, there is increasing evidence that the use of combination inhalers, containing both an inhaled corticosteroid and a fast-acting bronchodilator, lead to improvement in symptom control, reduction in severe asthma exacerbations, and increased adherence to medications. These combination inhalers may be used in the form of stand-alone anti-inflammatory reliever therapy or as single maintenance and reliever therapy.

Understanding asthma phenotypes, including asthma severity and presence of type 2 (T2) inflammation, can help guide a personalized approach to treatment. Clinically accessible biomarkers, such as blood eosinophils, fractional exhaled nitric oxide, and immunoglobulin E, can help to differentiate between T2-high and non-T2 asthma phenotypes. For mild-to-moderate asthma, inhaled corticosteroids are the mainstay with best response in T2-high asthma. The addition of a long-acting β2-agnoist, however, is the best step-up option for most patients regardless of phenotype. Anti-inflammatory and single maintenance and reliever therapies are also emerging treatment options. For severe asthma, T2 status can guide biological treatment.

Chronic mountain sickness (CMS) is a clinical syndrome affecting more than 4 million individuals living at high altitudes, primarily characterised by excessive polycythaemia. Current CMS consensus recommends therapies targeting haematocrit (HCT), such as phlebotomy or isovolemic haemodilution. However, the clinical benefit of HCT-reduction strategies in CMS has yet to be established. This trial aims to evaluate whether erythrocytapheresis, regarded as one of the most precise and effective approaches for HCT-reduction, could improve clinical outcomes in patients with CMS.

Efficacy and safety of erythrocytapheresis in chronic mountain sickness is an investigator-initiated, multicentre, randomised controlled superiority trial with blinded outcome assessment. A total of 112 eligible CMS patients will be consecutively randomised to receive either standard care alone or standard care plus erythrocytapheresis with a 1:1 ratio. The primary efficacy endpoint is the net change in CMS symptom score assessed at 48 hours post-erythrocytapheresis or prior to discharge for controls. The primary safety outcome is treatment-related adverse events before discharge. The primary outcome will be analysed using an analysis of covariance, with a clinical threshold for superiority.

Ethics approval was obtained from the Medical Ethics Committees of the coordinating centre (Xinqiao Hospital, 2020-014-02) and each participating centre. Written informed consent will be acquired prior to enrolment. The trial results will be submitted for publication in a peer-reviewed journal.

NCT04557995.

Heart failure (HF) is an end stage of cardiovascular diseases. Tongmai Qiangshen Medicinal Liquor (TMQSJ) is an over-the-counter traditional Chinese medicinal liquor, but its efficacy-related material basis and mechanisms in HF remain unclear. In this study, UPLC-Q-TOF-MS/MS was used to characterize TMQSJ constituents and prototype compounds in rat plasma. HPLC fingerprinting, spectrum-effect relationship analysis, single-compound activity evaluation, and integrative bioinformatics were combined to screen efficacy-related candidate quality markers and HF-associated candidate genes. A total of 223 compounds were characterized, including 33 prototype plasma components. Chemometric analysis identified caffeic acid, Epimedin A, Epimedin B, Epimedin C, and icariin as characteristic constituents. Spectrum-effect analysis based on 10 batches of TMQSJ and validation in DOX-injured AC16 cells highlighted Epimedin A, Epimedin B, and Epimedin C. Among them, Epimedin A and Epimedin C showed prototype plasma exposure and were prioritized as efficacy-related candidate quality markers. Integration of predicted targets of prototype plasma components with HF transcriptomic data and machine-learning analysis identified BCL2L1, STAT3, HIF1A, MAPK8, and ESR1 as five candidate core genes. Immune cell estimation and molecular docking provided computational clues. Overall, this study establishes an integrated framework linking chemical quality, pharmacological association, in vivo exposure, and mechanistic prediction for TMQSJ quality evaluation.

This case presents a female in her 20s with biopsy-confirmed cutaneous leukocytoclastic vasculitis (LCV) and significant gastrointestinal (GI) symptoms, manifesting as recurrent rectal bleeding, abdominal pain and maculopapular rash. The patient's symptoms emerged shortly after treatment with phenoxymethylpenicillin for tonsillitis. Investigations revealed left-sided colitis with distinctive endoscopic findings. Skin biopsy confirmed cutaneous LCV; however, colonic biopsies demonstrated non-specific inflammation without histological evidence of vasculitis. Therefore, GI vasculitic involvement could not be confirmed. Negative autoimmune serology excluded primary autoimmune disorders. Following treatment with prednisolone and mesalazine, the patient's symptoms resolved and she showed sustained improvement. While clinical improvement occurred after corticosteroid therapy, this response is non-specific and does not confirm GI vasculitis. Given the absence of vasculitic features on GI histology, the relationship between cutaneous LCV and colitis in this case remains associative rather than proven. This case underscores the importance of diagnostic caution when extracutaneous manifestations are suspected in LCV.

Accurate assessment of aortic stenosis (AS) requires integration of both structural and functional information characterized by visual traits as well as quantitation of gradients. Existing artificial intelligence models use solely either structural or functional information.

The authors aimed to develop and validate an automated multiview deep learning framework to assess AS severity.

The authors developed EchoNet-AS, an open-source end-to-end integrated approach combining video-based convolutional neural networks to assess valve motion with segmentation models to automate measurement of aortic valve peak velocity and classify AS severity.

EchoNet-AS was trained on 210,193 images from 16,076 studies from KPNC (Kaiser Permanente Northern California) and validated on 1,588 held-out test studies and a temporally distinct cohort of 19,202 studies. The final model was also externally validated on 2,415 studies from SHC (Stanford Healthcare) and 9,038 studies from CSMC (Cedars-Sinai Medical Center). Combining assessments from multiple echocardiographic videos and Doppler measurements, EchoNet-AS achieved excellent discrimination of severe AS with an area under the receiver-operating characteristic curve of 0.964 (95% CI: 0.952-0.973) in the KPNC held-out cohort and 0.986 (0.983-0.989) in the temporally distinct cohort, which was superior to models using single views or only Doppler measurements. The performance was consistently robust in distinct external cohorts with an area under the receiver-operating characteristic curve of 0.985 (0.975-0.992) at SHC and 0.989 (0.986-0.992) at CSMC.

EchoNet-AS synthesizes information from both B-mode videos and Doppler images to accurately assess AS severity. Its strong performance generalizes robustly to external validation cohorts and shows potential as an automated clinical decision support tool.

Out-of-hospital cardiac arrest (OHCA) in children is a rare but devastating event, with an estimated incidence of 9 per 100 000 person-years and a survival rate of less than 10%. OHCA may be the presenting event in children with undiagnosed cardiovascular disease. A substantial proportion of adults experiencing an unexpected OHCA report previous symptoms, but whether the same is true in paediatric survivors has not been systematically evaluated previously. This study aims to describe cardiac symptoms in children prior to presenting with OHCA.

Clinical characteristics and preceding symptoms of 58 consecutive patients aged 0-18 without a pre-existing causative diagnosis presenting with a survived OHCA between 1996 and 2025 to a single UK paediatric tertiary referral centre were reviewed.

The median age at cardiac arrest was 11 years (IQR 7-14 years). 29 OHCA (50%) occurred on exertion. Following the OHCA, 42 patients (72.4%) were diagnosed with an inherited cardiac condition, most commonly hypertrophic cardiomyopathy (n=17, 29.8%). 18 patients (31.0%) had a family history of sudden cardiac death.28 patients (50.9%) had reported cardiac symptoms prior to their OHCA, including 13 (23.6%) who had experienced syncope. Of the 13 patients who experienced syncope, only three sought medical attention and none had follow-up arranged.

Almost half of paediatric OHCA survivors had warning symptoms prior to their event, representing potential missed opportunities to identify an underlying diagnosis prior to OHCA. These findings highlight the need for more education, both for medical professionals and the general public, about 'red flag' symptoms.

The relationship between pericardial inflammation and subsequent constriction in tuberculous (TB) pericarditis remains poorly understood. Late gadolinium enhancement (LGE) has shown conflicting prognostic associations, suggesting that inflammation alone may not adequately reflect disease stage or reversibility. We hypothesised that inflammatory burden and oedema provide complementary information regarding subsequent pericardial tethering, and that segments without oedema would be more likely to demonstrate tethering at follow-up than oedematous segments.

Participants with TB pericarditis underwent baseline and 6-month cardiovascular magnetic resonance (CMR) imaging. Baseline inflammatory burden was quantified using pericardial LGE at 6 SD and 10 SD thresholds. Baseline oedema and 6-month visceral-parietal pericardial tethering were assessed segmentally using T2-weighted short tau inversion recovery imaging and radiofrequency tissue tagging, respectively, within a validated 20-segment pericardial model. Generalised estimating equation modelling was used to evaluate associations between baseline inflammatory burden, oedema and follow-up tethering.

Tethering at 6-month follow-up was present in 131/633 oedematous segments (20.7%) compared with 138/267 non-oedematous segments (51.7%; p<0.001). Greater baseline inflammatory burden was independently associated with subsequent tethering, while baseline oedema remained independently protective. In the 6 SD model, baseline oedema was independently associated with lower odds of subsequent tethering (OR 0.29, 95% CI 0.20 to 0.43; p<0.001), while greater baseline inflammatory burden was independently associated with higher odds of subsequent tethering (OR 1.32 per SD increase, 95% CI 1.12 to 1.55; p=0.0011). Similar findings were observed using 10 SD thresholds.

Pericardial inflammatory burden and oedema provide independent and complementary information regarding subsequent tethering in active TB pericarditis. Oedema may reflect a more acute, potentially reversible inflammatory phenotype, whereas intense inflammatory signal without oedema appears associated with a more fibrosis-prone state. These findings support multiparametric CMR for disease staging and provide longitudinal imaging evidence consistent with differing pathways of pericardial remodelling.

The natural history of symptomatic severe aortic stenosis (AS) heralds a poor prognosis-a paradigm that rightly drives valve intervention. A widely voiced corollary holds that once patients exceed their nation's average life expectancy, aortic valve replacement offers no prognostic benefit. We argue that this corollary is statistically incoherent and potentially clinically harmful. Life expectancy at birth is a period average across a whole cohort, weighted by deaths at younger ages; it is not a ceiling on individual survival. Expectancy is conditional on survival already achieved: the longer a person has lived, the older they are expected to be at death. A man of 90 in England and Wales still has a mean of 3.9 years ahead of him, and an average 82-year-old will outlive an average 72-year-old. The patient who has 'beaten the average' has, by surviving, demonstrated membership of a longer-lived stratum-precisely the group with most to gain from a durable valve. The hazard in AS is cardiac, not chronological: it tracks extravalvular cardiac damage, frailty and competing illness rather than the calendar, and stage, not age, discriminates outcome after valve replacement. Futility is real-advanced cardiac damage, established frailty and prognosis-dominating comorbidity do foreclose benefit-but age is an inaccurate proxy for it. Drawing on natural history and clinical trial data, including cardiac damage staging, national life tables and the deprivation gradient, we propose replacing age-based nihilism with conditional, comorbidity-adjusted prognostication, with the expected gain estimated, documented and stated explicitly as part of consent and shared decision-making.

Atrial fibrillation (AF) is a common cause of ischaemic stroke and a major global health burden, but is often undetected in routine clinical practice. Detection of AF is essential for secondary stroke prevention, but it remains unclear which patients who had a stroke are at highest risk of previously unrecognised or newly detected AF, limiting targeted detection strategies. The aim of this study was to determine the prevalence of AF and risk factors that are associated with AF among patients who had an ischaemic stroke.

In this retrospective cohort study, all patients who had an ischaemic stroke at the Radboud University Medical Center (Nijmegen, the Netherlands) from January 2019 to October 2024 were included. We applied descriptive statistics, logistic regression analyses and generative topographic mapping (GTM), a probabilistic machine learning method for clustering and data visualisation.

Of 1836 patients (median age 73 years (IQR 64-80), 44.1% female), 30.5% had AF, of which 19.7% were previously known and 10.8% were newly detected after stroke. The most significant factors associated with AF were older age, increased stroke severity, history of ischaemic stroke and heart failure, mitral regurgitation, increased left atrial volume and elevated peak E wave velocity. GTM identified a phenotype characterised by older age, severe strokes, high prevalence of history of ischaemic stroke, hypertension, diabetes, high cardiac burden with history of myocardial infarction, heart valve abnormalities and heart failure, impaired kidney function, high premature atrial and ventricular contraction burden, and signs of severe left atrial remodelling to be most associated with AF (prevalence 47.9%).

The prevalence of AF and stroke severity among AF patients in this study highlights the relevance of optimising monitoring strategies. Stroke severity, history of ischaemic stroke, high cardiac burden and left atrial remodelling signs were most strongly associated with AF. These findings suggest potential areas for targeted screening in high-risk clinical phenotypes.