BackgroundEndobronchial ultrasound-guided transbronchial needle core biopsy (EBUS-TBNB) is an emerging technique designed to obtain histological specimens from mediastinal lesions. This meta-analysis aims to compare the performance of EBUS-TBNB versus endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for mediastinal disorders.MethodsA thorough search of the literature was performed across PubMed, Embase and Web of Science databases, covering the period from their inception through 1 October 2025. Diagnostic yields were pooled via inverse variance weighting using fixed- or random-effects models based on heterogeneity. Complications were carefully assessed and categorised.Results11 studies involving 1434 patients were analysed. The pooled diagnostic yield was 88.51% (678/766) for EBUS-TBNB and 80.11% (741/925) for EBUS-TBNA, with an odds ratio of 2.04 (95% CI 1.52–2.74; p<0.00001) and I2=23%. Subgroup analysis showed significantly higher yield with EBUS-TBNB for lymphoma (OR 5.22, 95% CI 1.12–24.26; p=0.04) and sarcoidosis (OR 2.88, 95% CI 1.64–5.04; p=0.0002), while no significant difference was observed for lung cancer (OR 1.18, 95% CI 0.77–1.81; p=0.45). The safety profiles of EBUS-TBNB and EBUS-TBNA were favourable, with low complication rates and no procedure-related mortality.ConclusionCurrent pooled evidence suggests a possible diagnostic advantage of EBUS-TBNB over EBUS-TBNA, particularly for lymphoma and sarcoidosis, with a similar safety profile. However, this apparent benefit should be interpreted cautiously given the limited randomised evidence, heterogeneity across needle platforms and substantial contribution of observational studies.

BackgroundArtificial intelligence (AI) has advanced rapidly in adult sleep medicine, but its role in children under 2 years old remains unclear. This scoping review maps the current evidence on AI-enabled diagnostics in neonates, infants and toddlers, identifying challenges, opportunities and research priorities.MethodsA structured scoping review was undertaken up to April 2025, incorporating peer-reviewed studies, normative datasets, regulatory reports and evidence on consumer and commercial technologies. Studies focusing on children under 2 years were prioritised, with older cohorts reviewed where relevant to translation.FindingsNo AI tool is validated for independent diagnostic use in this age group. Research prototypes, including automated polysomnography scoring, convolutional neural networks for oximetry and multimodal wearable or radar-based systems, show feasibility but remain experimental. Commercial platforms such as SleepImage, Belun Sleep Health and WatchPAT exclude children under 2 years, while consumer monitors including Owlet, Nanit and Miku are widely used but lack clinical validation.ConclusionAI in sleep diagnostics for children under 2 years remains an emerging field with high potential but no current clinical readiness. Progress will depend on the development of infant-specific datasets, improved artefact handling, simplified monitoring strategies and human-in-the-loop workflows. Longer-term opportunities include prognostic modelling, circadian analysis and predictive closed-loop systems, but equity and regulatory clarity must guide translation to ensure safe and globally relevant impact.

Millions of people worldwide live in areas prone to earthquakes and face devastating consequences and severe medical issues. Earthquakes not only cause massive destruction but also release hazardous mixtures of dust, fibres, microbes and toxic particles from collapsed structures, posing serious risks to the respiratory system and other organs. Survivors, search and rescue workers and volunteers can be heavily exposed to earthquake-related hazards. The respiratory health impacts of earthquake-related exposures include both acute and long-term effects, as well as exacerbation of chronic pulmonary diseases. This narrative review focuses on the spectrum of environmental and occupational exposures that arise after earthquakes and their associated short- and long-term respiratory consequences. By incorporating post-earthquake particulate matter measurements of 2.5 and 10 µm diameter from the 2023 Türkiye–Syria earthquakes (also referred to as the Kahramanmaraş earthquakes), the review highlights exposure patterns that remain underrepresented in the existing literature. Furthermore, it places these findings within an earthquake-specific public health framework, aiming to inform risk reduction, preparedness and mitigation strategies relevant to affected communities.

IntroductionUndiagnosed exudative pleural effusions often require pleural biopsy for a definitive diagnosis. The optimal sampling method, particularly when medical thoracoscopy is compared with image-guided pleural biopsy, remains uncertain.MethodsThis systematic review and network meta-analysis included studies that reported on the diagnostic performance and safety of medical thoracoscopy and image-guided pleural biopsy approaches. The primary outcome of this study was diagnostic yield, and the secondary outcomes included other diagnostic parameters (e.g. sensitivity, negative likelihood ratio, and negative predictive value), post-procedure complications, and biopsy quality and number. A frequentist network meta-analysis was performed to compare and rank the diagnostic performance and safety of various pleural biopsy methods using a random-effects model. A subgroup analysis was performed according to the presence of pleural nodules or thickness and the type of biopsy needles used.ResultsAn analysis of 64 studies involving 8744 patients revealed that rigid medical thoracoscopy had the highest diagnostic yield (95.0%), followed by cryobiopsy (93.1%), semirigid medical thoracoscopy (92.3%), and ultrasound elastography (UE)-guided biopsy (92.3%). Closed pleural biopsy had the lowest yield (75.1%). The current evidence is insufficient to establish the superiority of any biopsy method in terms of complications. The network meta-analysis ranked rigid medical thoracoscopy as having the highest overall diagnostic yield, closely followed by UE-guided biopsy, with no statistically significant differences between image-guided biopsy and rigid medical thoracoscopy.ConclusionClosed pleural biopsy consistently has a low diagnostic yield and is not recommended as a first-line biopsy method for patients with undiagnosed exudative pleural effusions. Rigid medical thoracoscopy offers distinct clinical value for a complete visual inspection, large biopsies, and potential pleurodesis, particularly in suspected mesothelioma cases. The currently available evidence does not support the routine replacement of semirigid medical thoracoscopy with cryobiopsy. Computed tomography-guided biopsy may be preferred in cases involving thickening ≥10 mm or small target lesions. Conversely, ultrasound-guided biopsy may be appropriate for lesions measuring ≥3 mm with a clear acoustic window. UE-guided pleural biopsy shows promise as an emerging option, although the evidence is limited. In tuberculous pleurisy, medical thoracoscopy may offer higher diagnostic sensitivity in certain settings, particularly in high-prevalence areas. The current evidence is insufficient to establish a clear advantage for any biopsy method regarding the overall complication risk.

IntroductionDespite increasing trials examining palliative care for people with thoracic cancer, implementation remains limited. Understanding the core elements of palliative care interventions is critical to improving design, scalability, and accessibility globally. This review aimed to determine the core elements and efficacy of palliative care interventions in thoracic cancer.MethodsFive medical databases were searched from January 1987 to January 2025. Randomised controlled trials (RCTs) and nonrandomised studies of palliative care interventions in thoracic cancer addressing at least two National Consensus Project domains were eligible. Meta-analyses of RCTs were performed using a random-effects model.Results34 palliative care interventions (n=6490, mean±sd age 65±10 years, 41% women) were identified. Interventions were categorised as comprehensive palliative care interventions (n=18), nonpharmacological symptom interventions (n=12) and psychosocial-educational interventions (n=4). Comprehensive interventions were significantly longer (21.7 weeks) than nonpharmacological symptom (4.8 weeks) and psychosocial-educational interventions (8.7 weeks; p<0.01), addressed more components (mean components: 8, 3, 4, respectively; p<0.01), were often provided by specialist palliative care clinicians (83%, 16%, 0%; p<0.01), and included interprofessional teams (61%, 8%, 0%; p<0.01). Comprehensive palliative care interventions improved quality of life (standardised mean difference (SMD) 0.25, 95% CI 0.11–0.38), survival (hazard ratio 0.76, 95% CI 0.62–0.94), overall symptoms (SMD 0.20, 95% CI 0.02–0.38) and depression (SMD 0.28, 95% CI 0.07–0.5). Nonpharmacological symptom interventions improved breathlessness (SMD 0.29, 95% CI 0.15–0.43) and depression (SMD 0.17, 95% CI 0.02–0.31). Psychosocial-educational interventions did not affect quality of life or mood.ConclusionsComprehensive palliative care interventions improved quality of life, survival and symptoms among people with thoracic cancers. Nonpharmacological symptom interventions improved breathlessness and depression.

Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception – January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (e.g. stain normalisation) were common, but annotation and training practices were inconsistently reported. AI model performance was generally high (accuracy ≥90%; seven studies); however, validation metrics varied, and external validation was absent. Most studies used “black box” models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported.AI tools are poised to transform histological analysis in pre-clinical respiratory research. The field will be able to further harness AI to automate pre-clinical respiratory histological analysis by addressing gaps that we have identified in validation, transparency, and standardisation.

BackgroundCardiovascular disease is a primary driver of mortality in COPD. Elevated sympathetic nerve activity is a key proposed mechanism, but the magnitude of this autonomic overactivity has not been quantified in a pooled analysis.ObjectiveTo determine if adults with COPD exhibit higher resting muscle sympathetic nerve activity (MSNA) than healthy controls, assess associated cardiorespiratory effects, and evaluate the short-term impact of COPD interventions on MSNA.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, eight databases were searched from inception to 15 May 2025 to inform this systematic review and meta-analysis (PROSPERO CRD420251044931). Eligible studies included adults with spirometry-defined COPD in whom MSNA was measured by microneurography at rest. Primary outcomes were MSNA burst frequency (bursts·min−1) and incidence (bursts per 100 heartbeats). Secondary outcomes included resting heart rate and blood pressure.ResultsA total of 11 studies (171 COPD participants, 105 controls) met inclusion criteria. Compared with controls, COPD was associated with markedly higher MSNA, burst frequency (+18.5, 95% CI 9.4–27.7 bursts·min−1) and burst incidence (+21.3, 95% CI 9.2–33.4 bursts per 100 heartbeats). Resting heart rate was also elevated (+10.7, 95% CI 6.1–15.3 beats·min−1), while blood pressure did not differ significantly. Three studies on noninvasive ventilation or inhaled β2-agonists found no significant pooled changes in MSNA.ConclusionsResting MSNA is significantly elevated in patients with COPD despite normal blood pressure. This chronic sympathetic excitation likely contributes to COPD-related cardiovascular morbidity and exercise intolerance, highlighting the importance of developing therapies that reduce sympathetic nerve activity.

Paediatric asthma management has undergone a significant transformation from rudimentary assessments in the early 20th century to sophisticated diagnostic and therapeutic approaches today. Early clinical observations lacked paediatric specificity, but mid-20th-century studies introduced functional assessments, spirometry and recognition of asthma as a chronic inflammatory condition. The introduction of inhaled corticosteroids transformed long-term management, offering targeted control with reduced systemic risks. Advances in noninvasive diagnostics, such as fractional exhaled nitric oxide, induced sputum analysis, exhaled breath condensate and electronic nose technology, have improved inflammation monitoring, phenotype classification and therapeutic responsiveness. The integration of omics technologies, i.e. genomics, proteomics and metabolomics, has enabled deeper insights into disease mechanisms and facilitated early, individualised interventions. Concurrently, artificial intelligence (AI) and machine learning are emerging as tools for predicting exacerbations, identifying clinical subtypes and enhancing decision-making through large-scale data integration. Despite these advancements, challenges remain around standardisation, data quality and ensuring equitable access. This narrative review synthesises decades of progress in paediatric asthma care, emphasising the transition from empirical treatment to personalised, biomarker-driven strategies. It highlights current gaps, particularly in algorithm transparency, paediatric-specific validation and holistic care integration. As asthma management enters an era of digital health and AI-assisted precision medicine, future success will depend on interdisciplinary collaboration, real-world validation and policies that close care disparities.

Shareable abstractThe findings of this first systematic review of growth and nutrition in primary ciliary dyskinesia (PCD) highlight the need for timely and frequent assessment of nutrition and growth as standards of care in PCD.https://bit.ly/41jHpnv

BackgroundPrevious studies have shown that the algorithms and code lists used to define asthma exacerbations vary across different sources of data, if reported at all. Defining and validating asthma exacerbations in electronic health records (EHR) would help to improve future research on asthma using EHR by leading to more consistent and comparable evidence.MethodsWe systematically reviewed the literature to evaluate studies that define exacerbations of asthma in EHR and report which algorithms have the highest validity. An adapted version of the QUADAS-2 designed for this review was used to assess risk of bias.ResultsOf the studies yielded by the search, only five met the inclusion criteria. Eligible studies used algorithms that contained codes from versions or modifications of either the 9th or 10th revisions of the International Statistical Classification of Diseases and Related Health (ICD-9 or ICD-10), and validity scores varied. Using the ICD-9 code 493 within algorithms to detect asthma exacerbations, sensitivity scores varied from 44.8% to 91.28% and specificity was >85%. Using the ICD-9 code 493.xx as the principal and secondary diagnosis in claims data, validity measures were all >85%. Using the ICD-10 code J45, scores for sensitivity, specificity and negative predictive value were also all >85%.ConclusionsAlgorithms have been used to identify asthma exacerbations in EHR with varying degrees of validity. Algorithms including the ICD-9 code 493.xx or the ICD-10 code J45 to detect asthma exacerbations had high validity scores. However, there was a risk of bias in these studies and urgent work is needed using robust methods to validate definitions for future research using EHR.