Hasil untuk "Diseases of the respiratory system"

J. Holter, S. Pischke, Eline de Boer et al.

Significance The new SARS-CoV-2 pandemic leads to COVID-19 with respiratory failure, substantial morbidity, and significant mortality. Overactivation of the innate immune response is postulated to trigger this detrimental process. The complement system is a key player in innate immunity. Despite a few reports of local complement activation, there is a lack of evidence that the degree of systemic complement activation occurs early in COVID-19 patients, and whether this is associated with respiratory failure. This study shows that a number of complement activation products are systemically, consistently, and long-lastingly increased from admission and during the hospital stay. Notably, the terminal sC5b-9 complement complex was associated with respiratory failure. Thus, complement inhibition is an attractive therapeutic approach for treatment of COVD-19. Respiratory failure in the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is hypothesized to be driven by an overreacting innate immune response, where the complement system is a key player. In this prospective cohort study of 39 hospitalized coronavirus disease COVID-19 patients, we describe systemic complement activation and its association with development of respiratory failure. Clinical data and biological samples were obtained at admission, days 3 to 5, and days 7 to 10. Respiratory failure was defined as PO2/FiO2 ratio of ≤40 kPa. Complement activation products covering the classical/lectin (C4d), alternative (C3bBbP) and common pathway (C3bc, C5a, and sC5b-9), the lectin pathway recognition molecule MBL, and antibody serology were analyzed by enzyme-immunoassays; viral load by PCR. Controls comprised healthy blood donors. Consistently increased systemic complement activation was observed in the majority of COVID-19 patients during hospital stay. At admission, sC5b-9 and C4d were significantly higher in patients with than without respiratory failure (P = 0.008 and P = 0.034). Logistic regression showed increasing odds of respiratory failure with sC5b-9 (odds ratio 31.9, 95% CI 1.4 to 746, P = 0.03) and need for oxygen therapy with C4d (11.7, 1.1 to 130, P = 0.045). Admission sC5b-9 and C4d correlated significantly to ferritin (r = 0.64, P < 0.001; r = 0.69, P < 0.001). C4d, sC5b-9, and C5a correlated with antiviral antibodies, but not with viral load. Systemic complement activation is associated with respiratory failure in COVID-19 patients and provides a rationale for investigating complement inhibitors in future clinical trials.

S. Hay, K. Ong, D. Santomauro et al.

Summary Background For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. Methods The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010–23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Findings Total numbers of global DALYs grew 6·1% (95% UI 4·0–8·1), from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0–14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31–1·61) global DALYs in 2010, increasing to 1·80 billion (1·63–2·03) in 2023, alongside a concurrent 4·1% (1·9–6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176–209] DALYs), stroke (157 million [141–172]), and diabetes (90·2 million [75·2–107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0–107·5]), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and a 25·8% (22·6–28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7–61·0) for diarrhoeal diseases, 42·9% (38·0–48·0) for HIV/AIDS, and 42·2% (23·6–56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6–22·0) and 24·8% (7·4–36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7–19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18–1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation—with high SBP accounting for 8·4% (6·9–10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories—behavioural, metabolic, and environmental and occupational—risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8–37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0–11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023—eg, declining by 54·4% (38·7–65·3) for unsafe sanitation, 50·5% (33·3–63·1) for unsafe water source, and 45·2% (25·6–72·0) for no access to handwashing facility, and by 44·9% (37·3–53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [–2·7 to 15·6]; non-significant). Interpretation Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors—eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG—including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic—the complex interaction of multiple health risks, social determinants, and systemic challenges—will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Funding Gates Foundation and Bloomberg Philanthropies.

Mohamed Adam, Gamal Agmy, Mahmoud G.H. Ali et al.

Background Pre-existing clinical conditions, such as cardiovascular diseases, diabetes, cancer, and others, have been described to possibly modulate the immune responses and host–viral interactions, and increase patients’ risk for severe forms of coronavirus disease 2019 (COVID-19) and poor outcomes. Objective This study aimed to investigate the relationship between comorbidities, complications, and outcomes among COVID-19 patients. Patients and methods A prospective cohort study (n=218) was carried out at the isolation unit of Assiut University Hospitals. Infection by severe acute respiratory syndrome coronavirus 2 was confirmed by real-time reverse transcription PCR. Demographic characteristics, clinical symptoms, pre-existing comorbidities, laboratory data, and arterial blood gas analysis were all documented and analyzed. Imaging modalities, Sequential Organ Failure Assessment (SOFA) score, Pneumonia Severity Index, and CURB-65 score were applied. Complications and patients’ outcomes were recorded. Results Hypertension, diabetes mellitus, and ischemic heart disease were significantly higher among nonsurvivors. Significant predictors associated with mortality were: pre-existing comorbid disease (P=0.034), low mean blood pressure; low levels of PaO2 and PaO2/FiO2 at admission (P=0.004, P=0.034, P˂0.001, respectively); occurrence of complications (P=0.004); increased level of serum ferritin (P=0.045). The diagnostic accuracy for prediction of mortality for SOFA score, Pneumonia Severity Index, CURB-65, neutrophil lymphocyte ratio, serum ferritin, D-dimer, and PaO2/FiO2 was 79, 75.5, 71, 78, 68, 60.5, and 73.5%, respectively at cut off points more than 8, more than 99, more than 2, more than 10, more than 870, more than 1, less than 138, respectively. Conclusion The existence of certain comorbidities and complications significantly influences the outcomes of COVID-19 patients. Notably, low mean blood pressure, low PaO2, and low PaO2/FiO2 at admission, duration of ICU stay, occurrence of complications, serum ferritin, and SOFA score were all factors related to mortality. This study highlights the importance of individualized patient care and the need for early recognition of severity and mortality predictors to improve patients’ outcome.

Truc The Nguyen, Minh D. N. Nguyen, S. V. Nguyen et al.

We propose a multi-channel multiple–instance learning (MIL) framework for automated respiratory disease classification that addresses acoustic heterogeneity among recording site conditions, handles weak supervision, and reflects spatial cues in auscultation. The system combines a tailored residual encoder with a 16-channel–adapted ResNet and aggregates segment embeddings through gated attention to yield patient-level predictions. Training employs task-matched losses and light, label-preserving augmentations. We evaluate the framework on a newly introduced 16-channel dataset (SPSC-HCM-16C, 183 subjects) under three taxonomies: binary (healthy vs. unhealthy), 3-class (healthy/chronic/non-chronic), and 5-class. Using subject-independent (patient-wise) evaluation with a 60/40 train–test split, the proposed method consistently outperforms all baselines, achieving an F1 score of 97.3% with a sensitivity of 99.1% in the binary task, an F1 score of 77.3% in the 3-class task, and an F1 score of 50.6% in the challenging 5-class setting. Transfer learning on the ResNet branch provides up to a 17.6 percentage-point improvement in macro-F1, while simple augmentations further enhance the minority-class recall. The largest gains arise from explicitly modeling multi-site spatial information via the dual-encoder with gated attention, which stabilizes patient-level decisions and improves the detection of rare classes. Task-appropriate objectives further strengthen performance, with second-order polynomial loss proving most effective for the binary and 5-class tasks, and focal loss favoring the 3-class setting. Overall, the results demonstrate that leveraging spatial cues within a patient-level MIL framework enables robust, interpretable, and clinically meaningful respiratory disease classification.

Stijn J M Niessen, Ellen N. Behrend, F. Fracassi et al.

Simple Summary To make progress in the field of hormonal diseases in companion animals, it helps when researchers, clinicians, and educators use the same language. Currently, there is no consensus on basic concepts such as what constitutes the correct definition of diseases affecting the adrenal glands, important hormone-producing glands situated next to the kidneys. This publication reports on the second cycle of a novel project called “Agreeing Language in Veterinary Endocrinology” (ALIVE) that brings experts and those interested in the field together to try and achieve consensus on such disease definitions. The cycle’s methods were adapted from previous ones to improve efficiency and were completed successfully, accomplishing a majority-based consensus. It also delivered agreement on diagnostic criteria for adrenal diseases in companion animals. It is hoped the work will improve education, diagnosis, and treatment in this field, ultimately leading to improvements in the quality of life of animals suffering from adrenal disease.

Shanmukha Priya Satuluri, Rakesh Kodati, Narendra Kumar Narahari et al.

Tuberculosis is the initial etiology considered during evaluation of miliary opacities on chest radiograph in endemic regions. Further characterization of opacities requires performing a thin-section computed tomography (CT) scan. Here, we report a case of pulmonary alveolar microlithiasis (PAM) in a 35-year-old lady with respiratory symptoms. She was initially diagnosed as miliary tuberculosis and was prescribed antitubercular therapy, without any clinical relief. We considered the probability of PAM after performing a CT scan, which showed interstitial septal thickening and dense pleuropulmonary calcifications. The diagnosis was confirmed by whole-exome sequencing, which revealed a homozygous mutation in the <i>SLC34A2</i> gene (c.675G>A). The need for physicians to be aware of the classic presentation of such a rare disease is highlighted in this case. How to cite this article: Satuluri SP, Kodati R, Narahari NK, <i>et al.</i> Pulmonary Alveolar Microlithiasis: A Rare Genetic Lung Disease Mistaken for Miliary Tuberculosis —A Case Report. Indian J Respir Care 2025;14(2):127–129.

Koo HK, Chung SJ, Park D et al.

Hyeon-Kyoung Koo,1 Sung Jun Chung,1 Dongil Park,2 Ho Cheol Kim,3 Hyewon Seo,4 Hyun Jung Kim,5 Hyoung Kyu Yoon,6 Chin Kook Rhee,7 Kwang Ha Yoo,8 Deog Kyeom Kim9 1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea; 2Department of Internal Medicine, Division of Pulmonology, College of Medicine, Chungnam National University, Deajeon, Republic of Korea; 3Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, Republic of Korea; 4Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Republic of Korea; 5Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Keimyung University School of Medicine, Dongsan Hospital, Daegu, Republic of Korea; 6Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Yeouido St Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea; 7Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; 8Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea; 9Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of KoreaCorrespondence: Deog Kyeom Kim, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 20 Boramaero-5-Gil, Dongjak-Gu, Seoul, 07061, Republic of Korea, Tel +82-2-870-2228, Fax +82-2-831-2826, Email kimdkmd@snu.ac.krBackground and Objective: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, with chronic bronchitis (CB) and emphysema phenotypes. The aim of our study was to compare the distinct patterns of correlation networks for respiratory symptoms and predictors of future exacerbations of different COPD phenotypes.Methods: CB and emphysema were identified using a questionnaire and computed tomography images, respectively, and also included patients with preserved ratio impaired spirometry (PRISm). We constructed separate correlation networks for each subgroup using Spearman correlation coefficients. Predictors of future exacerbations were selected via least absolute shrinkage and selection operation regression analyses in multivariable analysis.Results: Among the 3436 patients, 2232 were non-CB, 1131 were CB, 1116 were emphysema, and 73 were PRISm groups. The forced expiratory volume in one second (FEV1) and respiratory symptoms worsened in the following order: PRISm, non-CB, emphysema, and CB groups. During the 1-year follow-up, 17.3%, 21.3%, and 18.9% of patients in the non-CB, CB, and emphysema groups, respectively, experienced exacerbation. Each group showed a distinct correlation pattern between demographic characteristics, comorbidities, pulmonary function, blood biomarkers, respiratory symptoms, and exercise capacity. Across all groups, lower FEV1 (%), higher white blood cell count, higher erythrocyte sedimentation rate, and worse Saint George’s Respiratory Questionnaire symptom and total scores were identified as common risk factors for future exacerbations. However, each group showed distinct predictors for future exacerbations.Conclusion: The correlation network patterns and predictors of future exacerbations varied significantly depending on the COPD phenotype. Further research is required to understand the heterogeneous COPD pathophysiology and facilitate personalized medicine.Plain Language Summary: COPD has various subtypes, including chronic bronchitis, emphysema, and PRISm phenotypes. This study compared symptom patterns and predictors of future exacerbation in these groups. We analyzed data from over 3400 patients and observed that lung function and symptoms worsened in the following order: PRISm, non-chronic bronchitis, emphysema, and chronic bronchitis. Each group showed distinct patterns of relationships between demographics, lung function, biomarkers, and respiratory symptoms. Although some risk factors overlapped, each group had its own predictors for future exacerbation. Understanding these differences among subtypes could lead to better personalized treatments for COPD patients.Keywords: COPD, exacerbation, heterogeneous, network analysis

Yu Chen^*+, Haoyi Liu^*+, Shiyu Liu^*+ et al.

Introduction Adolescent tobacco use has become a serious global public health problem, and effective tobacco control public service advertisements (PSAs) are crucial for reducing adolescent smoking rates. The study aims to employ a mixedmethods approach combining quantitative surveys and qualitative focus groups to evaluate the effectiveness of different types of tobacco control PSAs among Chinese adolescents, identify effective advertising characteristics and content elements, and provide empirical evidence for optimizing youth tobacco control communication strategies. Methods A total of 125 students aged 10–18 years were recruited from six primary and secondary schools in Beijing and Kunming from November 2020 to April 2021. Participants completed Likert-scale ratings measuring advertisement effectiveness after viewing eight tobacco control PSAs and participated in focus group interviews. Quantitative data were analyzed using independent samples t-tests, Spearman correlation analysis, and multivariable logistic regression analysis, while qualitative data were analyzed using thematic analysis. All statistical tests were two-tailed with significance set at p<0.05. Results Quantitative analysis revealed that PSAs employing ‘testimonials’ and ‘disease’ frameworks were most strongly associated with prevention intentions, while those using ‘celebrity endorsement’, ‘humor’ and ‘appearance damage’ frameworks showed the weakest associations. Kunming adolescents showed significantly higher advertisement acceptance scores than Beijing adolescents (mean difference=0.21; 95% CI: 0.04–0.38, p<0.05). The 10-item effectiveness scale demonstrated good internal consistency (Cronbach’s α=0.82). Qualitative analysis identified effective characteristics including presentation of specific health hazards, use of testimonials, and fear appeals; ineffective characteristics included non-specific harm presentation, use of humorous elements, and appearance damage content. Conclusions Tobacco control PSA design should consider strategies combining disease warnings with real-life testimonials, avoid humorous advertisements and industry-sponsored messaging, and consider regional cultural differences. Distribution through online and social media platforms frequently used by adolescents may enhance reach. Future longitudinal research with broader geographical sampling is needed to confirm these findings.

Nuria Rodríguez‐Núñez, Alejandra Virgós‐Pedreira, Alfonso Illade‐Fornos et al.

ABSTRACT We present the case of a 42‐year‐old woman on oral contraceptives that presented to the emergency department with pain and swelling in the left lower limb. Diagnosis of extensive deep vein thrombosis was established. A few minutes later, she exhibited signs of shock and hemodynamic instability, thus raising suspicion of high‐risk acute pulmonary thromboembolism. Prior to the administration of fibrinolytic treatment, a bedside transthoracic echocardiography was performed that excluded right ventricular dilatation. Then, the study was complemented with a thoraco‐abdominal computed tomography scan that demonstrated a large retroperitoneal hematoma as the cause of the shock. In conclusion, a transthoracic echocardiography should be performed before initiating thrombolytic therapy in hemodynamically instable patients with strong suspicion of high‐risk pulmonary embolism.

Anete Trajman, Ezio Távora dos Santos Filho, Ricardo Arcêncio

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda et al.

Recent advancements in foundation models have sparked interest in respiratory audio foundation models. However, the effectiveness of applying conventional pre-training schemes to datasets that are small-sized and lack diversity has not been sufficiently verified. This study aims to explore better pre-training practices for respiratory sounds by comparing numerous pre-trained audio models. Our investigation reveals that models pre-trained on AudioSet, a general audio dataset, are more effective than the models specifically pre-trained on respiratory sounds. Moreover, combining AudioSet and respiratory sound datasets for further pre-training enhances performance, and preserving the frequency-wise information when aggregating features is vital. Along with more insights found in the experiments, we establish a new state-of-the-art for the OPERA benchmark, contributing to advancing respiratory audio foundation models. Our code is available online at https://github.com/nttcslab/eval-audio-repr/tree/main/plugin/OPERA.

Shaheer Ahmad Khan, Muhammad Usamah Shahid, Ahmad Abdullah et al.

This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the intrinsic explainability of Random Forests.

Alberto Pliego Marugán, Jesús M. Pinar-Pérez, Fausto Pedro García Márquez

Efficient maintenance has always been essential for the successful application of engineering systems. However, the challenges to be overcome in the implementation of Industry 4.0 necessitate new paradigms of maintenance optimization. Machine learning techniques are becoming increasingly used in engineering and maintenance, with reinforcement learning being one of the most promising. In this paper, we propose a gamma degradation process together with a novel maintenance model in which repairs are increasingly imperfect, i.e., the beneficial effect of system repairs decreases as more repairs are performed, reflecting the degradational behavior of real-world systems. To generate maintenance policies for this system, we developed a reinforcement-learning-based agent using a Double Deep Q-Network architecture. This agent presents two important advantages: it works without a predefined preventive threshold, and it can operate in a continuous degradation state space. Our agent learns to behave in different scenarios, showing great flexibility. In addition, we performed an analysis of how changes in the main parameters of the environment affect the maintenance policy proposed by the agent. The proposed approach is demonstrated to be appropriate and to significatively improve long-run cost as compared with other common maintenance strategies.

Young-Chan Kim, Kyoung-Hee Sohn, Hye-Ryun Kang

The emerging field of gut-lung axis research has revealed a complex interplay between the gut microbiota and respiratory health, particularly in asthma. This review comprehensively explored the intricate relationship between these two systems, focusing on their influence on immune responses, inflammation, and the pathogenesis of respiratory diseases. Recent studies have demonstrated that gut microbiota dysbiosis can contribute to asthma onset and exacerbation, prompting investigations into therapeutic strategies to correct this imbalance. Probiotics and prebiotics, known for their ability to modulate gut microbial compositions, were discussed as potential interventions to restore immune homeostasis. The impact of antibiotics and metabolites, including short-chain fatty acids produced by the gut microbiota, on immune regulation was examined. Fecal microbiota transplantation has shown promise in various diseases, but its role in respiratory disorders is not established. Innovative approaches, including mucus transplants, inhaled probiotics, and microencapsulation strategies, have been proposed as novel therapeutic avenues. Despite challenges, including the sophisticated adaptability of microbial communities and the need for mechanistic clarity, the potential for microbiota-based interventions is considerable. Collaboration between researchers, clinicians, and other experts is essential to unravel the complexities of the gut-lung axis, paving a way for innovative strategies that could transform the management of respiratory diseases.

C. Casadevall, Sara Quero, L. Millares et al.

The respiratory microbiome may influence the development and progression of COPD by modulating local immune and inflammatory events. We aimed to investigate whether relative changes in respiratory bacterial abundance are also associated with systemic inflammation, and explore their relationship with the main clinical COPD phenotypes. Multiplex analysis of inflammatory markers and transcript eosinophil-related markers were analyzed on peripheral blood in a cohort of stable COPD patients (n = 72). Respiratory microbiome composition was analyzed by 16S rRNA microbial sequencing on spontaneous sputum. Spearman correlations were applied to test the relationship between the microbiome composition and systemic inflammation. The concentration of the plasma IL-8 showed an inverted correlation with the relative abundance of 17 bacterial genera in the whole COPD cohort. COPD patients categorized as eosinophilic showed positive relationships with blood eosinophil markers and inversely correlated with the degree of airway obstruction and the number of exacerbations during the previous year. COPD patients categorized as frequent exacerbators were enriched with the bacterial genera Pseudomonas which, in turn, was positively associated with the severity of airflow limitation and the prior year’s exacerbation history. The associative relationships of the sputum microbiome with the severity of the disease emphasize the relevance of the interaction between the respiratory microbiota and systemic inflammation.

Jesús Villar, J. González-Martín, C. Férnandez et al.

Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods: This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8–25) vs. 13 days (IQR 7–21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO2/FiO2, PaCO2, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65–0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).

N. Torner, N. Soldevila, L. Basile et al.

Background: During seasonal influenza activity, circulation of other respiratory viruses (ORVs) may contribute to the increased disease burden that is attributed to influenza without laboratory confirmation. The objective of this study was to characterize and evaluate the magnitude of this contribution over 12 seasons of influenza using the Acute Respiratory Infection Sentinel Surveillance system in Catalonia (PIDIRAC). Methods: A retrospective descriptive study of isolations from respiratory samples obtained by the sentinel surveillance network of physicians was carried out from 2008 to 2020 in Catalonia, Spain. Information was collected on demographic variables (age, sex), influenza vaccination status, epidemic activity weeks each season, and influenza laboratory confirmation. Results: A total of 12,690 samples were collected, with 46% (5831) collected during peak influenza seasonal epidemic activity. In total, 49.6% of the sampled participants were male and 51.1% were aged 64 (21.5%, 10.8%, 8.2% and 7.6%: p < 0.001). A lower ORVs coinfection ratio was observed in the influenza-vaccinated population (11.9% vs. 17.4% OR: 0.64 IC 95% 0.36–1.14). Conclusions: During the weeks of seasonal influenza epidemic activity, other respiratory viruses contribute substantially, either individually or through the coinfection of two or more viruses, to the morbidity attributed to influenza viruses as influenza-like illness (ILI). The contribution of these viruses is especially significant in the pediatric and elderly population. Identifying the epidemiology of most clinically relevant respiratory viruses will aid the development of models of infection and allow for the development of targeted treatments, particularly for populations most vulnerable to respiratory viruses-induced diseases.

Natália de Souza Xavier Costa, Giovana da Costa Sigrist, Alexandre Santos Schalch et al.

Abstract Background Acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure in critically ill patients, and diffuse alveolar damage (DAD) is considered its histological hallmark. Sepsis is one of the most common aetiology of ARDS with the highest case-fatality rate. Identifying ARDS patients and differentiate them from other causes of acute respiratory failure remains a challenge. To address this, many studies have focused on identifying biomarkers that can help assess lung epithelial injury. However, there is scarce information available regarding the tissue expression of these markers. Evaluating the expression of elafin, RAGE, and SP-D in lung tissue offers a potential bridge between serological markers and the underlying histopathological changes. Therefore, we hypothesize that the expression of epithelial injury markers varies between sepsis and ARDS as well as according to its severity. Methods We compared the post-mortem lung tissue expression of the epithelial injury markers RAGE, SP-D, and elafin of patients that died of sepsis, ARDS, and controls that died from non-pulmonary causes. Lung tissue was collected during routine autopsy and protein expression was assessed by immunohistochemistry. We also assessed the lung injury by a semi-quantitative analysis. Results We observed that all features of DAD were milder in septic group compared to ARDS group. Elafin tissue expression was increased and SP-D was decreased in the sepsis and ARDS groups. Severe ARDS expressed higher levels of elafin and RAGE, and they were negatively correlated with PaO2/FiO2 ratio, and positively correlated with bronchopneumonia percentage and hyaline membrane score. RAGE tissue expression was negatively correlated with mechanical ventilation duration in both ARDS and septic groups. In septic patients, elafin was positively correlated with ICU admission length, SP-D was positively correlated with serum lactate and RAGE was correlated with C-reactive protein. Conclusions Lung tissue expression of elafin and RAGE, but not SP-D, is associated with ARDS severity, but does not discriminate sepsis patients from ARDS patients.

Sebastian Reidy, Manuel Meier, Christian Holz

Deviations in respiratory rate often precede abnormalities in other vital signs. However, continuously monitoring respiratory rates outside clinical settings remains challenging due to the obtrusive nature and sensitivity to body motions in existing monitoring approaches. In this study, we propose a single-point-of-contact wearable device that leverages off-the-shelf, consumer-grade ultra-wideband radar to monitor respiratory rate as part of a chest strap. Our signal processing pipeline reliably extracts the wearer's respiratory signal from windowed complex channel impulse responses. In a controlled experiment, twelve participants performed various activities to evaluate the system's accuracy under motion while capturing ground-truth recordings through a spirometer. Our method extracted respiratory rates with less than 1 breath per minute deviation in 71% of all measurements, averaging 1.11 breaths per minute across all sessions and participants. Our findings underscore the potential of consumer-grade ultra-wideband radar technology in body-worn devices for unobtrusive yet effective respiratory monitoring.

Haruto Kobayashi, Yuji Tanaka, Takuya Sakamoto

This study proposes a method for radar-based identification of individuals using a combination of their respiratory and heartbeat features. In the proposed method, the target individual's respiratory features are extracted using the modified raised-cosine-waveform model and their heartbeat features are extracted using the mel-frequency cepstral analysis technique. To identify a suitable combination of features and a classifier, we compare the performances of nine methods based on various combinations of three feature vectors with three classifiers. The accuracy of the proposed method in performing individual identification is evaluated using a 79-GHz millimeter-wave radar system with an antenna array in two experimental scenarios and we demonstrate the importance of use of the combination of the respiratory and heartbeat features in achieving accurate identification of individuals. The proposed method achieves accuracy of 96.33% when applied to a five-day dataset of six participants and 99.39% when applied to a public one-day dataset of thirty participants.