Win Min Han, Bastian Neesgaard, Michael Knappik
et al.
Summary: Background: Data on cancer incidence and associated risk factors among women with HIV are limited. We investigated cancer burden among women with HIV. Methods: We included all women ≥18 years from the two large multicentre observational cohort collaborations (D:A:D and RESPOND). The primary outcomes were incidence of all cancers, HPV-related and common individual cancers including breast cancer, lung cancer, and non-Hodgkin lymphoma (NHL) from 2006 to 2021. Baseline was defined as the latest date of entry into local cohort enrolment and 1st January 2006 for D:A:D and 1st January 2012 for RESPOND. Participants were followed from baseline until the date of first cancer, final follow-up or administrative censoring—whichever occurred first. We assessed risk factors using multivariable Poisson regression by applying robust standard errors and determined a population attributable fraction (PAF) for key risk factors for cancers. Findings: Among 17,512 women included, median age at baseline was 39.5 years (interquartile range, IQR 32.5–46.0). Over 141,404 person-years (PYS) and a median 9.2 (5.5–10.1) years of follow-up, 832 women were diagnosed with any cancer; incidence rate 5.9 (95% CI 5.5–6.4)/1000 PYS, 163 HPV-related cancers (1.1 [1.0–1.3]/1000 PYS), 150 breast cancers (1.1 [0.9–1.2]/1000 PYS), 94 lung cancers (0.7 [0.5–0.8]/1000 PYS) and 72 NHL (0.5 [0.4–0.6]/1000 PYS). Older age (≥45 vs. <45 years), Southern Europe (vs. Western Europe) and smoking were associated with an increased risk of overall cancers. Lower pre-ART nadir CD4, time-updated CD4, and a prior AIDS diagnosis were associated with lung- and HPV-related cancer. In PAF analysis, smoking and HIV-related factors such as lower current CD4, nadir CD4 and HIV viremia significantly contributed to cancer risk. Interpretation: Our findings suggest that women with HIV older than 45 years, past or current immunosuppressed or current smokers could be candidates for intensified cancer screening and prevention. Funding: The Highly Active Antiretroviral Therapy Oversight Committee, The CHU St Pierre Brussels HIV Cohort, The Austrian HIV Cohort Study, The Australian HIV Observational Database, The AIDS Therapy Evaluation in the Netherlands national observational HIV cohort, The Brighton HIV Cohort, The National Croatian HIV Cohort, The EuroSIDA cohort, The Frankfurt HIV Cohort Study, The Georgian National AIDS Health Information System, The Nice HIV Cohort, The Isabel Foundation, The Modena HIV Cohort, The PISCIS Cohort Study, The Swiss HIV Cohort Study, The Swedish InfCare HIV Cohort, The Royal Free HIV Cohort Study, The San Raffaele Scientific Institute, The University Hospital Bonn HIV Cohort, The University of Cologne HIV Cohort, Merck Life Sciences, ViiV Healthcare, and Gilead Sciences.
As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure modes under realistic conditions. Despite the importance of respiratory audio for mobile health screening, respiratory audio question answering remains underexplored, with existing studies evaluated narrowly and lacking real-world heterogeneity across modalities, devices, and question types. We hence introduce the Respiratory-Audio Question-Answering (RA-QA) benchmark, including a standardized data generation pipeline, a comprehensive multimodal QA collection, and a unified evaluation protocol. RA-QA harmonizes public RA datasets into a collection of 9 million format-diverse QA pairs covering diagnostic and contextual attributes. We benchmark classical ML baselines alongside multimodal audio-language models, establishing reproducible reference points and showing how current approaches fail under heterogeneity.
Holly L Olvera, Andrew Bossert, Megan Koster
et al.
# Background
Approximately 3,500 infants die of a sleep-related incident a year in the United States. Although safe sleep guidelines have been implemented, infants are still at risk from many commercial products. Breathing-related injuries or suffocation are of serious concern for younger infants exposed to soft product materials, yet respiration-related measures of these common materials are unknown.
# Methods
Nine healthy young adults’ respiratory patterns were evaluated while breathing into materials commonly used in infant products. Breathing rate, end-tidal carbon dioxide (EtCO2), heart rate, and oxygen saturation (SpO2) were collected using a Capnostream 35. Participants lay prone with their faces in contact with each material for ten-minute trials. Three baseline trials, where participants could breathe freely with no obstruction, were collected for comparison (ANOVA (p < 0.05), Tukey post-hoc comparisons).
# Results
The 100% cotton and 50/50 (cotton/polyester) materials both resulted in significant changes in EtCO2 and SpO2, while the 10/90 (spandex/polyester) resulted in only a significant change in SpO2, and no significant changes were detected for the 100% polyester material. Mean respiratory rate decreased and mean heart rate increased significantly for all materials.
# Discussion
EtCO2 and SpO2 are important considerations for suffocation risk when breathing into the selected materials. Even during this short time period, infants with more vulnerable respiratory systems and less robust arousal responses than the adults in this study will be at higher risk.
# Conclusion
Conscious efforts should be made to prevent infants from interacting with soft goods microenvironments that inhibit normal breathing while using infant products.
Background Vilobelimab, a first in class C5a-specific monoclonal antibody, improved 28-day and 60-day mortality in intubated COVID-19 patients in PANAMO, a phase 3 randomised, double-blind, placebo-controlled multicentre study. All-cause mortality was pre-specified to be analysed pooling by region (western Europe, South America, South Africa/Russia).Methods Critically ill, invasively mechanically ventilated COVID-19 patients were randomised in a 1:1 ratio within 48 hours of intubation to receive vilobelimab treatment (six, 800 mg intravenous infusions) or placebo on top of standard of care. We analysed the efficacy and safety of vilobelimab based on prespecified geographic regions.Results 368 patients were randomised and analysed: 177 in the vilobelimab group and 191 in the placebo group. In western Europe (n=209), 28-day all-cause mortality was significantly lower in the vilobelimab group (21%) compared with placebo (37%) (HR 0.51 (95% CI: 0.30, 0.87), p=0.014). In South America (n=126), mortality was similar between groups (40% vs 37%; HR 0.94 (95% CI: 0.53, 1.67), p=0.83). In South Africa/Russia (n=33), mortality was 69% in the vilobelimab group and 87% in the placebo group (HR 0.62 (95% CI: 0.28, 1.38), p=0.25). Within the Brazilian subpopulation (n=74), a significant age imbalance between the vilobelimab and placebo group was detected (median 53.5 years in the vilobelimab group vs 44.5 years in the placebo group). Occurrence of treatment-emergent adverse events between regions was similar.Conclusion The most apparent 28-day all-cause mortality benefit for vilobelimab was in western Europe. Age imbalance between treatment groups in Brazil may have resulted in a lower efficacy signal for vilobelimab in South America compared with other regions. Overall, vilobelimab demonstrated a favourable safety profile and reduced mortality in critically ill, intubated COVID-19 patients, with regional variations influencing outcomes.
Jeanne I. M. Parmentier, Rhana M. Aarts, Elin Hernlund
et al.
Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and performance and ultimately improve equine welfare. In this work, we compare deep learning-based methods to an adapted signal processing method to automatically detect cyclic respiratory events and extract the dynamic respiratory rate from microphone recordings during high intensity exercise in Standardbred trotters. Our deep learning models are able to detect exhalation sounds (median F1 score of 0.94) in noisy microphone signals and show promising results on unlabelled signals at lower exercising intensity, where the exhalation sounds are less recognisable. Temporal convolutional networks were better at detecting exhalation events and estimating dynamic respiratory rates (median F1: 0.94, Mean Absolute Error (MAE) $\pm$ Confidence Intervals (CI): 1.44$\pm$1.04 bpm, Limits Of Agreements (LOA): 0.63$\pm$7.06 bpm) than long short-term memory networks (median F1: 0.90, MAE$\pm$CI: 3.11$\pm$1.58 bpm) and signal processing methods (MAE$\pm$CI: 2.36$\pm$1.11 bpm). This work is the first to automatically detect equine respiratory sounds and automatically compute dynamic respiratory rates in exercising horses. In the future, our models will be validated on lower exercising intensity sounds and different microphone placements will be evaluated in order to find the best combination for regular monitoring.
Stereotactic Arrhythmia Radiotherapy (STAR) treats ventricular tachycardia (VT) but requires internal target volume (ITV) expansions to compensate for cardiorespiratory motion. Current clinical r4DCT imaging methods are limited, and the reconstructed r4DCTs suffer from unmanaged cardiac motion artifacts that affect the quantitative assessment of respiratory motion. A groupwise surface-to-surface deformable image registration (DIR) algorithm, named gCGF, was developed. A novel principal component filtering (PCF) mechanism and a spatial smoothing mechanism were developed and incorporated into gCGF to iteratively register heart contours from an average respiratory-phase CT to ten r4DCT phases while removing random cardiac motion from the cyclic respiratory motion. The performance of the groupwise DIR was quantitatively validated using 8 digital phantoms with simulated cardiac artifacts. An ablation study was conducted to compare gCGF to another comparable state-of-the-art groupwise DIR method. gCGF was applied to r4DCTs of 20 STAR patients to analyze the respiratory motion of the heart. Validation on digital phantoms showed that gCGF achieved a mean target registration error of 0.63+-0.51 mm while successfully achieving phase smoothness and reducing cardiac motion artifacts. Among all STAR patients, the heart's maximum and mean respiratory motion magnitudes ranged from 3.6 to 7.9 mm and 1.0 mm to 2.6 mm. The peak-to-peak motion range was from 6.2 to 14.7 mm. For VT targets, the max and mean motion magnitude ranges were 3.0 to 6.7 mm and 0.8 to 2.9 mm, respectively. The peak-to-peak range was from 4.7 to 11.8 mm. Significant dominance of the first principal component of the motion direction was observed (p = 0).
Kuniaki Hirai,1 Akihiko Tanaka,1 Naruhito Oda,2 Keisuke Kaneko,3 Yoshitaka Uchida,1 Tomoki Uno,1 Shin Ohta,1 Tetsuya Homma,1 Fumihiro Yamaguchi,4 Shintaro Suzuki,1 Hironori Sagara1 1Department of Medicine, Division of Respiratory Medicine and Allergology, Showa University School of Medicine, Tokyo, Japan; 2Department of Medicine, Division of Respiratory Medicine, Yamanashi Red Cross Hospital, Yamanashi, Japan; 3Department of Medicine, Division of Respiratory Medicine, Tokyo Metropolitan Health and Hospitals Corporation Ebara Hospital, Tokyo, Japan; 4Department of Medicine, Division of Respiratory Medicine, Showa University Fujigaoka Hospital, Kanagawa, JapanCorrespondence: Kuniaki Hirai, Department of Medicine, Division of Respiratory Medicine and Allergology, Institute/University/Hospital: Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan, Tel +81-3-3784-8000, Fax +81-3-3784-8742, Email hiraik@med.showa-u.ac.jpBackground: Patients with chronic obstructive pulmonary disease (COPD) are more inclined to have a high level of social vulnerability due to their physical and psychological burden. However, to date, there have been no study on social frailty in patients with COPD. This study aimed to investigate the prevalence, characteristics, and impact of social frailty in patients with COPD.Methods: Social frailty was assessed using five items in a questionnaire. A patient was diagnosed with social frailty if responses to two or more items were positive. Four hundred and five patients with COPD were assessed for social frailty, dyspnea, and appetite. We also prospectively examined the number of acute exacerbation and unexpected hospitalization for 1 year.Results: Thirty-six percent of patients with COPD had social frailty. They had reduced appetite and more severe dyspnea [Simplified Nutritional Appetite Questionnaire score: odds ratio (OR) 0.81, 95% confidence interval (CI) 0.69‒0.95, p < 0.01; modified Medical Research Council score: OR 1.42, 95% CI 1.05‒1.93, P = 0.02] than patients without social frailty. Social frailty was not a risk factor for moderate acute exacerbation of COPD but a risk factor for severe acute exacerbation and all-cause unexpected hospitalization (severe acute exacerbation: β, standardized regression coefficient: 0.13, 95% CI 0.01‒0.25, P = 0.04, unexpected hospitalization: β 0.17, 95% CI 0.05‒0.29, P = 0.01).Conclusion: The prevalence of social frailty is 36%; however, social frailty has a marked clinical impact in patients with COPD.Keywords: social frailty, chronic obstructive pulmonary disease, social robustness
Felix Plappert, Gunnar Engström, Pyotr G. Platonov
et al.
Information about autonomic nervous system (ANS) activity may be valuable for personalized atrial fibrillation (AF) treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in AV nodal refractory period and conduction delay. A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where a ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. We demonstrated using synthetic data that the 1D-CNN can predict the respiratory modulation from RR series alone ($ρ$ = 0.805) and that the addition of either respiration signal ($ρ$ = 0.830), AFR ($ρ$ = 0.837), or both ($ρ$ = 0.855) improves the prediction. Results from analysis of clinical ECG data of 20 patients with sufficient signal quality suggest that respiratory modulation decreased in response to deep breathing for five patients, increased for five patients, and remained similar for ten patients, indicating a large inter-patient variability.
Oliver Eales, Michael J. Plank, Benjamin J. Cowling
et al.
To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.
In the world of epidemics, the mathematical modeling of disease co-infection is gaining importance due to its contributions to mathematics and public health. Because the co-infection may have a double burden on families, countries, and the universe, understanding its dynamics is paramount. We study a SEIQR (susceptible-exposed-infectious-quarantined-recovered) deterministic epidemic model with a single host population and multiple strains (-$c$ and -$i$) to account for two competitive diseases with quarantine effects. To model the role of quarantine and isolation efficacy in disease dynamics, we utilize a linear function. Further, we shed light on the standard endemic threshold and determine the conditions for extinction or coexistence with and without forming co-infection. Next, we show the dependence of the criticality based on specific parameters of the different pathogens. We found that the disease-free equilibrium (DFE) of the single-strain model always exists and is globally asymptotically stable (GAS) if $\tilde{\mathcal{R}}_k^q\leq 1$, else, a stable endemic equilibrium. On top of that, the model has forward bifurcation at $\tilde{\mathcal{R}}_k^q = 1$. In the case of a two-strain model, the strain with a large reproduction number outcompetes the one with a smaller reproduction number. Further, if the co-infected quarantine reproduction number is less than one, the infections of already infected individuals will die out, and co-infection will persist in the population otherwise. We note that the quarantine and isolation of exposed and infected individuals will reduce the number of secondary cases below one, consequently reducing the disease complications if the total number of people in the quarantine is at most the critical value.
Anuradha N. Godallage, Shailesh Kolekar, Karen Ege Olsen
et al.
Dental care workers are frequently exposed to various types of volatile organic and inorganic compounds. In addition to biological materials, these compounds include silica, heavy metals, and acrylic plastics. Such exposures may cause respiratory symptoms, but the nonspecific nature of these symptoms often means that the etiology is difficult to discern. The disease severity depends on the particle size and type of the inhaled compounds, as well as the duration and intensity of exposure, which varies markedly among dental workers. Here, we present two unique cases with the same occupational exposure. Both patients showed radiological changes in the lungs that were suspicious for lung cancer.The first patient did not undergo a biopsy due to cardiac comorbidities and risk of bleeding, and the diagnosis was based on thoracic computer tomography (CT) which confirmed multiple, bilateral, solid, smooth, partly calcified lung nodules, normal positron emission tomography (PET)-CT and the relevant occupational exposure. In the second case, a CT-guided biopsy and thoracoscopic resection was done with histopathological findings consistent with granuloma. The multi-disciplinary team decision of both cases was consistent with occupational exposure related lunge disease.This is the first case study report whereby same occupational exposure related health condition is compared with two different approaches. Respiratory clinicians should be aware of this potential diagnosis, especially for asymptomatic patients with relevant exposures. Careful attention to the occupational history may help to prevent unnecessary, invasive diagnostic procedures or surgeries.
Ping-Chih Hsu, John Wen-Cheng Chang, Ching-Fu Chang
et al.
Background: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are standard treatments for advanced EGFR-mutated non–small cell lung cancer (NSCLC) patients. Osimertinib is an effective therapy for NSCLC patients with acquired resistance due to T790M mutation after first- and second-generation EGFR-TKI treatment. This study aimed to analyze the clinical outcomes of sequential therapy following first-line EGFR-TKIs and the predictive factors of an acquired T790M mutation. Methods: Between January 2014 and December 2018, data from 2190 advanced NSCLC patients with common EGFR mutations (exon 19 deletion and L858R) receiving first- and second-generation EGFR-TKIs in Linkou, Kaohsiung, Chiayi and Keelung Chang Gung Memorial Hospitals were retrospectively retrieved and analyzed. Results: Until August 2021, among 1943 patients who experienced progressive disease, 526 underwent T790M mutation tests, and their T790M-positive rate was 53.6%. Exon 19 deletion mutation and progression-free survival (PFS) of >12 months were positively associated with secondary T790M mutation. Different first-line first- and second-generation EGFR-TKI therapies did not affect the appearance of acquired T790M mutations. The median overall survival (OS) was 58.3 [95% confidence interval (CI): 49.0–67.5] months among the patients with T790M mutation who received second-line osimertinib therapy compared with 31.0 (95% CI: 27.5–34.5) months among the patients without T790M mutation who received chemotherapy alone. The multivariate analysis showed that a poor performance status (score: >2), nonadenocarcinoma histology, stage IV cancer, liver metastasis, brain metastasis, PFS while on first-line EGFR-TKIs, and subsequent chemotherapy without third-generation EGFR-TKIs were significant independent unfavorable prognostic factors for OS. Conclusion: This study demonstrated the efficacy of first-line EGFR-TKIs and sequential osimertinib therapy. The results of our study suggest that T790M mutation tests are important for the use of subsequent osimertinib, which yielded favorable survival outcomes.
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve multiple pretext tasks that learn a good representation of data. We apply our approach on respiratory sound classification. This task is suited for this setting as demographic information such as sex and age are correlated with presence of lung diseases, and learning a system that implicitly encode this information may better detect anomalies. Supervised contrastive learning is a paradigm that learns similar representations to samples sharing the same class labels and dissimilar representations to samples with different class labels. The feature extractor learned using this paradigm extract useful features from the data, and we show that it outperforms cross-entropy in classifying respiratory anomalies in two different datasets. We also show that learning representations using only metadata, without class labels, obtains similar performance as using cross entropy with those labels only. In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned. This work suggests the potential of using multiple metadata sources in supervised contrastive settings, in particular in settings with class imbalance and few data. Our code is released at https://github.com/ilyassmoummad/scl_icbhi2017
Shane D. Ross, Jeremie Fish, Klaus Moeltner
et al.
An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011-2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches.
We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units. A contactless device is used, which contains four load sensors mounted under the bed and one data processing unit (data logger). Various machine learning methods are applied to the data streamed from the data logger to detect the Respiratory Rate (RR). We have implemented Support Vector Machine (SVM) and also Neural Network (NN)-based pattern recognition methods, which are combined with either peak detection or Hilbert transform for robust RR calculation. Experimental results show that our methods could effectively extract RR using the data collected by contactless bed sensors. The proposed methods are robust to outliers and noise, which are caused by body movements. The monitoring system provides a flexible and scalable way for continuous and remote monitoring of sleep, movement and weight using the embedded sensors.