55,152 research outputs found

    COPD in Never-Smokers: BOLD Australia Study

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    Marsha A Ivey,1,2 Sheree M Smith,3,4 Geza Benke,1 Brett G Toelle,5,6 Michael L Hunter,7 Alan L James,8 Graeme P Maguire,9 Richard Wood-Baker,10 David P Johns,11 Guy B Marks,5,12 Michael J Abramson1 1School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia; 2School of Medicine, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago; 3School of Nursing and Midwifery, Campbelltown Campus, Western Sydney University, Penrith, NSW, 2751, Australia; 4Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia; 5Respiratory and Environmental Epidemiology Group, Woolcock Institute of Medical Research, Sydney, NSW, 2037, Australia; 6Sydney Local Health District, Sydney, NSW, 2050, Australia; 7School of Population and Global Health, University of Western Australia, Perth, WA, 6009, Australia; 8Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital and Medical School, University of Western Australia, Perth, WA, 6009, Australia; 9Curtin Medical School, Curtin University, Perth, WA, 6102, Australia; 10School of Medicine, University of Tasmania, Hobart, TAS, 7000, Australia; 11Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia; 12School of Clinical Medicine, University of New South Wales, Sydney, NSW, 2052, AustraliaCorrespondence: Michael J Abramson, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia, Tel +61 3 9903 0573, Fax +61 3 9903 0556, Email [email protected]: Tobacco smoking is the major risk factor for COPD, and it is common for other risk factors in never-smokers to be overlooked. We examined the prevalence of COPD among never-smokers in Australia and identified associated risk factors.Methods: We used data from the Australia Burden of Obstructive Lung Disease (BOLD) study, a cross-section of people aged ‚Č• 40 years from six sites. Participants completed interviews and post-bronchodilator spirometry. COPD was primarily defined as an FEV1/FVC ratio < 0.70 and secondarily as the ratio less than the lower limit of normal (LLN).Results: The prevalence of COPD in the 1656 never-smokers who completed the study was 10.5% (95% CI: 9.1‚Äď 12.1%) [rati

    Consistent patterns of common species across tropical tree communities

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    Trees structure the Earth‚Äôs most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10‚ÄČcm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth‚Äôs 800 billion tropical trees with trunk diameters of at least 10‚ÄČcm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world‚Äôs most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

    Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

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    The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol

    Nutritional composition of ultra-processed plant-based foods in the out-of-home environment: a multi-country survey with plant-based burgers.

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    Ultra-processed plant-based foods, such as plant-based burgers have gained in popularity. Particularly in the out-of-home (OOH) environment, evidence regarding their nutritional profile and environmental sustainability is still evolving. Plant-based burgers available at selected OOH sites were randomly sampled in cities of four WHO European Member States; Amsterdam, Copenhagen, Lisbon, and London. Plant-based burgers (patty, bread and condiment) (n=41) were lab-analysed for their energy, macronutrients, amino acids, and minerals content per 100g and serving, and were compared with reference values. For the plant-based burgers, the median values per 100g were: 234 kcal, 20.8g carbohydrates, 3.5g dietary fibre, and 12.0g fat, including 0.08g TFA and 2.2g SFA. Protein content was 8.9g/100g, with low protein quality according to amino acid composition. Median sodium content was 389mg/100g, equivalent to 1g salt. Compared with references, the median serving of plant-based burgers provided 31% of energy intake based on a 2,000 kcal per day, and contributed to carbohydrates(17-28%), dietary fibre(42%), protein(40%), total fat(48%), SFA(26%), and sodium(54%). One serving provided 15-23% of the reference values for calcium, potassium, and magnesium, while higher contributions were found for zinc(30%), manganese(38%), phosphorus(51%), and iron(67%). The ultra-processed plant-based burgers, provide protein, dietary fibre and essential minerals, but also contain relatively high levels of energy, sodium, and total fats. The amino acid composition of the plant-based burgers indicated low protein quality. The multifaceted nutritional profile of plant-based burgers highlights the need for manufacturers to implement improvements to better support healthy dietary habits. These improvements should include reducing energy, sodium and total fats

    Predictors of Early Lung Function in Patients with Congenital Diaphragmatic Hernia

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    Purpose Long-term pulmonary outcomes of congenital diaphragmatic hernia (CDH) have demonstrated airflow obstruction in later childhood. We examined pulmonary function data to assess what factors predict lung function in the first three years of life in children with CDH. Methods This was a retrospective study of patients treated for CDH who underwent infant pulmonary function testing (IPFT) between 2006 and 2012. IPFT was performed using the raised volume rapid thoracoabdominal compression technique and plethysmography. Results Twenty-nine neonates with CDH had IPFTs in the first 3 years of life. Their mean predicted survival using the CDH Study Group equation was 63% ¬Ī 4%. Fourteen infants (48%) required extracorporeal membrane oxygenation (ECMO). The mean age at IPFT was 85.1 ¬Ī 5 weeks. Airflow obstruction was the most common abnormality, seen in 14 subjects. 12 subjects had air trapping, and 9 demonstrated restrictive disease. ECMO (p = 0.002), days on the ventilator (p = 0.028), and days on oxygen (p = 0.023) were associated with restrictive lung disease. Conclusion Despite following a group of patients with severe CDH, lung function revealed mild deficits in the first three years of life. Clinical markers of increased severity (ECMO, ventilator days, and prolonged oxygen use) are correlated with reduced lung function. ¬© 2014 Elsevier Inc.http://deepblue.lib.umich.edu/bitstream/2027.42/192031/2/Predictors of early lung function in patients with congenital diaphragmatic hernia.pdfPublished versionDescription of Predictors of early lung function in patients with congenital diaphragmatic hernia.pdf : Published versio

    Reduced emergent character of neural dynamics in patients with a disrupted connectome

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    High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as ‚ÄúIntegrated Information Decomposition,‚ÄĚ which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems ‚ÄĒ including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients‚Äô structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence

    Drivers determining TB disease screening yield in four European screening programmes: a comparative analysis.

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    INTRODUCTION: The WHO End-TB Strategy emphasises screening for early diagnosis of tuberculosis (TB) in high-risk groups, including migrants. We analysed key drivers of TB yield differences in four large migrant TB screening programmes to inform TB control planning and feasibility of a European approach. METHODS: We pooled individual TB screening episode data from Italy, the Netherlands, Sweden, and the UK and analysed predictors and interactions for TB case yield using multivariable logistic regression models. RESULTS: Between 2005-2018 in 2,302,260 screening episodes among 2,107,016 migrants to four countries; the programmes identified 1,658 TB cases (yield 72.0 per 100,000; 95% confidence interval, CI68.6-75.6). In logistic regression analysis, we found associations between TB screening yield and age (>55‚ÄÖyears odds ratio, OR2.91, CI2.24-3.78), being an asylum seeker (OR3.19, CI1.03-9.83) or on a settlement visa (OR1.78, CI1.57-2.01), close TB contact (OR12.25, 11.73-12.79), and higher TB incidence in the country of origin (CoO). We demonstrated interactions between migrant typology and age, as well as CoO. For asylum seekers, the elevated TB risk remained similar above CoO incidence thresholds of 100 per 100,000. CONCLUSIONS: Key determinants of TB yield included close contact, increasing age, incidence in CoO and specific migrant groups including asylum seekers and refugees. For most migrants such as UK students and workers, TB yield significantly increased with levels of incidence in CoO. The high, CoO-independent TB risk in asylum seekers above a 100 per 100,000 threshold could reflect higher transmission and reactivation risk of migration routes; with implications for selecting populations for TB screening

    The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

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    Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (‚ąľ30 tissues √ó ‚ąľ15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics
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