Harmonised ambient air pollution and road traffic noise exposures linked to cardiovascular outcomes in European cohorts

Ambient air pollution and traffic-related noise are the two leading environmental risk factors for health in Europe. Associations between long-term exposure to air pollution or noise and cardiovascular diseases (CVD) were not entirely consistent across previous studies in adults. Moreover, noise may confound the relationship between air pollution and CVD, and vice versa. This PhD project was conducted to study the separate and joint effects of both air pollution and noise on 1) CVD blood biochemistry including C-reactive protein, blood lipids and glucose and on 2) incident CVD outcomes. Health and exposures data were harmonised across four European cohorts (EPIC-Oxford, HUNT, LifeLines, UK Biobank), as part of the EU-funded BioSHaRE project. All harmonised data were virtually pooled for individual-level analyses in DataSHIELD, a novel statistical tool to perform a ‘compute to data’ statistical approach. The cross-sectional analyses on biochemistry data generally suggested that both air pollution and noise were significantly associated with adverse changes in markers of systemic inflammation, blood lipids and glucose. The significant association between road traffic noise and C-reactive protein or triglycerides was confounded by air pollution whilst both air pollution and noise were significantly and independently associated with elevated blood glucose levels. Incident analyses suggested a possible increased risk for both particulate matter (PM) and gaseous air pollution on incident cerebrovascular disease but a null association for ischaemic heart disease (IHD). Daytime noise was associated with a non-significantly increased risk for incident IHD but evidence for cerebrovascular disease was inconclusive. Both air pollution and noise effects on CVD outcomes were independent from each other. This PhD study provides some novel evidence of both air pollution and noise on CVD biochemistry and incident CVD outcomes, and is a substantial addition to the current knowledge of cardiovascular health effects of both ambient air pollution and traffic noise.Open Acces

Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

The classification of benign versus malignant lung nodules using chest CT plays a pivotal role in the early detection of lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign lung nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of lung nodules, respectively, and jointly utilizes them to classify lung nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches

When STING meets viruses: Sensing, trafficking and response

To effectively defend against microbial pathogens, the host cells mount antiviral innate immune responses by producing interferons (IFNs), and hundreds of IFN-stimulated genes (ISGs). Upon recognition of cytoplasmic viral or bacterial DNAs and abnormal endogenous DNAs, the DNA sensor cGAS synthesizes 2\u27,3\u27-cGAMP that induces STING (stimulator of interferon genes) undergoing conformational changes, cellular trafficking, and the activation of downstream factors. Therefore, STING plays a pivotal role in preventing microbial pathogen infection by sensing DNAs during pathogen invasion. This review is dedicated to the recent advances in the dynamic regulations of STING activation, intracellular trafficking, and post-translational modifications (PTMs) by the host and microbial proteins

Editorial: Epidemiology and clinical researches on neuropsychiatric disorders in aging

 With the rising aging population in a global range, related neuropsychiatric disorders such as depression and dementia, have emerged and caused a tremendous disease burden. Over the past decades, many risk factors have been identified (1–12), and advances have been made in developing prevention and intervention strategies. However, there still exist challenges to be addressed. These challenges include but are not limited to early detection and prediction of neuropsychiatric disorders, comorbidities of both neuropsychiatric and non-neuropsychiatric aspects, identifying novel indicators for disease progression and prognosis, as well as investigating potential mediating mechanisms. Facing unprecedented challenges, we launched this Research Topic to promote healthy aging and longevity from the neuropsychiatric perspective, via collaboration from a number of professional disciplines. </p

Reliability sequential compliance method for a partially observable gear system subject to vibration monitoring

Assumptions accompanying exponential failure models are often not met in the standard sequential probability ratio test (SPRT) of many products. However, for most of the mechanical products, Weibull distribution conforms to their life distributions better compared to other techniques. The SPRT method for Weibull life distribution is derived in this paper, which enables the implementation of reliability compliance tests for gearboxes. Using historical failure data and condition monitoring data, a life prediction model based on hidden Markov model (HMM) is established to describe the deterioration process of gearboxes, then the predicted remaining useful life (RUL) is transformed into failure data that is used in SPRT for further analysis, which can significantly save on testing time and reduce costs. Explicit expression for the distribution of RUL is derived in terms of the posterior probability that the system is in the unhealthy state. The predicted and actual values of the residual life are compared, and the average relative error is 3.90 %, which verifies the validity of the proposed residual life prediction approach. A comparison with other life prediction and SPRT methods is given to elucidate the efficacy of the proposed approach

Restauro, 1-3/1992.

Ambient air pollution increases the risk of respiratory mortality, but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.We used UK Biobank data on 303 887 individuals aged 40-69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of land use regression-based estimates of particulate matter (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM; 2.5; and PM; 10; , respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PM; coarse; ) and nitrogen dioxide (NO; 2; ) concentrations with forced expiratory volume in 1 s (FEV; 1; ), forced vital capacity (FVC), the FEV; 1; /FVC ratio and COPD (FEV; 1; /FVC &lt;lower limit of normal). Effect modification was investigated for sex, age, obesity, smoking status, household income, asthma status and occupations previously linked to COPD.Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m; -3; increase in PM; 2.5; concentration was associated with lower FEV; 1; (-83.13 mL, 95% CI -92.50- -73.75 mL) and FVC (-62.62 mL, 95% CI -73.91- -51.32 mL). COPD prevalence was associated with higher concentrations of PM; 2.5; (OR 1.52, 95% CI 1.42-1.62, per 5 µg·m; -3; ), PM; 10; (OR 1.08, 95% CI 1.00-1.16, per 5 µg·m; -3; ) and NO; 2; (OR 1.12, 95% CI 1.10-1.14, per 10 µg·m; -3; ), but not with PM; coarse; Stronger lung function associations were seen for males, individuals from lower income households, and "at-risk" occupations, and higher COPD associations were seen for obese, lower income, and non-asthmatic participants.Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study

Inflation and Dark Energy from spectroscopy at z > 2

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at z < 1, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at 2 < z < 5, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports