Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.

Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.

Longitudinal Lung Function Assessment of Patients Hospitalized With COVID-19 Using 1H and 129Xe Lung MRI.

BACKGROUND: Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear. RESEARCH QUESTION: Do patients hospitalized with COVID-19 without evidence of architectural distortion on structural imaging exhibit longitudinal improvements in lung function measured by using 1H and 129Xe MRI between 6 and 52 weeks following hospitalization? STUDY DESIGN AND METHODS: Patients who were hospitalized with COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25, and 51 weeks following hospital admission in a prospective cohort study between November 2020 and February 2022. The imaging protocol was as follows: 1H ultra-short echo time, contrast-enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion-weighted, and 129Xe spectroscopic imaging of gas exchange. RESULTS: Nine patients were recruited (age 57 ± 14 [median ± interquartile range] years; six of nine patients were male). Patients underwent MRI at 6 (n = 9), 12 (n = 9), 25 (n = 6), and 51 (n = 8) weeks following hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients exhibited impaired 129Xe gas transfer (RBC to membrane fraction), but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6- to 25-week period. At 12 weeks, all patients with lung perfusion data (n = 6) showed an increase in both pulmonary blood volume and flow compared with 6 weeks, although this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared with 6-week examinations; however, 129Xe gas transfer remained abnormally low at weeks 12, 25, and 51. INTERPRETATION: 129Xe gas transfer was impaired up to 1 year following hospitalization in patients who were hospitalized with COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation was normal at 52 weeks.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. PURPOSE: Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center. STUDY TYPE: Retrospective. POPULATION: A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI. ASSESSMENT: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. STATISTICAL TESTS: Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. RESULTS: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data. DATA CONCLUSION: The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Global geological occurrence and character of the carcinogenic zeolite mineral, erionite: A review.

As with the six regulated asbestos minerals (chrysotile, amosite, crocidolite, anthophyllite, tremolite, and actinolite), the zeolite mineral, erionite, can exhibit a fibrous morphology. When fibrous erionite is aerosolized and inhaled, it has been linked to cases of lung cancers, such as malignant mesothelioma. Importantly, fibrous erionite appears to be more carcinogenic than the six regulated asbestos minerals. The first health issues regarding erionite exposure were reported in Cappadocia (Turkey), and more recently, occupational exposure issues have emerged in the United States. Erionite is now classified as a Group 1 carcinogen. Thus, identifying the geological occurrence of erionite is a prudent step in determining possible exposure pathways, but a global review of the geological occurrence of erionite is currently lacking. Here, we provide a review of the >100 global locations where erionite has been reported, including: 1) geological setting of host rocks; 2) paragenetic sequence of erionite formation, including associated zeolite minerals; 3) fiber morphological properties and erionite mineral series (i.e., Ca, K, Na); and 4) a brief overview of the techniques that have been used to identify and characterize erionite. Accordingly, erionite has been found to commonly occur within two major rock types: felsic and mafic. Within felsic rocks (in particular, tuffaceous layers within lacustrine paleoenvironments), erionite is disseminated through the layer as a cementing matrix. In contrast, within mafic (i.e., basaltic) rocks, erionite is typically found within vesicles. Nevertheless, aside from detailed studies in Italy and the United States, there is a paucity of specific information on erionite geological provenance or fiber morphology. The latter issue is a significant drawback given its impact on erionite toxicity. Future erionite studies should aim to provide more detailed information, including variables such as rock type and lithological properties, quantitative geochemistry, and fiber morphology.

Reanalysis of historic elemental speciation filters to investigate the presence of fibrous mineral particles using microscopy techniques.

A case is presented for the value of archiving air quality filters to allow for retrospective analysis of emerging contaminants, that is filter constituents not considered to be harmful (and thus not identified or quantified specifically) at the time of collection but subsequently considered to be of interest. As an example, filters from a 20-year historical archive consisting of 16,000 filters from three sites across Auckland are re-examined for the presence of elongated mineral fibres known to be present in rock across the city. Originally collected for the purpose of the source apportionment of particulate matter, 10 filters from each of the three sites were chosen for reanalysis based on their high silica and aluminium content, and thus considered more likely to contain fibre-like particles (FLP). These filters were analysed using various microscopic methods, including phase contrast microscopy (PCM), scanning electron microscopy (SEM) and energy-dispersive x-ray spectroscopy (EDS). The results show that although the commonly used fibrous polytetrafluoroethylene (PTFE) material of the filters may hamper the visual identification of any fibre-like particles under a certain length, their key components are able to be identified using a combination of PCM and SEM when they are of a suitable dimension and have settled in a certain orientation on the filter. In this case, the use of EDS confirmed the silicon content of the fibres and also revealed elemental spectra. Although the exact identification of the mineral fibre is uncertain, the EDS scan is consistent with hazardous zeolites such as erionite, known to be present in the rock found in Auckland. This study highlights the value in maintaining filter archives for the purpose of investigating the historical evolution of emerging atmospheric pollutants.

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.

Aims: Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods and results: Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality. Conclusion: The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.

Erionite in Auckland bedrock and malignant mesothelioma: an emerging public and occupational health hazard?

Overseas, emerging research has shown that where erionite is present in bedrock as a zeolite, and then subsequently disturbed and blown into the atmosphere, resulting exposure is associated with health effects similar to those caused by asbestos, including malignant mesothelioma (MM). Erionite-induced MM is thought to be particularly prevalent in the construction and quarrying industries, in regions where rock containing erionite is disturbed. In 2015, the then Government Chief Scientist, Sir Peter Gluckman, reported that erionite was a more potent carcinogen than asbestos, and more recent studies have established its presence in the Auckland Region. However, globally at present, there are no established occupational exposure limits for erionite, standard sampling and analytical methods or exposure mitigation guidelines. Given the many major construction projects being carried out in Auckland at the present time, which involve the removal of large quantities of bedrock containing erionite, an assessment of the health risks such activities pose to the public is needed.

'Munchausen's syndrome by proxy' or a 'miscarriage of justice'? An initial application of functional neuroimaging to the question of guilt versus innocence.

'Munchausen's syndrome by proxy' characteristically describes women alleged to have fabricated or induced illnesses in children under their care, purportedly to attract attention. Where conclusive evidence exists the condition's aetiology remains speculative, where such evidence is lacking diagnosis hinges upon denial of wrong-doing (conduct also compatible with innocence). How might investigators obtain objective evidence of guilt or innocence? Here, we examine the case of a woman convicted of poisoning a child. She served a prison sentence but continues to profess her innocence. Using a modified fMRI protocol (previously published in 2001) we scanned the subject while she affirmed her account of events and that of her accusers. We hypothesized that she would exhibit longer response times in association with greater activation of ventrolateral prefrontal and anterior cingulate cortices when endorsing those statements she believed to be false (i.e., when she 'lied'). The subject was scanned 4 times at 3 Tesla. Results revealed significantly longer response times and relatively greater activation of ventrolateral prefrontal and anterior cingulate cortices when she endorsed her accusers' version of events. Hence, while we have not 'proven' that this subject is innocent, we demonstrate that her behavioural and functional anatomical parameters behave as if she were.