Endoscopic ultrasound-based application system for predicting endoscopic resection-related outcomes and diagnosing subepithelial lesions: Multicenter prospective study.

OBJECTIVES: Subepithelial lesions (SELs) are associated with various endoscopic resection (ER) outcomes and diagnostic challenges. We aimed to establish a tool for predicting ER-related outcomes and diagnosing SELs and to investigate the predictive value of endoscopic ultrasound (EUS). METHODS: Phase 1 (system development) was performed in a retrospective cohort (n = 837) who underwent EUS before ER for SELs at eight hospitals. Prediction models for five key outcomes were developed using logistic regression. Models with satisfactory internal validation performance were included in a mobile application system, SEL endoscopic resection predictor (SELERP). In Phase 2, the models were externally validated in a prospective cohort of 200 patients. RESULTS: An SELERP was developed using EUS characteristics, which included 10 models for five key outcomes: post-ER ulcer management, short procedure time, long hospital stay, high medication costs, and diagnosis of SELs. In Phase 1, 10 models were derived and validated (C-statistics, 0.67-0.99; calibration-in-the-large, -0.14-0.10; calibration slopes, 0.92-1.08). In Phase 2, the derived risk prediction models showed convincing discrimination (C-statistics, 0.64-0.73) and calibration (calibration-in-the-large, -0.02-0.05; calibration slopes, 1.01-1.09) in the prospective cohort. The sensitivities and specificities of the five diagnostic models were 68.3-95.7% and 64.1-83.3%, respectively. CONCLUSION: We developed and prospectively validated an application system for the prediction of ER outcomes and diagnosis of SELs, which could aid clinical decision-making and facilitate patient-physician consultation. EUS features significantly contributed to the prediction. TRIAL REGISTRATION: Chinese Clinical Trial Registry, http://www.chictr.org.cn (ChiCTR2000040118).

Diurnal and weekly patterns of primary pollutants in Beijing under COVID-19 restrictions.

Restrictions on movement in Beijing to limit the COVID-19 epidemic tended to reduce the emissions of primary pollutants. However, changes in pollutant concentrations are also affected by chemical transformation and meteorology. Decreases in concentrations were also not as obvious in Beijing when compared with Hubei Province, where lockdown was especially strict. Declines in concentrations between 2019 and 2020 are evident for both NO2 (37.0 to 26.2 µg m-3) and SO2 (5.86 to 4.15 µg m-3), but there was little evidence of change for PM2.5 (50.0 to 53.8 µg m-3) and CO (0.71 to 0.74 mg m-3). Despite this, Fourier analysis revealed that the weekly cycle of PM2.5 evident in 2019 was not apparent in 2020. In 2019, CO showed both diurnal and weekly signals, but these were absent under the restrictions of 2020. This suggests that while concentrations may remain relatively constant, the temporal distribution of pollutants can show subtle changes under restrictions imposed in attempts to limit the spread of the coronavirus.

Profiling Airborne Microbiota in Mechanically Ventilated Buildings Across Seasons in Hong Kong Reveals Higher Metabolic Activity in Low-Abundance Bacteria.

Metabolically active bacteria within built environments are poorly understood. This study aims to investigate the active airborne bacterial microbiota and compare the total and active microbiota in eight mechanically ventilated buildings over four consecutive seasons using the 16S rRNA gene (rDNA) and the 16S rRNA (rRNA), respectively. The relative abundances of the taxa of presumptive occupants and environmental origins were significantly different between the active and total microbiota. The Sloan neutral model suggested that ecological drift and random dispersal played a smaller role in the assembly of the active microbiota than the total microbiota. The seasonal nature of the active microbiota was consistent with that of the total microbiota in both indoor and outdoor environments, while only the indoor environment was significantly affected by geography. The relative abundances of the active and total taxa were positively correlated, suggesting that the high-abundance members were also the greatest contributors to the community-level metabolic activity. Based on the rRNA/rDNA ratio, the low-abundance members consistently had a higher taxon-level metabolic activity than the high-abundance members over seasons, suggesting that the low-abundance members may have the ability to survive and thrive in the indoor environment and their impact on the health of occupants cannot be overlooked.

Airborne Bacteria in Outdoor Air and Air of Mechanically Ventilated Buildings at City Scale in Hong Kong across Seasons.

Studies of the indoor airborne microbiome have mostly been confined to a single location and time point. Here, we characterized, over the course of a year, the geographic variation, building-function dependence, and dispersal characteristics of indoor and outdoor airborne microbiomes (bacterial members only) of eight mechanically ventilated commercial buildings. Based on the Sloan neutral model, airborne microbiomes were randomly dispersed in the respective indoor and outdoor environments and between the two environments during each season. The dominant taxa in the indoor and outdoor environments showed minor variations at each location among seasons. The airborne microbiomes displayed weak seasonality for both indoor and outdoor environments, while a weak geographic variation was found only for the indoor environments. Source tracking results show that outdoor air and occupant skin were major contributors to the indoor airborne microbiomes, but the extent of the contribution from each source varied within and among buildings over the seasons, which suggests variations in local building use. Based on 32 cases of indoor airborne microbiome data, we determined that the indoor/outdoor (I/O) ratio of PM2.5 was not a robust indicator of the sources found indoors. Alternatively, the indoor concentration of carbon dioxide was more closely correlated with the major sources of the indoor airborne microbiome in mechanically ventilated environments.

Regulatory effects on particulate pollution in the early hours of Chinese New Year, 2015.

Human activities are a key driver of air pollution, so it is hardly surprising that celebrations affect air quality. The use of fireworks contributes to high particulate concentrations in many parts of the world, with the Chinese Lunar New Year (spring festival) particularly noticeable, as firecrackers are traditionally used to drive off evil spirits. Fireworks lead to short-term peaks in the concentration of PM10, PM2.5 and SO2. Regulatory actions that restrict the use of fireworks have been evident in China since the 1990s. This paper investigates the particulate concentrations in nine Chinese cities (Beijing, Chengdu, Chongqing, Tianjin, Xi'an, Nanjing, Shanghai, Guangzhou and Shenzhen, along with Hong Kong (a Special Administrative Region) and Taipei and Kaohsiung (Taiwan) with a particular focus on the celebrations of 2015. Extremely high concentrations of particulate matter were observed, with some sites revealing peak PM10 concentrations in excess of 1000 µg m-3 in the early hours of the New Year. In Beijing, Tianjin and Chongqing, the activities caused high particulate matter concentrations at most sites throughout the city. These peaks in particulate load in the early hours of Chinese New Year do not appear to be closely related to meteorological parameters. However, in cities where fireworks appear to be better regulated, there are fewer sharp pollution peaks just after midnight, although lowered air quality can still be found in the outer parts of some cities, remote from regulatory pressures. A few cities seem to have been effective at reducing the impact of the celebrations on air quality, with Nanjing a recent example. An increasing focus on light displays and electric lanterns also seems to offer a sense of celebration with much reduced impacts on air quality.

Optimal method for determining the intraclass correlation coefficients of urinary biomarkers such as dialkylphosphates from imputed data.

Urinary biomarkers are commonly used in epidemiological studies as surrogates or indicators of exposure to chemical substances. Evaluating the reliability of a biomarker is highly important because use of an unreliable marker may lead to misclassification and attenuation bias, resulting in flawed interpretations and conclusions. Although intraclass correlation coefficient (ICC) is regarded as a typical index of test reliability, methods for determining the ICCs of urinary biomarkers have not been standardised, and different methods have been used. This study evaluated different imputation methods for left-censored data, i.e., four imputation or one substitution methods, before calculating ICCs, and at the same time mathematically assessed the impact of the left-censoring proportion on the estimated ICCs. Biomarkers of exposure to organophosphate pesticides, i.e., dialkylphosphates, were used as an example. The Gibbs sampler-based left-censored missing value imputation approach had the best performance for imputation of values below reporting limits, with lower values on Kolmogorov-Smirnov test statistics than other imputation/substitution methods, i.e., a univariate distribution fitting approach, multiple imputation by chained equation, a bootstrap expectation-maximisation algorithm approach, and a single value substitution. In all imputation methods, however, ICCs decreased as censoring rates increased. We propose a method to estimate true ICCs based on mathematical estimation.