Reducing the Influence of Environmental Factors on Performance of a Diffusion-Based Personal Exposure Kit.

Sensor technology has enabled the development of portable low-cost monitoring kits that might supplement many applications in conventional monitoring stations. Despite the sensitivity of electrochemical gas sensors to environmental change, they are increasingly important in monitoring polluted microenvironments. The performance of a compact diffusion-based Personal Exposure Kit (PEK) was assessed for real-time gaseous pollutant measurement (CO, O3, and NO2) under typical environmental conditions encountered in the subtropical city of Hong Kong. A dynamic baseline tracking method and a range of calibration protocols to address system performance were explored under practical scenarios to assess the performance of the PEK in reducing the impact of rapid changes in the ambient environment in personal exposure assessment applications. The results show that the accuracy and stability of the ppb level gas measurement is enhanced even in heterogeneous environments, thus avoiding the need for data post-processing with mathematical algorithms, such as multi-linear regression. This establishes the potential for use in personal exposure monitoring, which has been difficult in the past, and for reporting more accurate and reliable data in real-time to support personal exposure assessment and portable air quality monitoring applications.

Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment.

BACKGROUND: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO2) in Hong Kong using a deep learning (DL) structured model. METHODS: We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. RESULTS: Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO2. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. CONCLUSION: The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

Evaluating methods for marine fuel sulfur content using microsensor sniffing systems on ocean-going vessels.

Establishing emission control areas (ECAs) can effectively reduce air pollution from marine emissions, making efficient monitoring of the fuel sulfur content (FSC) of ocean-going vessels (OGVs) key to ECA policy enforcement. Various FSC detection approaches, including oil sample analysis, sniffing method, and optical remote sensing, have been proposed, each with its benefits and drawbacks. Among these, the sniffing method appears promising but requires further improvement in field operation protocol and data analysis processes. This study aims to develop a comprehensive methodology, including sensor calibration, field operations, and data analysis, to enhance the performance of an Unmanned Aerial Vehicle (UAV)-based Microsensor Sniffing System (MSS) for real-time FSC monitoring. Hong Kong has a cap of 0.5 % m/m FSC for OGVs, and hence Hong Kong waters served as the "real-world" monitoring location to evaluate the MSS system through land-based and sea-based measurements. Three different FSC calculation methods were employed and verified against bunker delivery note (BDN) data through blind testing. Results confirm that the MSS is effective in field settings, though it has an underestimation tendency, demonstrating an absolute error of 0.06 % m/m, 0.11 % m/m, and 0.10 % m/m for the Crest, Slope, and Area methods, respectively, compared to BDN data. However, high errors were possible with low CO2 and SO2 peak heights, and single-peak samples compared to multi-peak samples. Over 16 successful trips, the FSC of 125 valid OGVs (Mean FSC = 0.39 % m/m) exhibited a lognormal distribution pattern, with the distribution tail approaching the 0.5 % m/m regulatory cap. This investigation highlights the potential of a UAV-based MSS for monitoring and enforcing FSC regulations within ECAs, providing a systematic protocol to guide future research direction and enforcement practices.

Novel circular RNA hsa_circ_0036683 suppresses proliferation and migration by mediating the miR-4664-3p/CDK2AP2 axis in non-small cell lung cancer.

BACKGROUND: The aim of the present study was to investigate the function of novel circular RNA hsa_circ_0036683 (circ-36683) in non-small cell lung cancer (NSCLC). METHODS: RNA sequencing was used to screen out differentially expressed miRNAs. Expression levels of miR-4664-3p and circ-36683 were evaluated in lung carcinoma cells and tissues by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The effects of miR-4664-3p and circ-36683 on proliferation and migration were assessed using cell counting kit-8 (CCK-8), wound healing and transwell migration assays and xenograft experiments. The targeting relationship of circ-36683/miR-4664-3p/CDK2AP2 was assessed by luciferase reporter assays, western blot, qRT-PCR and argonaute2-RNA immunoprecipitation (AGO2 RIP). Co-immunoprecipitation (Co-IP), 5-ethynyl-2'-deoxyuridine (EdU) staining and CCK-8 were used to validate the indispensable role of CDK2AP2 in suppressing cell proliferation as a result of CDK2AP1 overexpression. RESULTS: By RNA sequencing, miR-4664-3p was screened out as an abnormally elevated miRNA in NSCLC tissues. Transfection of miR-4664-3p could promote cell proliferation, migration and xenograft tumor growth. As a target of miR-4664-3p, CDK2AP2 expression was downregulated by miR-4664-3p transfection and CDK2AP2 overexpression could abolish the proliferation promotion resulting from miR-4664-3p elevation. Circ-36683, derived from back splicing of ABHD2 pre-mRNA, was attenuated in NSCLC tissue and identified as a sponge of miR-4664-3p. The functional study revealed that circ-36683 overexpression suppressed cell proliferation, migration and resulted in G0/G1 phase arrest. More importantly, the antioncogenic function of circ-36683 was largely dependent on the miR-4664-3p/CDK2AP2 axis, through which circ-36683 could upregulate the expression of p53/p21/p27 and downregulate the expression of CDK2/cyclin E1. CONCLUSION: The present study revealed the antioncogenic role of circ-36683 in suppressing cell proliferation and migration and highlighted that targeting the circ-36683/miR-4664-3p/CDK2AP2 axis is a promising strategy for the intervention of NSCLC.

Effect of systemic lupus erythematosus on the ovarian reserve: A systematic review and meta-analysis.

OBJECTIVE: Systemic Lupus Erythematosus (SLE) is an autoimmune disease that occurs at higher rates in young women. Evidence suggests that SLE may be associated with ovarian dysfunction. Therefore, it is crucial to investigate the possible effects of SLE on ovarian reserve function. METHODS: PubMed, Embase, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov databases were searched from inception to July 2023 to identify studies that compared ovarian reserve in patients with SLE to that of healthy individuals. The study examined anti-müllerian hormone (AMH), antral follicle count (AFC), and follicle-stimulating hormone (FSH) as outcomes. RESULTS: Thirteen studies (n=1017) were eligible for meta-analysis. Females with SLE had significantly lower levels of AMH (weighted mean difference [WMD]: -1.07, 95% confidence interval [CI]: -1.37 to -0.76, P<0.001) and AFC (WMD: -3.46, 95% CI: -4.57 to -2.34, P<0.001). There was no significant difference in FSH levels. Subgroup analyses by age of onset revealed that SLE patients with adult-onset had significantly lower AMH levels (WMD: -1.44, 95% CI: -1.71 to -1.18, P<0.001), lower AFCs (WMD: -3.11, 95% CI: -3.60 to -2.61, P<0.001) and higher FSH levels (WMD: 0.60, 95% CI: 0.15 to 1.05, P<0.01). However, SLE patients with juvenile-onset did not exhibit significant differences in their AMH and FSH levels, except for AFCs (WMD: -7.27, 95% CI: -12.39 to -2.14, P<0.01). CONCLUSION: The impact of SLE on ovarian reserve is significant, and the effect may be particularly severe in cases of adult-onset SLE.

Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network.

Traffic-related air pollutants are major contributors to deteriorating urban air quality and pose a serious threat to pedestrians. From both a scientific and a regulatory standpoint, it is important and challenging to understand the contributions of local and non-local sources to accurately apportion specific sources such as traffic emissions contribution to on-road and near-road microenvironment air quality. In this study, we deployed mobile sensors on-board buses to monitor NO, NO2, CO and PM2.5 along ten important routes in Hong Kong. The measurements include two seasons: April 2017 and July 2017. Two types of baseline extraction methods were evaluated and applied to separate local and background concentrations. The results show NO and NO2 are locally dominated air pollutants in spring, constituting 72%-84% and 58%-71%, respectively, with large inter-road variation. PM2.5 and CO largely arise from background sources, which contribute 55%-65% and 73%-79% respectively. PM2.5 displays a homogeneous spatial pattern, and the contributions show seasonal change, decreasing during summer. Regional transport pollution is the primary contributor during high pollution episodes. Isolated vehicle plumes show highly skewed concentration distributions. There are characteristic polluted segments on routes and they are most evident at rush hours. The most polluted road segments (top 10%) cluster at tunnel entrances and congested points. Some of these polluted locations were observed in Hong Kong's Low Emission Zones and suggest limitations to the existing control strategies, which only address larger buses. Our work gives new insights in the importance of regional cooperation to improve background air pollution combined with local control strategies to improve roadside air quality in Hong Kong.