Dual-Channel Prototype Network for Few-Shot Pathology Image Classification.

In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning has demonstrated unique advantages in modeling tasks with limited data, yet explorations of this method in the field of pathology remain in the early stages. To address this issue, we present a dual-channel prototype network (DCPN), a novel few-shot learning approach for efficiently classifying pathology images with limited data. The DCPN leverages self-supervised learning to extend the pyramid vision transformer (PVT) to few-shot classification tasks and combines it with a convolutional neural network to construct a dual-channel network for extracting multi-scale, high-precision pathological features, thereby substantially enhancing the generalizability of prototype representations. Additionally, we design a soft voting classifier based on multi-scale features to further augment the discriminative power of the model in complex pathology image classification tasks. We constructed three few-shot classification tasks with varying degrees of domain shift using three publicly available pathological datasets-CRCTP, NCTCRC, and LC25000-to emulate real-world clinical scenarios. The results demonstrated that the DCPN outperformed the prototypical network across all metrics, achieving the highest accuracies in same-domain tasks-70.86% for 1-shot, 82.57% for 5-shot, and 85.2% for 10-shot setups-corresponding to improvements of 5.51%, 5.72%, and 6.81%, respectively, over the prototypical network. Notably, in the same-domain 10-shot setting, the accuracy of the DCPN (85.2%) surpassed that of the PVT-based supervised learning model (85.15%), confirming its potential to diagnose rare diseases within few-shot learning frameworks.

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

Obesity-related cardiometabolic indicators modify the associations of personal noise exposure with heart rate variability: A further investigation on the Study among Obese and Normal-weight Adults (SONA).

Elucidating the associations between environmental noise and heart rate variability (HRV) would be beneficial for the prevention and control of detrimental cardiovascular changes. Obese people have been found to manifest heightened susceptibility to the adverse effects of noise on HRV. However, the underlying mechanisms remain unclear. Based on 53 normal-weight and 44 obese young adults aged 18-26 years in Beijing, China, this study aimed to investigate the role of obesity-related cardiometabolic indicators for associations between short-term environmental noise exposure and HRV in the real-world context. The participants underwent personal noise exposure and ambulatory electrocardiogram monitoring using portable devices at 5-min intervals for 24 continuous hours. Obesity-related blood pressure, glucose and lipid metabolism, and inflammatory indicators were subsequently examined. Generalized mixed-effect models were used to estimate the associations between noise exposure and HRV parameters. The C-peptide, homeostasis model assessment of insulin resistance (HOMA-IR), and leptin levels were higher in obese participants compared to normal-weight participants. We observed amplified associations between short-term noise exposure and decreases in HRV among participants with higher C-peptide, HOMA-IR, and leptin levels. For instance, a 1 dB(A) increment in 3 h-average noise exposure level preceding each measurement was associated with changes of -0.20% (95%CI: -0.45%, 0.04%) and -1.35% (95%CI: -1.85%, -0.86%) in standard deviation of all normal to normal intervals (SDNN) among participants with lower and higher C-peptide levels, respectively (P for interaction <0.05). Meanwhile, co-existing fine particulate matter (PM2.5) could amplify the associations between noise and HRV among obese participants and participants with higher C-peptide, HOMA-IR, and leptin levels. The more apparent associations of short-term exposure to environmental noise with HRV and the effect modification by PM2.5 may be partially explained by the higher C-peptide, HOMA-IR, and leptin levels of obese people.

scDFC: A deep fusion clustering method for single-cell RNA-seq data.

Clustering methods have been widely used in single-cell RNA-seq data for investigating tumor heterogeneity. Since traditional clustering methods fail to capture the high-dimension methods, deep clustering methods have drawn increasing attention these years due to their promising strengths on the task. However, existing methods consider either the attribute information of each cell or the structure information between different cells. In other words, they cannot sufficiently make use of all of this information simultaneously. To this end, we propose a novel single-cell deep fusion clustering model, which contains two modules, i.e. an attributed feature clustering module and a structure-attention feature clustering module. More concretely, two elegantly designed autoencoders are built to handle both features regardless of their data types. Experiments have demonstrated the validity of the proposed approach, showing that it is efficient to fuse attributes, structure, and attention information on single-cell RNA-seq data. This work will be further beneficial for investigating cell subpopulations and tumor microenvironment. The Python implementation of our work is now freely available at https://github.com/DayuHuu/scDFC.

Joint effect of multiple air pollutants on cardiometabolic health in normal-weight and obese adults: A novel insight into the role of circulating free fatty acids.

The cardiometabolic effects of air pollution in the context of mixtures and the underlying mechanisms remain not fully understood. This study aims to investigate the joint effect of air pollutant mixtures on a broad range of cardiometabolic parameters, examine the susceptibility of obese individuals, and determine the role of circulating fatty acids. In this panel study, metabolically healthy normal-weight (MH-NW, n = 49) and obese (MHO, n = 39) adults completed three longitudinal visits (257 person-visits in total). Personal exposure levels of PM2.5, PM10, O3, NO2, SO2, CO and BC were estimated based on fixed-site monitoring data, time-activity logs and infiltration factor method. Blood pressure, glycemic homeostasis, lipid profiles, systematic inflammation and coagulation biomarkers were measured. Targeted metabolomics was used to quantify twenty-eight plasma free fatty acids (FFAs). Bayesian kernel machine regression models were applied to establish the exposure-response relationships and identify key pollutants. Significant joint effects of measured air pollutants on systematic inflammation and coagulation biomarkers were observed in the MHO group, instead of the MH-NW group. Lipid profiles showed the most significant changes in both groups and O3 contributed the most to the total effect. Specific FFA patterns were identified, and de novo lipogenesis (DNL)-related pattern was most closely related to blood lipid profiles. In particular, interaction analysis suggested that DNL-related FFA pattern augmented the effects of O3 on triglyceride (TG, Pinteraction = 0.040), high-density lipoprotein cholesterol (HDL-C, Pinteraction = 0.106) and TG/HDL-C (Pinteraction = 0.020) in the MHO group but not MH-NW group. This modification was further confirmed by interaction analysis with estimated activity of SCD1, a key enzyme in the DNL pathway. Therefore, despite being metabolically healthy, obese subjects have a higher cardiometabolic susceptibility to air pollution, especially O3, and the DNL pathway may represent an intrinsic driver of lipid susceptibility. This study provides new insights into the cardiometabolic susceptibility of obese individuals to air pollution.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Associations between personal noise exposure and heart rate variability were modified by obesity and PM2.5: The study among obese and normal-weight adults (SONA).

Noise pollution has been documented to increase the risks of cardiovascular disorders, which can be predicted by heart rate variability (HRV), nevertheless, there has been limited evidence on the modifiers of noise pollution. Environmental fine particulate matter (PM2.5) and obesity status are both growing major concerns of cardiovascular disease burden. Our study aims to investigate whether these two factors may modify the associations between noise exposure and HRV indices. An investigation was performed on 97 (53 normal-weight and 44 obese) participants aged 18-26 years, with continuous 5-min personal exposure assessment and ambulatory electrocardiogram monitoring for 24 h. This study found that personal exposure to noise was associated with decreased HRV level and imbalanced cardiac autonomic function, as indicated by decreases in standard deviation of normal-to-normal intervals (SDNN), square root of the mean squared differences of successive intervals (rMSSD), the percentage of R-R intervals that differ from each other by more than 50 ms (pNN50), low-frequency (LF) power, high-frequency (HF) power, and increases in LF-HF-Ratio. Stronger associations between personal noise exposure and HRV indices were observed among obese participants and participants with higher PM2.5 exposure levels compared to their counterparts. For SDNN, a 1 dB(A) increment in personal noise exposure at 3h-average was associated with a 1.25% (95%CI: -1.64%, -0.86%) decrease among obese participants, and a 0.11% (95%CI: -0.38%, 0.16%) decrease among normal-weight participants (P for subgroup difference<0.001); and a 0.87% (95%CI: -1.20%, -0.54%) decrease among participants with higher PM2.5 exposure levels, and a 0.22% (95%CI: -0.58%, 0.14%) decrease among participants with lower PM2.5 exposure levels (P for subgroup difference = 0.008). Obesity and PM2.5 may aggravate the adverse effects of noise on HRV, which has implications for targeted prevention of cardiovascular disease burden associated with noise pollution.

Short-term ozone exposure and metabolic status in metabolically healthy obese and normal-weight young adults: A viewpoint of inflammatory pathways.

Unhealthy metabolic status increases risks of cardiovascular and other diseases. This study aims to explore whether there is a link between O3 and metabolic health indicators through a viewpoint of inflammatory pathways. 49 metabolically healthy normal-weight (MH-NW) and 39 metabolically healthy obese (MHO) young adults aged 18-26 years were recruited from a panel study with three visits. O3 exposure were estimated based on fixed-site environmental monitoring data and time-activity diary for each participant. Compared to MH-NW people, MHO people were more susceptible to the adverse effects on metabolic status, including blood pressure, glucose, and lipid indicators when exposed to O3. For instance, O3 exposure was associated with significant decreases in high-density lipoprotein cholesterol (HDL-C), and increases in C-peptide and low-density lipoprotein cholesterol (LDL-C) among MHO people, while only weaker changes in HDL-C and LDL-C among MH-NW people. Mediation analyses indicated that leptin mediated the metabolic health effects in both groups, while eosinophils and MCP-1 were also important mediating factors for the MHO people. Although both with a metabolically healthy status, compared to normal-weight people, obese people might be more susceptible to the negative effects of O3 on metabolic status, possibly through inflammatory indicators such as leptin, eosinophils, and MCP-1.

Joint effect of multiple air pollutants on lipid profiles in obese and normal-weight young adults: The key role of ozone.

Dyslipidemia may be a potential mechanism linking air pollution to adverse cardiovascular outcomes and this may differ among obese and normal-weight populations. However, the joint effect of multiple air pollutants on lipid profiles and the role of each pollutant are still unclear. This panel study aims to investigate and compare the overall associations of major air pollutants with lipid parameters in obese and normal-weight adults, and assess the relative importance of each pollutant for lipid parameters. Forty-four obese and 53 normal-weight young adults were recruited from December 2017 to June 2018 in Beijing, China. Their fasting blood was collected and serum lipid levels were measured in three visits. Six major air pollutants were included in this study, which were PM2.5, PM10, NO2, SO2, O3 and CO. Bayesian kernel machine regression (BKMR) was implemented to estimate the joint effect of the six air pollutants on various lipid parameters. We found that decreased high-density lipoprotein cholesterol (HDL-C) in the obese group and increased low-density lipoprotein cholesterol (LDL-C) and non-HDL-C in the normal-weight group were associated with the exposure to the mixture of six air pollutants above. Significant increases in total cholesterol (TC)/HDL-C and non-HDL-C/HDL-C were observed in both groups, and the effect was stronger in obese group. Of the six air pollutants above, O3 had the largest posterior inclusion probability in above lipid indices, ranging from 0.75 to 1.00. In the obese group, approximately linear exposure-response relationships were observed over the whole range of logarithmic O3-8 h max concentration, while in the normal-weight group, these relationships existed when the logarithmic concentration exceeded about 2.8. Therefore, lipid profiles of obese adults may be more sensitive to air pollution and this study highlights the importance of strengthening emissions control efforts for O3 in the future.

Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults.

BACKGROUND: The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. METHODS: A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5-24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects' residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. RESULTS: The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: - 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p <  0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. CONCLUSIONS: Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied.

Exposure to fine particulate matter promotes platelet activation and thrombosis via obesity-related inflammation.

Short-term exposure to fine particulate matter (PM2.5) increases thrombotic risk particularly in obese individuals, but the underlying mechanisms remain unclear. This study aims to compare the effects of PM2.5 on inflammation and platelet activation in obese versus normal-weight adults, and investigate potential causal pathways. We conducted a panel study measuring blood markers in 44 obese and 53 normal-weight adults on 3 separate occasions in 2017-2018. Associations between PM2.5/black carbon (BC) and biomarkers were estimated using mixed-effect models. An interaction analysis compared PM2.5/BC-related effects between subgroups. Biomarker combinations and mediation analysis were performed to elucidate the biological pathways. There was a significant "low-high-low" trend of PM2.5 levels across the 3 study periods. Increases in pro-inflammatory cytokines and changes of platelet activation and aggregation markers were associated with PM2.5/BC in obese subgroup only. Among obese subjects, the combination of pro-inflammatory cytokines and that of platelet markers increased 26.8% (95% CI: 16.0%, 37.9%) and 14.7% (95% CI: 1.9%, 27.0%) per IQR increase in PM2.5 over 5-day and 7-day averages. Inflammation mediated 24.5% of the pathways through which PM2.5 promoted platelet activation. This study suggested obese people are susceptible to pro-thrombotic impacts of PM2.5 exposures. PM2.5 may aggravate thrombosis through obesity-related inflammation.

Detecting urine metabolites of bladder cancer by surface-enhanced Raman spectroscopy.

AIM: Metabolites present in urine reflect the current phenotype of the cancer state. Surface-enhanced Raman spectroscopy (SERS) can be used in urine supernatant or sediment to largely reflect the metabolic status of the body. MATERIALS & METHODS: SERS was performed to detect bladder cancer (BCa) and predict tumour grade from urine supernatant, which contains various system metabolites, as well as from urine sediment, which contains exfoliated tumour cells. RESULTS & DISCUSSION: Upon combining the urinary supernatant and sediment results, the total diagnostic sensitivity and specificity of SERS were 100% and 98.85%, respectively, for high-grade tumours and 97.53% and 90.80%, respectively, for low-grade tumours. CONCLUSION: The present results suggest high potential for SERS to detect BCa from urine, especially when combining both urinary supernatant and sediment results.

Sorting Technology for Circulating Tumor Cells Based on Microfluidics.

Circulating tumor cells (CTCs) carry reliable clinical information for the diagnosis and treatment of cancer that is a malignant disease with a high mortality rate. However, the amount of CTCs in the blood is quite low. To obtain credible clinical information, an efficient method of extracting CTCs is necessary. Microfluidic technology has proven its effectiveness on CTCs separation in recent years. Here, we present a comprehensive review of CTC sorting methods based on microfluidics. Specifically, we introduce four different microfluidic sorting methods of CTCs and compare their advantages and disadvantages. Finally, we summarize the analysis of CTCs based on microfluidics and present a prospective view of future research.

The relationship between personal exposure and ambient PM2.5 and black carbon in Beijing.

This study is part of the "Air Polluion Impacts on Cardiopulmonary disease in Beijing: an integrated study of Exposure Science, Toxicologenomics & Environmental Epidemiology (APIC-ESTEE)" project under the UK-China joint research programme "Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China)". The aim is to capture the spatio-temporal variability in people's exposure to fine particles (PM2.5) and black carbon (BC) air pollution in Beijing, China. A total of 120 students were recruited for a panel study from ten universities in Haidian District in northwestern Beijing from December 2017 to June 2018. Real-time personal concentrations of PM2.5 and BC were measured over a 24-h period with two research-grade portable personal exposure monitors. Personal microenvironments (MEs) were determined by applying an algorithm to the handheld GPS unit data. On average, the participants spent the most time indoors (79% in Residence and 16% in Workplace), and much less time travelling by Walking, Cycling, Bus and Metro. Similar patterns were observed across participant gender and body-mass index classifications. The participants were exposed to 33.8 ± 27.8 µg m-3 PM2.5 and to 1.9 ± 1.2 µg m-3 BC over the 24-h monitoring period, on average 24.3 µg m-3 (42%) and 0.8 µg m-3 (28%) lower, respectively, than the concurrent fixed-site ambient measurements. Relative differences between personal and ambient BC concentrations showed greater variability across the MEs, highlighting significant contributions from Dining and travelling by Bus, which involve potential combustion of fuels. This study demonstrates the potential value of personal exposure monitoring in investigating air pollution related health effects, and in evaluating the effectiveness of pollution control and intervention measures.

Identification of potential markers for internal exposure to ambient ozone in oral cavity of healthy adults.

BACKGROUND: Ozone is a highly oxidative gaseous pollutant associated with adverse health outcomes, but markers for internal exposure to ambient ozone are not well-established. METHODS: We aimed to evaluate the feasibility and suitability of the markers in oral microbiome for ambient ozone exposure. Between March and May in 2018, 97 healthy adults were examined on 2 or 3 occasions for oral swab sampling. Hourly concentrations of ambient ozone 1-7 days preceding sampling were collected. Mixed-effect models were fitted to examine the associations between ambient ozone and the diversity and taxon abundances of oral microbiome. Receiver operating characteristic (ROC) curves estimated the accuracies of markers to delineate between samples exposed to different concentrations of ambient ozone. The associations between the makers and lung function were further examined by linear mixed effect models. RESULTS: The averages of daily mean concentrations of ambient ozone (O3-daily), maximum 8-h means (O3-8hmax) and 1-h maximums (O3-1hmax) were respectively 72 µg/m³, 123 µg/m³ and 144 µg/m³. O3-daily was positively associated with α-diversity of oral microbiome, but the exposure-response curves only yielded positive associations in the range of O3-daily from 60 µg/m³ to 75 µg/m³. Results of O3-8hmax and O3-1hmax were consistent with these of O3-daily. With an interquartile range increase in O3-daily at lag04, the abundance of Proteobacteria decreased by 3.1% (95% CI: -4.0%, -2.2%) and Firmicutes increased by 3.3% (95% CI: 2.3%, 4.3%), whilst the Proteobacteria:Firmicutes ratio (P/F) decreased by 0.9 (95% CI: -1.5, -0.4). The areas under ROC curves for Proteobacteria, Firmicutes and P/F were 0.8535, 0.7569 and 0.8929, respectively. Proteobacteria and P/F were associated with forced expiratory volume in the first second and fractional exhaled nitric oxide significantly. CONCLUSION: Ambient ozone disturbs oral microbial homeostasis. Proteobacteria, Firmicutes and their ratio may be potential markers for short-term ambient ozone exposure, and indicators of airway inflammation or lung function decline.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy.

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

A surface-enhanced Raman scattering-based probe method for detecting chromogranin A in adrenal tumors.

Aim: We aim to demonstrate that a surface-enhanced Raman spectroscopy (SERS) probe can be effectively used for protein detection in adrenal tumors. Materials & methods: The SERS probe method, which uses Au@Ag core-shell nanoparticles conjugated with a CgA antibody and a SERS reporter, was applied to detect CgA in adrenal tumors. Results: Our data reveal that the results of the CgA-SERS probe method were almost identical to those of western blot and superior to those of traditional immunohistochemistry. Conclusion: This study offers a novel strategy to detect CgA in adrenal tumors and provides more reliable protein test results than traditional immunohistochemistry analysis for adrenal pathologists, meaning that it might be a better clinical reference for the diagnosis of pheochromocytoma.

Urine metabolites associated with cardiovascular effects from exposure of size-fractioned particulate matter in a subway environment: A randomized crossover study.

BACKGROUND: Ambient particulate matter (PM) is closely associated with morbidity and mortality from cardiovascular disease. Urine metabolites can be used as a non-invasive means to explore biological mechanisms for such associations, yet has not been performed in relation to different sizes of PM. In this randomized crossover study, we used metabolomics approach to explore the urine biomarkers linked with cardiovascular effects after PM exposure in a subway environment. METHODS AND RESULTS: Thirty-nine subjects were exposed to PM for 4 h in subway system, with either a respirator intervention phase (RIP) with facemask and no intervention phase (NIP) in random order with a 2-week washout period. Electrocardiogram (ECG) parameters and ambulatory blood pressure (BP) were monitored during the whole riding period and urine samples were collected for metabolomics analysis. After exposure to PM for 4 h in subway system, 4 urine metabolites in male and 7 urine metabolites in female were screened out by UPLC/Q-TOF MS/MS-based metabolomics approach. Cardiovascular parameters (HRV and HR) predominantly decreased in response to all size-fractions of PM and were more sensitive in response to different size-fractioned PM in males than females. Besides LF/HF, most of the HRV indices decrease induced by the increase of all size-fractioned PM while PM1.0 was found as the most influential one on indicators of cardiovascular effects and urine metabolites both genders. Prolyl-arginine and 8-OHdG were found to have opposing role regards to HRV and HR in male. CONCLUSION: Our data indicated that short-term exposure to PM in a subway environment may increase the risk of cardiovascular disease as well as affect urine metabolites in a size dependent manner (besides PM0.5), and male were more prone to trigger the cardiovascular events than female after exposure to PM; whereas wearing facemask could effectively reduce the adverse effects caused by PM.

The modification of indoor PM2.5 exposure to chronic obstructive pulmonary disease in Chinese elderly people: A meet-in-metabolite analysis.

BACKGROUND: Exposure to airborne fine particulate matter (PM2.5) has been associated with a variety of adverse health outcomes including chronic obstructive pulmonary disease (COPD). However, the linkages between PM2.5 exposure, PM2.5-related biomarkers, COPD-related biomarkers and COPD remain poorly elucidated. OBJECTIVES: To investigate the linkages between PM2.5 exposure and COPD outcome by using the meet-in-middle strategy based on urinary metabolic biomarkers. METHODS: A cross-sectional study was designed to illustrate the mentioned quadripartite linkages. Indoor PM2.5 and its element components were assessed in 41 Chinese elderly participants including COPD patients and their healthy spouses. Metabolic biomarkers involved in PM2.5 exposure and COPD were identified by using urinary metabolomics. The associations between PM2.5- and COPD-related biomarkers were investigated by statistics and metabolic pathway analysis. RESULTS: Seven metabolites were screened and identified with significant correlations to PM2.5 exposure, which were majorly involved in purine and amino acid metabolism as well as glycolysis. Ten COPD-related metabolic biomarkers were identified, which suggested that amino acid metabolism, lipid and fatty acid metabolism, and glucose metabolism were disturbed in the patients. Also, PM2.5 and its many elemental components were significantly associated with COPD-related biomarkers. We observed that the two kinds of biomarkers (PM2.5- and COPD-related) integrated in a locally connected network and the alterations of these metabolic biomarkers can biologically link PM2.5 exposure to COPD outcome. CONCLUSIONS: Our study indicated the modification of PM2.5 to COPD via both modes of action of lowering participants' antioxidation capacity and decreasing their lung energy generation; this information would be valuable for the prevention strategy of COPD.

Short-term effects of particulate matter in metro cabin on heart rate variability in young healthy adults: Impacts of particle size and source.

BACKGROUND: Metro system has become popular in urban areas. However, short-term effects of size-fractionated particulate matter (PM) on cardiac autonomic function in metro system remain unexplored. OBJECTIVES: To explore the contribution of ambient PM to in-cabin PM and investigate the short-term effects of exposure to size-fractionated PM and black carbon (BC) in metro system on cardiac autonomic function in young healthy adults. METHODS: Thirty nine young healthy adults were asked to travel in metro system during 9:00-13:00 on a weekends between March and May 2017. We performed continuous ambulatory electrocardiogram monitoring for each of them, and measured real-time size-fractionated PM, BC, nitrogen dioxide, nitric oxide, carbon dioxide, ozone, noise, temperature and relative humidity in metro cabin. We also collected the data of ambient PM2.5 (aerodynamic diameter < 2.5 µm) concentrations in Beijing. Linear regression model was used to estimate the infiltration factor of ambient PM2.5 to assess the relationship between metro cabin PM and ambient PM. Mixed-effects model was used to estimate the associations between changes in HRV parameters and PM0.5 (aerodynamic diameter < 0.5 µm), PM0.5-2.5 (aerodynamic diameter between 0.5 µm and 2.5 µm), PM2.5-10 (aerodynamic diameter between 2.5 µm and 10 µm), and BC, respectively. RESULTS: We found that size-fractionated PM in metro systems were significantly associated with HRV parameters. Per IQR (interquartile range) increase in PM0.5 (1.6*107/m3) in 1-h moving average concentration was associated with a 13.96% (95% CI: - 18.99%, - 8.61%) decrease in SDNN (standard deviation of normal-to-normal intervals). Similar inverse associations were found between size-fractionated PM exposure and LF (low frequency power), HF (high frequency power), respectively, and smaller particles had greater effects on HRV parameters at shorter lag time. Sex of participants modified the adverse associations between size-fractionated PM and HRV. An IQR of 1-h PM0.5 increasing was associated with a decrease of 6.05% (95% CI: - 22.87%, - 14.44%) in males and a 34.87% (95% CI: - 49.59%, - 15.85%) in females in LF (P for interaction = 0.026). The infiltration factor of ambient PM2.5 was 0.39 (95% CI: 0.33, 0.45). It is estimated that PM2.5 originated from ambient air may account for 20.2% of the PM measured in metro cabin. Per IQR increase in BC (5.5 µg/m3) in 5-min, 1-h, and 2-h moving averages, a primary tracer for ambient PM from combustion source, was associated with decreases of 0.84% (95% CI: - 1.20%, - 0.47%), 2.22% (95% CI: - 3.20%, - 1.22%), and 4.44% (95% CI: - 6.28%, - 2.56%) in SDNN, respectively. CONCLUSIONS: Short-term exposure to PM may disturb metro commuter's cardiac autonomic function, and the potential effects depend on the size of PM and the sex of commuters. Ambient PM from combustion source may have adverse effects on the cardiac autonomic function of passengers in cabin.